IFTA Journal 2021 Edition

A Professional Journal Published by The International Federation of Technical Analysts

21
Inside This Issue
4

Number-Based Sentiment Indicators

12 The Noise Trend
27 Determination of Time Target Zones for Price Targets of Classic Price Patterns
38 Emerging Currencies as Equity Earthquake Indicator
55 Coefficient Moving Average“Weak leadershipcan wreck the soundest
strategy; forcefulexecution of even apoor plan can often
bring victory.”—Sun Tzu

ISSN 2409-0271

IFTA JOURNALEDITORIAL
Rolf Wetzer, Ph.D., CFTe, MFTA
Editor and Chair of the Editorial Committee
Rolf.Wetzer@ifta.org
Regina Meani, CFTe
regina.meani@gmail.com
Aurélia Gerber, MBA, CFA, MFTA
aurelia.gerber@ifta.org

Send your queries about advertisinginformation and rates to admin@ifta.org

2021 EDITION

Letter From the Editor
By Dr. Rolf Wetzer, CFTe, MFTA ......................................................................................................................3
MFTA Papers
Number-Based Sentiment Indicators
By Przemysław Smoliński, MFTA...................................................................................................................4
The Noise Trend
By Mohamed M. Khedr, CFTe, MFTA........................................................................................................... 12
Determination of Time Target Zones for Price Targets of Classic Price Patterns
By Momen Atef El Shayal, CFTe, MFTA.......................................................................................................27
Emerging Currencies as Equity Earthquake Indicator
By Ron Albert Marcelino Acoba, CFTe, CMT, MFTA...................................................................................38
Coefficient Moving Average
By Mohamed Fawzy ElSayed Ali AbdAlla, CETA, CFTe, MFTA...................................................................55
Articles
The Ripples Effect: A Clearer View for Market Action and Price Patterns
By Mohamed Ashraf Mahfouz, CETA, CFTe, MFTA ....................................................................................63
Forecasting Major World Indices on Ichimoku
By Yukitoshi Higashino, MFTA ....................................................................................................................76
NAAIM Papers
Stock Trends and Trend-Based Trading Strategies—Backed by Large-Scale Back-Testing
Implemented by Automated Software System
By Kevin Luo.................................................................................................................................................. 84
Simple and Effective Market Timing With Tactical Asset Allocation
By Lewis A. Glenn, Ph.D............................................................................................................................... 90

Cover Photograph from iStock images

Book Review
How the Average Investor Can Use Technical Analysis for Stock Profits:
An In-Depth Work on Stock Market Technical Analysis, Mob Psychology,
and Fundamentals by James Dines
Reviewed by Regina Meani, CFTe.................................................................................................................97
Author Profiles................................................................................................................................................. 98
IFTA Staff......................................................................................................................................................... 100
IFTA Board of Directors................................................................................................................................ 100

IFTA Journal is published yearly by The International Federation of Technical Analysts, 1300 Piccard Dr., Suite LL 14 Rockville, MD 20850
USA. © 2020 The International Federation of Technical Analysts. All rights reserved. No part of this publication may be reproduced ortransmitted in any form or by any means, electronic or mechanical, including photocopying for public or private use, or by any information
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IFTA.ORG PAGE 1

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IFTA JOURNAL

2021 EDITION

Letter From the Editor
By Dr. Rolf Wetzer, CFTe, MFTA

Dear IFTA Colleagues and Friends:
I hope that you and your family and friends came through the year healthy and safe. We
are living through strange times. Something unprecedented keeps us from doing what weare used to. We shelter ourselves by staying at home, wearing masks, and restricting ourmeetings with others. The same applies to IFTA. For the first time in our history, there will be
no in-person conference this year. We canceled Philadelphia, we will stay at home, and we will
not meet as usual. Also, for the first time, there will be an online conference.
But, despite the pandemic, there is still an IFTA Journal. Not business as usual, butnevertheless as normal as possible. IFTA is international and so is this Journal. We havegathered articles and papers from colleagues around the world.
In this issue, you will find five papers from our MFTA program. Starting with the first idea until the final gradingof the paper, our colleagues usually spend almost a year achieving their MFTA designation.
We also have two papers that NAAIM has kindly made available to us. NAAIM also had to cancel its annual
meeting, and hence, the winner of the Wagner Award will not be announced until later this year. We therefore are
publishing papers from previous years. Furthermore, we are more than happy about the longlasting cooperation with NAAIM. I want to thank NAAIM for its support and especially Susan
Truesdale for her kind cooperation.
We also have two articles from colleagues in Egypt and Japan. The authors were speakers at ourlast conference in Cairo. Since their presentations raised much interest, they both agreed to write
an article about their respective topics to make them available for a broader audience.
Last, but not least, the Journal traditionally closes with a book review from our Australiancolleague, Regina Meani. She is something like the faithful soul of the Journal. Always there, always
willing to help and to support. Thank you for that.
Finally, I need to thank Aurélia Gerber for her help and especially Linda Bernetich. Without Linda
and her team, the Journal simply wouldn’t exist. Thanks for your patience and organizational skills.
I hope that you enjoy the Journal.

…the greatest
resources for the
Journal are ourcolleagues from allover the world.

Best regards,
Dr. Rolf Wetzer, CFTe, MFTA

IFTA.ORG PAGE 3

IFTA JOURNAL

2021 EDITION

Przemysław Smoliński, MFTA

Number-Based Sentiment Indicators

Stanislawa Herbsta 2a/17
Warsaw, 02-784
PolandBy Przemysław Smoliński, MFTA

+48 696 433 713
przemyslaw.smolinski2@pkobp.pl

Abstract

probability of a market bottom or peak. While a gradually increasing number of rallying or

Depending on the stock exchange, various types of data are
available to determine commitment of traders that can be used
to help forecast future market behaviour. Such data may bereleased directly by the exchange itself or be obtained throughvarious kinds of surveys of trader opinions and sentiment.
While the former type of data is not always available, giventhat such data is not published by all exchanges, the latter isfrequently burdened with different types of errors that arise
from conducting the survey on a limited group of traders,the periodic nature of the surveys, etc. Help in solving theabove problem came, rather unexpectedly, from emotionlessalgorithms. An investment portfolio based on them turned out
to be a good indicator of market sentiment.
A detailed analysis of the above issue proved its solid
theoretical foundation based on the theory of cycles and thefractal nature of the market as well as capital flows in thestock market and in the economy as a whole. More importantly,
however, it also has practical investment applications. This
thesis presents the theoretical foundations of the describedissue and examples of its practical application.

Acknowledgements

Based on the above observation, commitment of traders
indicators, named Synthetic Sentiment Indicators, were
contributes to its continuation, a decisive prevalence of either market side serves as a reliable
developed. Recent years have shown that they accurately
prognosis of a market extreme emerging and a reversal of the current long-term trend.
determine the probability of occurrences of market peaks andbottoms.
The high
effectivenesspresented
canBasedon the above
observation,commitmentof
ofthe
traders
indicators, indicators
named Synthetic
be explained
fromdeveloped.
a fundamental
perspective—basedon theSentiment
Indicators, were
Recent years
have shown that they accurately
flowstheofprobability
capital in
the economy
andpeaks
in theitself,determineof occurrencesof market
andcapitalbottoms.market
The high effectivenessfromindicators
the perspectiveof technicalanalysisby using cyclesof theandpresented
can be explained
from a fundamental
perspective—basedon thea fractal
marketandstructure.
flowsandof capitalin the economy
in the capital market itself, and from the perspective of
declining stocks in the portfolio serves as a confirmation of the trend’s strength andtechnical analysis by using cycles and a fractal market structure.

Technical Grounds
The majority of main market peaks and bottoms can be
explained
a simultaneous
superpositionof extremes
fromThe
majority ofby
main
market peaks and bottoms
can be explainedby a simultaneous
severalofdifferent
marketaddition,
if weconsiderthe thesuperposition
extremes fromseveralcycles.
differentIn
marketcycles. In
addition,
if we consider
fractalnatureof themarket,the endof thetimeframes
structuresinfractalnature ofthe market,
where
the end ofwhere
the structuresin various
constitutes
variousoftimeframes
constitutes
the beginningof a subsequent
the beginninga subsequent structure,market extremes
can be forecasted
on the basis of thestructure,
be forecasted
on the basis
current
cycle phasesmarket
in variousextremes
timeframes.can
The simultaneous
superpositionof their peaks andof thecurrent
cycle phases
in various
timeframes. The
bottoms
resultsin the occurrenceof the mainmarket extremes.
simultaneous superposition of their peaks and bottoms results
Figure1. Interactions
betweenin theoccurrenceof thecycles
main market extremes.
Technical Grounds

Figure 1. Interactions between cycles
Cycle long-term

I would like to start by thanking Paweł Małmyga forexchanging many interesting investment ideas with me and forthe countless joint strategy backtests conducted on their basis. I
would also like to thank Emil Łobodziński for numerous hours ofinspiring conversations about the capital market, and my yearlong employer PKO BP Securities for providing me with working
conditions that allow me to combine my work with my passion
for technical analysis.

Introduction

Cycle mid-term

Cycle short-term

Trend reversals and corrections

Every morning, the author of this thesis prepares analgorithmic investment portfolio covering 140 of the biggest
companies listed on the Warsaw Stock Exchange. After yearsof monitoring signals and the general number of short and longpositions in the portfolio, a pattern has started to emerge thatproves helpful in determining market sentiment. As it turned
out, a decisive prevalence in the number of equities on one sideof the market far increases the probability of a market bottom
or peak. While a gradually increasing number of rallying or
declining stocks in the portfolio serves as a confirmation of thetrend’s strength and contributes to its continuation, a decisive
prevalence of either market side serves as a reliable prognosis ofa market extreme emerging and a reversal of the current longterm trend.

PAGE 4 IFTA.ORG

A superposition of short- andmid-term cycles suggests the
beginning and end of corrections

A superposition of short- andmid-term cycles suggests the
beginning and end of corrections

Cycle Identification
In classical technical analysis, one must first identify thedominant cycles, determine their period, and find the mostrecent highs and lows to predict respective market peaks andbottoms. While it may theoretically seem uncomplicated, cycle
identification tends to be problematic in practice. To achieve thisgoal, one can use visual methods based on manually measuring

IFTA JOURNAL

the distance between visible market extremes or, alternatively,
use more advanced methods, such as the Fourier analysis
developed by French mathematic John Baptiste Joseph Fourier,the Phasing presented by J.M. Hurst, or the Maximum Entropy
Spectral Analysis (MESA) described by John Ehlers.
Figure 2. Traditional forecasting method using cycles
Forecast

Forecast

While it is relatively easy to spot recurring structures on thecharts during a long-time horizon, shortening the timeframe
usually results in increased chaos and decreased regularity
in the occurrence of market extremes. An average 54-year
Kondratieff Wave, 18-year Kuznets, and 9-year Juglar cycles or
the Presidential Cycle on the U.S. market can serve as a casein point here. Thanks to robust macroeconomic foundations,these cycles are relatively easy to identify, and their impact on
stock prices is clearly distinguishable. However, as the cycle
period is shortened, predicting market peaks and bottoms
frequently becomes more difficult, and the results are prone toerrors. Not only do the cycles frequently change their period andamplitude but they also tend to disappear and re-emerge aftersome time. Their practical application is further complicatedby the superposition of several cycles of different periods andamplitudes.
Figure 3. Probability of occurrences of peaks andbottoms based on cycles superposition

In light of the above, we should consider applying a methodof predicting cycle-based market extremes that is slightly
different from the traditional method based on measuring
the cycle period from its latest bottom or peak. If we make anassumption regarding the fractal nature of the market and theresulting occurrence of the main market extremes at the pointsof simultaneous superposition of the end of structures in alltime horizons (a short, medium, and long investment horizon
can be assumed for simplicity), we can simply restrict ourselvesto determining the current position of the price in respective
cycles of a specific period. As a result, instead of seeking specificcycle periods and measuring their distance from the last market
extremes, we simply assume their existence and determine
whether the price is currently in the growth or the decline
phase. Once the phases of the studied cycles coincide, theprobability of a market extreme emerging increases.
As it turns out, the approach described above works even2021 EDITION

better with respect to broad market indicators that aggregate
the current cycle position for all index constituents than withregard to individual equities. For an individual stock, theconvergence of cycle phases, i.e., trends with the same direction
in various timeframes, may suggest a growing or declining
interest in the given equities on the part of consecutive groupsof traders and translate into a continuation of the current
trend in the following sessions. Meanwhile, if a great majorityof equities consistently remains in a growth or a decline cycle
phase across all timeframes, the market can be consideredwidely overbought or oversold, respectively, which considerablyincreases the probability of a market peak or bottom emerging.
As a result, one might say that, instead of forecasting apriori a market extreme, in the future, it is better to focus
on determining the probability of its occurrence now. The
above approach can help eliminate the need for a far-fromperfect manual forecasting of peaks and bottoms or for theapplication of advanced mathematical methods which often do
not seem fully adapted to the capital market and its constantly
changing nature.
Macroeconomic Grounds
Although macroeconomics is not the focus of this thesis,
every good theory should have the most extensive theoreticalfoundation possible. As a result, apart from the technicalapproach to the presented theory, we should also analyse its
foundations based in the flows of capital in the economy and thecapital market.
A bull market, especially supported by the good conditionof the economy as a whole, means a gradual capital circulation
between equities. The capital steadily flows from the rallying,overvalued stocks to less expensive ones (e.g., according to peer
valuation ratios) whose prices have so far been declining or
moving horizontally. The higher the number of stocks following
a strong uptrend, the fewer stocks that remain attractive interms of their price, which means that, following the profittaking from overvalued equities, the traders cannot reinvest
their capital in less expensive stocks. As a result, at some point,
traders begin to look for alternative means of capital allocation
which, combined with changing prospects of GDP growth or
the monetary policy, results in shifting the capital from theequity market toward markets that are more attractive at thattime, be it commodities, money, or debt instruments. Combinedwith the change in the economic cycle, a strong capital outflow
contributes to a reversal to downtrend on the stock market.
Declining prices of equities result in a gradual increase in their
attractiveness, which in time causes another inflow of capitalfrom other areas of the capital market.
The above explanation has an additional advantage overthe purely technical approach, because it also clarifies why asimultaneous superposition of cycles of the same phase and indifferent timeframes works better for forecasting extremes inthe broad market, using all the equities comprised by the given
index, than for individual equities.

IFTA.ORG PAGE 5

IFTA JOURNAL

2021 EDITION

Figure 4. Money flow between sectors of the economy

analysis tools for trend determination. First, trend direction
must be established for every equity separately in the short,medium, and long term. Next, the results should be summed
up for the whole market or respective indices. This can beachieved through the application of many different technicalanalysis tools, such as the simplest moving average. In thisthesis, a Trailing Stop was used, which is distant from thestock price by a fixed ATR value that increases the more theindicator is expected to serve the purpose of determining thelonger cycle phase.
Figure 5. Cycle identification
based on price
Cycle identification based on priceNasdaq, Trailing Stop Short-term, Trailing Stop Mid-term, Trailing Stop Long-term

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Short-term Trailing
Short-term
Trailing Stop
Stopidentifies
identifies short-term cycles
short-term
cycles

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Mid-term Trailing Stop
Stop identifies
identifies
mid-term cycles

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mid-term cycles

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Long-term Trailing
Stop
Long-term
Trailing
identifies long-term cycles
Stopidentifies
long-term cycles

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5
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Figure 5 Cycle identification based on price

Given that it is possible to use classical technical analysis
Given that ittois possible
to use classicalanalysisindicators to determine
indicators
determine
cyclestechnicalinsteadof forecastingpeakscycles
andbottomsmanually,wholemanually,
process
automatedwithwith
instead of forecastingpeaks theand bottoms
thecanwholebeprocess
can be automatedrelativeease for a large number of equities listed on the stock
relative ease for a large number of equities listed on the stock exchange or included in a
exchange or included in a specific index.
specific index.

Basic
Interpretation
Basic Interpretation
After aggregating the results for all equities included in the
After aggregating
the results
for all equities
included in theindices, weanalysedindices,
we arriveat a summary
ofanalysed
the market
inarrivethe at aform
ofofindicators
illustrating
theillustrating
currentthecycle
in various
summary
the market in theform of indicators
currentphase
cycle phasein various
timeframes
that
can
be
interpreted
as
a
joint
marketsentiment
timeframes that can be interpreted as a joint market sentiment of short-, mid- and long-termof short-, mid-, and long-term traders. For most equities, thetraders. For most equities, the prevalence of the growth or decline phase of the analysed cycle
prevalenceof the growth or decline phase of the analysedindicatesindicatesthe positive or
negative
attitudes
the traders towards
the market.
cycle
the
positive
orofnegative
attitudesof the traders
toward the market.

8

PAGE 6 IFTA.ORG

5350

Mid-term Trailing

Materials and Methods
Identifying Market Sentiment
The abovementioned regularity can be used for creating
Synthetic Sentiment Indicators to determine the scale oftraders’ commitment and, at the same time, general market
sentiment that prove helpful in establishing the strengthof the current trend and the degree to which the market isoverbought or oversold, which is useful in forecasting themain turning points.
Given that neither the precise period of the cycles nor theexact dates of peaks and bottoms are needed, and instead
are replaced with determining the current cycle phase in the
adopted investment horizons, the whole process can be largely
automated based on properly prepared indicators applied tothe prices of all equities from the given index or market. Their
combination into an aggregate indicator determines the generalmarket sentiment.
Assuming that the cycle amplitude is largely proportional
to its period, we can stipulate that the respective cycles are
represented in charts by trends in various timeframes. As aresult, instead of determining the phase of the cycle basedon the latest bottoms or peaks, we can use classical technical

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IFTA JOURNAL

2021 EDITION

Figure 6. Various stages of market behaviour
Various stages of market behaviour

DAX

Overbought

Balanced Market

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Oversold100

Short-term Sentiment_DAX Sentiment Short-term

50
0
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_DAX Sentiment Mid-term
Mid-term
Sentiment

50
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_DAX Sentiment Long-termLong-term
Sentiment

50
0
-50

28

4
11
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18

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2018

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12

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3
April

9

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Figure 6 Various stages of market behaviour

A gradual increase in the number of equities in an uptrend or a downtrend implies a strong
IFTA.ORG PAGE 7

IFTA JOURNAL

2021 EDITION

A gradual increase in the number of equities in an uptrend
or a downtrend implies a strong and healthy trend that should
continue until a critical value is reached. Above it, a decisive
majority of equities will be at the same cycle phase in all
the timeframes, which means that the whole market will be
extremely overbought or oversold. If the market is overbought,
the traders will not be able to reinvest their profits in cheaper
equities which will result in an outflow of capital in favour ofother, more attractive instruments, while stock indices willreverse to a downtrend or at least start a strong correction. On
the other hand, if the market is extremely oversold, the majorityof equities will be sufficiently attractive in terms of price toinduce the traders to turn their back on other segments of thecapital market and the indices will begin long-term uptrends.
The above description of the sentiment indicators is only
basic. The indicators provide far greater analytic possibilities
that make it possible to determine the current trend and signala trend weakening, an approaching correction or a long-term
trend reversal.

Structure

Synthetic Sentiment Indicators are based on four
fundamental source indicators from which aggregate result
indicators emerge:• Short-term Cycle: determines the phase of the short-term
cycle.
• Mid-term Cycle: determines the phase of the mid-term cycle.
• Long-term Cycle: determines the phase of the long-term
cycle.
• Combined Cycles: determines the degree to which cycle
phases coincide in the short, mid, and long horizons.
Each of the above indicators should be applied to every
individual equity included in the index, after which the results
should be summed up, giving rise to the actual Synthetic
Sentiment Indicators:• Short-term Sentiment: estimates the commitment degree ofshort-term traders.
• Mid-term Sentiment: estimates the commitment degree of
mid-term traders.
• Long-term Sentiment: estimates the commitment degree of
long-term traders.
• Long vs. Short Sentiment: estimates the degree ofcommitment consistency for all analysed trader groups on
the long or the short side of the market.
The formulas for the above indicators can be found in the
Appendix at the end of this thesis.
As stipulated above, other classical technical analysis tools
can be used for constructing the aforementioned indicators,
such as moving averages or oscillators. This thesis, however,uses the Trailing Stop, which determines the current trend ina specified investment horizon in a simple and transparent
manner. As a result, it can easily be used for a large number of
equities, while the aggregate results clearly depict the generalmarket sentiment.

PAGE 8 IFTA.ORG

Backtests
To determine the validity of the described theory, Synthetic
Sentiment Indicators were tested on several markets from2017–2019. The test results confirmed the effectiveness of thedescribed method for DAX, STOXX 600, ASX 200, S&P 500, andNasdaq Composite indices; it is likely that the positive effects of
its application would also apply with a high degree of probability
in the case of other stock indices.

Results

The presented indicators can be applied both for the purposeof discretionary trading, in the scope of classical technicalanalysis, and in a systemic approach as one of many components
of more complex investment strategies. In either case, thebasic application of the indicators focuses on determining thecurrent dominant market trend and its further potential anddetermining the points in time where the probability of thetrend reversal is high.
Interpreting the Synthetic Sentiment Indicators involves
aggregating the results of the three separate indicators
determining the sentiment of short-, mid-, and long-term
traders or aggregating the Long vs. Short Sentiment Indicator,
which establishes the joint commitment of all trader groups inall investment horizons. In the former case, the basic signal ofa trend reversal involves all three indicators being overboughtor oversold at the same time. In the latter, a mutual position ofboth lines of the Long vs. Short Sentiment Indicator serves asa signal of trend continuation. Taking this into consideration,
the conducted tests were divided into mean reversion and trend
following. An additional third test was also performed, taking
both categories into consideration.
The results of the abovementioned tests follow.
Mean Reversion
In the case of the mean reversion test, the signal reversing thecurrent uptrend appears when all three indicators illustrating
the sentiment of short-, mid-, and long-term traders are
overbought (above 50 points) at the same time. The situationis reversed in the case of a sell signal that appears when all ofthese indicators are simultaneously oversold (below 50 points).
In both cases, the position is closed automatically 10 sessions
from the signal’s appearance, which corresponds on average to aperiod of two weeks.
Table 1. Mean reversion signals
Index

TradesPercent
Profitable

Avg. Profit /Avg. Loss
ProfitASX 200

7

86%

2.27

12.36%

DAX

12

67%

1.76

14.78%

NASDAQ

6

50%

2.97

8.98%

S&P 500

7

71%

3.43

13.63%STOXX 600

5

80%

1.91

10.94%

Percent
Profitable
86%
67%
50%
71%
80%

Trades
7
12
6
7
5

Index
ASX 200
DAX
NASDAQ
S&P 500STOXX 600

Avg. Profit /Avg. Loss
2.27
1.76
2.97
3.43
1.91

Profit12.36%
14.78%
8.98%
13.63%
10.94%IFTA JOURNAL

Mean reversion signals on DAX
Figure 7. Mean reversion
signals on DAX
DAX

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Y

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_DAX Sentiment Mid-term

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100

_DAX Sentiment Long-term

50
02021 EDITION

following test when either line of the Long vs. Short Sentiment
Indicator enters the overbought range (above 50 points). The
3.3 TREND FOLLOWING WITH PROFIT-TAKING
position is renewed after the indicator declines beneath theoverbought
range, provided that the original conditions for
The last test conducted combines Trend Following signals with Mean Reversion by adding
opening the position are still met.
profit-taking signals to the Trend Following test when either line of the Long vs Short

Table
Trend-following
withprofit-taking
Sentiment3.
Indicator
enters the overboughtsignals
range (above
50 points).
The position is renewed
after the indicator declines beneath the Percent
overbought range, provided
that theAvg. Profit/ originalIndex for opening
Tradesconditions
the position are Profitablestill met.

Avg. Loss

ProfitASX 200

1.00

10.38%

1.54

33.10%

40

55%

Table
profit-taking
DAX 3 Trend following
32 signals with53%
Percent
Profitable
55%
53%
54%
52%
50%

Trades
40
32
41
23
34

Index
ASX 200
DAX
NASDAQ
S&P 500STOXX 600

NASDAQ41

S&P 500

23

STOXX 600

34

Avg. Profit /Avg. Loss
1.00
1.54
1.27
1.51
1.26

Profit10.38%
33.10%
30.65%
27.73%
8.54%

54%

1.27

30.65%

52%

1.51

27.73%

50%

1.26

8.54%

-50

Dec

2017

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2018

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2019

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2020

Figure 9. Trend-following signals with profit-taking on
S&P 500 Trend following signals with profit-taking on S&P 500

Figure 7 Mean Reversion Signals on DAX

Trend Following
13
In the following test, trend-following
signals are the resultof the Long vs. Short Sentiment Indicator that illustrates theaggregate commitment of all trader groups in all investment
horizons. A rally of the long line in excess of the short line
3.2
TREND FOLLOWING
generates
a buy signal and vice versa. In order to filter thesignals in consolidations, the signals do not appear immediately
In the following test, Trend Following signals are the result of the Long vs Shortwhen the lines cross but, instead, when the difference between
Sentiment Indicator that illustrates the aggregate commitment of all trader groups in allthem is at least five points. The position is closed immediately
investment
horizons.
rally of the Long line in excess of the Short line generates a buy signal
after bothlinesA cross.

3300

SP500

3250

Y

3150
3100
3050

Y

Y
O
P
Y
O
Y
O
Y P

P
Y

Y
Y

P

O

P

PO

Y

2950
2900

Y

O

2850
2800
2750

P

Y

Y

3000

O

Y

Y
O

YP

P

P
Y

Y

2700

Y
O

Y

2650
2600

Y

2550

P

2500

Y
P O
Y

2450
2400
2350
2300
2250

O

2200
_SP500 Sentiment Long vs Short70

and vice versa. In order to filter the signals in consolidations, the signals do not appear

65
60

Table 2. Trend-following signals

55
50
45
40

immediately when the lines cross but, instead, when the difference between them is at least

35
30
25

Percent
Avg. Profit /five points. The position is closed immediately after both lines cross.
Index
TradesProfitable Avg. Loss
ProfitASX 200
17 signals
Table
2 Trend following
DAX

19TradesIndex
ASX 200
DAX
NASDAQ
S&P 500STOXX 600

NASDAQ

20

S&P 500

16

STOXX 600

17

29%

17
19
20
16
17

20
15
10
5
0
-5
Dec

1.82

-5.38%

47%

1.97
Profit20.46%

40%

2.26

21.42%

50%

1.61

16.34%

41%-5.38%
20.46%
21.42%
16.34%
6.33%

1.89

6.33%

Percent
Profitable
29%
47%
40%
50%
41%

Avg. Profit /Avg. Loss
1.82
1.97
2.26
1.61
1.89

Nasdaq

Y

Y
P
P

Y
P

Y
Y
P
O

Y
O

Y
O

P

O

Y
P O
Y

Y

O

Y

Y

9000
8900
8800
8700
8600
8500
8400
8300
8200
8100
8000
7900
7800
7700
7600
7500
7400
7300
7200
7100
7000
6900
6800

Y

Y

2017

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2018

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2019

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2020

Figure 9 Trend following signals with profit-taking on S&P 500

Figure 8. Trend-following
signals
oncomposite
Nasdaq composite
Trend following signals
on Nasdaq

P

6700

O

6600

Final Remarks on the Backtests
The results presented above illustrate the effects of a basic
application of the indicators to several main stock indices.
15
It should be noted that the indicators’
parameters were not
optimised with respect to generating the highest profit possible;
instead, appropriate overbought and oversold levels wereselected visually so that they better reflect the nature of thegiven market. It should also be borne in mind that the results ofthe tests do not illustrate any tradeable investment strategies
but only the raw verifiability of the indicators themselves, whichshould only serve as a basis for further and more advanced
works on the specific methods of their application.

6500

Y

6400
6300
6200
6100
6000

PY
Y
OPO
Y

5900

O

5800

Y

5700

Discussion

5600

Y

5500
5400
5300
5200
5100
5000
4900

O

4800
4700
_Nasdaq Sentiment Long vs Short

Dec

2017

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2018

Feb

Mar

Apr

May

Jun

Jul

Aug

80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
-5
Sep

Oct

3200

Nov

Dec

2019

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2020

Figure 8 Trend following signals on Nasdaq composite

Trend Following With Profit-Taking
The last test conducted combinestrend-following signals with14
mean reversion by adding profit-taking signals to the trend-

Results Commentary
The test results presented above explicitly show the higheffectiveness of the Synthetic Sentiment Indicators. A positive
rate of return was achieved both in the case of mean reversion
signals, based on the simultaneously overbought or oversold
indicators in the short, medium, and long investment horizon,
and the trend-following signals that are based on the Long vs.
Short Sentiment Indicator, which determines the consistencyof the phases of all the cycles at the same time. In regard to themean reversion signals, a positive rate of return was generated
IFTA.ORG PAGE 9

Indicator to create an oscillator that works better in the consolidation period while still

2021 EDITIONIFTA JOURNAL

highlighting the points of the market being strongly overbought or oversold.
Long-Short
Difference oscillator
on DAX on DAX
Figure 11. Long-Short
Difference
oscillator

with respect to each of the tested indices, and the total profit
amounted to 60.69%. The test of the trend-following signals
returned slightly worse results; ASX 200 was the only index
that generated a small loss, and the total profit for all the tested
markets amounted to 59.17%.
As it turns out, a combination of both signal types generated
the best results by far. Not only was a profit rather than aloss observed in the case of ASX 200, but practically all theremaining indices generated a far better result than in either
test individually, achieving a total rate of return of 110.40%.

DAX
13500

13000

12500

12000

11500

11000

10500

10000

9500

_DAX Sentiment Difference
150
100
50
0
-50
-100
-150
_DAX Sentiment Long vs Short

Discretionary Trading With Synthetic Sentiment
Indicators
Apart from using clear and unequivocal investment signals
generated by the lines of respective indicators crossing or
the indicators entering overbought or oversold ranges, theSynthetic Sentiment Indicators can also be applied to a more
subjective market assessment that utilizes less obvious nuancesin the conduct of the indices. The mutual relations between
respective indicators or their relative behaviour versus theexchange index can be used for this purpose.

y

Jul

Aug

Sep

Oct

Nov

Dec

2017 Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec 2018

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec 2019 Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec 2020

Figure 11 Long-Short Difference oscillator on DAX

Comparison With Other Broad Market Indicators
Finally, a comparison of the Synthetic Sentiment Indicatorswith other broad market indicators is recommended. The mostpopular among them are the Advance/Decline Line and the52 Week High/Low. In a great majority of cases, the Synthetic
Sentiment Indicators are decisively better with respect todisclosing a strongly overbought and oversold market andcreating divergences as well as generating trend reversal andcontinuation signals more accurately and quickly than classic
broad market indicators that are commonly used.

Divergences

Divergences
The most straightforward example of using the discussed indicators are divergences. They
The most straightforward example of using the discussed
warn in advance of an increased probability of a correction start or a reversal of a long-term
indicators are divergences. They warn in advance of an increased
trend.
probabilityof a correction start or a reversal of a long-term trend.

19

Figure 12. Synthetic Sentiment Indicators vs. A/D Lineand 52 Week High/Low on S&P 500

Figure 10. Divergenceson ASXon200
Divergences
ASX 200
ASX200

Jun

70
65
60
55
50
45
40
35
30
25
20
15
10
5
0

Advance/Decline Line

6400

30000

6350
6300

25000

52 Week High/Low Index

6250
6200

100

6150
6100

50

6050

52 Week High/Low

6000

0
200

5950

150

5900

100

5850

50

5800

0

5750

3050
3000
2950
2900
2850
2800
2750
2700

5700
5650
5600
5550

2650

5500

2600
2550

5450

2500

5400

2450
2400

100

_ASX200 Sentiment Cycle Short-term

2350

Short‐term Sentiment

50

50

0

0

-50

-50

Mid‐term Sentiment

-100

-100

_ASX200 Sentiment Cycle Mid-term

50
50

0

0

-50

Long‐term Sentiment

50

-50

0
_ASX200 Sentiment Cycle Long-term

-50
50

-100

Long vs Short Sentiment

60
50
40
30
20
10
0

0

-50
September

OctoberNovember

December

2018

February

March

April

May

June

July

AugustSeptember

OctoberNovember

December

2019

February

March

3

20

27

4
10 17
September24

1
8
October

15

22

29

5
12
November

19 26

3
10 17
December

24 31 7
2019

14

22 28

4
11
February

19 25

4
11
March

18

25

1
8
April

15 22

29 6
May

13

20

28 3
10
June

17

24

1
8
July

15

22

29

5
12
August1926

3 9
Septembe

Figure 10 Divergences on ASX 200

Long-Short Difference Oscillator
The discussed indicators are not the ultimate form ofpresenting the collected data. On the contrary, both theirpresentation and interpretation merits ongoing experiments.
One possibility involves using the difference between the twolines of the Long vs. Short Sentiment Indicator to create an
oscillator that works better in the consolidation period whilestill highlighting the points of the market being strongly
overbought or oversold.

18

PAGE 10 IFTA.ORG

Conclusion

Although, as indicated in the introduction to this thesis,
the Synthetic Sentiment Indicators were developed rather byaccident than as a result of purposeful studies, the tests have
proved that their usefulness in determining market sentimentand forecasting (or rather estimating) the probability ofmarket extremes emerging is very high. It also may be that theadvantage of the described method involves the fact that it is
the result of observing a specific market phenomenon insteadof an attempt to adapt a theory to historical data. Nevertheless,irrespective of the foundations of the described indicators, they
constitute a new and valuable tool in the technical analysis
arsenal that permits a better determination of the current
market situation than the broad market indicators deployed todate. They also help estimate the17market sentiment in the case

IFTA JOURNAL

of stock exchanges that do not release data on the commitment
levels of respective traders’ groups, thus constituting analternative to traders’ opinion surveys. While this thesis only
presents the results of applying the indicators to several
selected indices, the data are promising enough, and the theory
backing them is consistent enough, to support their applicationin other markets that are not covered by this study.

References
Ehlers, John F. MESA and Trading Market Cycles: Forecasting and Trading
Strategies from the Creator of MESA. Wiley, 2002.
Hurst, J.M. The Profit Magic of Stock Transaction Timing. Prentice-Hall,
1970.

APPENDIX
Indicator Codes for Metastock
Trailing Stop Short-term/Mid-term/Long-term
indicators formula:
Length:= 1.5; { Mid-term: 3.0 / Long-term: 4.5 }Volatility:= Length*ATR(5);StdevOfVolatility:= 3*(1/Sqrt(Length))*Stdev(Volatility,30);
EmaM:= (H+L+C)/3;
EmaTR:= Volatility + StdevOfVolatility;
Stop:= If( PREV < C,
If(( EmaM - EmaTR ) >= PREV,( EmaM - EmaTR ), PREV),( EmaM - EmaTR ));StopLong:= Stop;
Stop:= If( PREV > C,
If(( EmaM + EmaTR ) <= PREV,( EmaM + EmaTR ), PREV),( EmaM + EmaTR ));StopShort:= Stop;
If(BarsSince(C < Ref(StopLong,-1)) > BarsSince(C >Ref(StopShort,-1)), StopLong, StopShort);
The following values of the Length parameter (illustrating the
distance between the Stop Trailing line and the stock price) havebeen adopted in this thesis for the respective cycles:• Short-term: 1.5• Mid-term: 3.0• Long-term: 4.5
Similarly, the relevant parameters for the Mid-term andLong-term indicators in the following indicator formulas areprovided in the comment.
Short-term/Mid-term/Long-term Cycle indicators
formula:SignalLine:= Fml(“Trailing Stop Short-term”);{ Mid-term: Fml(“Trailing Stop Mid-term”) / Long-term:Fml(“Trailing Stop Long-term”) }Cycle:= If(C > SignalLine, 1, If(C < SignalLine, -1, 0));
UP:= If(Cycle = 1, 1, 0);
DW:= If(Cycle = -1, -1, 0);2021 EDITION

Combined Cycles indicator formula:
CycleShort:= Fml(“Trailing Stop Short-term”);
CycleMid:= Fml(“Trailing Stop Mid-term”);
CycleLong:= Fml(“Trailing Stop Long-term”);
SignalLineUP:= Max(Max(CycleLong, CycleMid), CycleShort);
SignalLineDW:= Min(Min(CycleLong, CycleMid), CycleShort);
CyclesComb:= If(C > SignalLineUP, 1, If(C < SignalLineDW, -1, 0));
UP:= If(CyclesComb = 1, 1, 0);
DW:= If(CyclesComb = -1, -1, 0);
To compute the target aggregate indicators, it is necessary
to aggregate the result of the above indicators for all individual
equities comprised by the given index. It can be achieved inthree ways:• By using the Security function for every stock priceindividually.
• By using the Cum function with the GlobalVars.dll add-in in
Explorer.
• By exporting the values of individual indicators to an external
spreadsheet and importing the aggregated value back toMetastock.
The result of the above operation should be used in the target
aggregate Sentiment Indicators. It is marked by the prefix “Agg.”in the formulas presented below.
Short-term/Mid-term/Long-term Sentiment
Indicators Formula:
AdjustmentFactor:= 1 {range: 0-2};
Sentiment:= ((Agg.CycleShorttermUP*100) - (Agg.
CycleShorttermDW*(-100))) * AdjustmentFactor;{ Mid-term: Agg.CycleMidtermUP, Agg.CycleMidtermDW
Long-term: Agg.CycleLongtermUP, Agg.CycleLongtermDW }Sentiment.Gray:= If(Sentiment >= -50 AND Sentiment <= 50,
Sentiment, 0);
Sentiment.Blue:= If(Sentiment > 50 , Sentiment, 0);
Sentiment.Red:= If(Sentiment < -50, Sentiment, 0);
Sentiment.Gray;
Sentiment.Blue;
Sentiment.Red;
Long vs. Short Sentiment Indicators Formula:
AdjustmentFactor:= 1 {range: 0-2};
Long.Blue:= (Agg.CycleCombUP*100) * AdjustmentFactor;
Short.Red:= (Agg.CycleCombDW*(-100)) * AdjustmentFactor;
Long.Blue;
Short.Red;
The AdjustmentFactor parameter was used in aggregate
indicators, as it helps adjust the given indicators to the natureof the given market. It should also be noted that the factor
does not impact the manner of computing the indicator itself
or its ultimate form but only permits adjusting the absolutevalues adopted by the indicators so that they are better adaptedvisually to overbought and oversold levels.
IFTA.ORG PAGE 11

IFTA JOURNAL

2021 EDITION

Mohamed M. Khedr, CFTe, MFTA

The Noise Trend

Prime Securities
2 Wadi El Nile St., Mohandeseen, Giza, Egypt

By Mohamed M. Khedr, CFTe, MFTA

Abstract

With the various technical patterns exceeding their minimum
targets, what should the optimal strategy be for dealing withsuch situations? And how can we reduce the risks, especially
if the breakouts occurred without throwbacks or pullbacks to
the neckline in the head and shoulders patterns or to the apex
in the triangle patterns. This article searches for a new use ofamazing Fibonacci retracements to reduce risks and looks at thepossibility of anticipating peak and trough zones and identifying
oversold and overbought territories after determining thetarget zone, especially with the development of the behavior ofsome players in the market recently due to many reasons, mostimportantly the strength of technical analysis. I will explain
the impact of this development on trends and the movement oftechnical patterns. I will also explain how to deal with it.

Acknowledgements

I wish to express my sincere appreciation to my advisor, Dr.
Rolf Wetzer, for his aspiring guidance and for his patience. I
greatly benefited from his extensive experience. Also, I can’t
ignore the knowledge I got from the all the big financial experts,
especially Professor Charles K. Patrick and Professor Thomas
Bulkowski. Finally, my deep and sincere gratitude to my family
for their continuous and unparalleled love, help and support.

Introduction

As it is known, the financial market experts (Kirkpatrick II
and Dahlquist 2010) classified the market players into three
sections: informed, uniformed and liquidity player.
They define the noise player as one who has no knowledge
about evaluation norms or various analytical methods whether
it is technical or financial, and also, they are the market leaders
for a random movement around the equilibrium price.
How the Noise Player’s Behavior Has Evolved andthe Reasons Behind ThisTo clarify the above questions I will discuss these following
six points:

The power of technical analysis
The common issue now is that this model is a head andshoulders model; however, only 50% of its composition has been
completed. Therefore, this model has become the most popularamong individuals. And, the interest in this pattern came after
individuals ignored it, which led to great losses, to the extent thatsome mocked the name of the model because they didn’t know
the reason behind it. Besides, there is a shampoo on the market

PAGE 12 IFTA.ORG+201550205296
M.KhedrAfifi@gmail.com

with same name. But now, after achieving impressive results on
stock prices, they are showing a lot of respect to this model.
The technical reports from the various research fields haveachieved remarkable results, and the ability of technicalanalysis to measure momentum and predict changes inprice trends and technical analysis has been superior to theknowledge of financial analysis in terms of its ability to predict
financial market crises if it occurs reflective analysis patterns
with distant analytical targets.
All these reasons led some individual players (noise players)
to pay attention and read technical reports on a daily basis tohelp them make decisions.

Social media networks
These networks have contributed significantly to the spreadof technical recommendations that related to the market
and stocks, especially with the intense competition between
brokerage companies after the global financial crisis and theneed to attract new clients and portfolios.
In addition, social networks have been used as an advertisingand marketing method between companies by publishingbrief technical recommendations that clarify the general andexpected trends of stock prices in the coming period; here iswhere the problem showed up.
Money and fameSome brokers and users who aren’t specialized at technicalanalysis quoted the idea of the mini recommendation andapplied it by creating some pages technical in nature to show
various recommendations for buying and selling, which madethe followers of these pages to reach thousands because thefollowing two reasons:
1. The ease of speaking as a technical analyst and showing
recommendations—all you have to do is to write the stock
name, recommendation, and target (just number) without
attaching a graph or an explanation about when these
positions should be closed (the stop-loss level).
2. The success of this mini recommendation comes with theuptrend cycle, which then raises most stocks with different
rates (outperforming and underperforming stocks).
And with the high salaries of the technical analysts who
work at brokerage companies or funds, a lot of individuals are
motivated to attend basic technical analysis courses, especiallyafter one of the biggest securities companies at Egyptian
Market hired an administrator of the pages that containthousands of members at the company's marketing department.

IFTA JOURNAL

The technical analyst is not a machine or a system (study andimplementation)
Despite the simplicity of technical analysis in terms of study
and the ease of passing and obtaining the diploma of technicalanalysis after a short period of focus, as well as issuing reportsthat contain your technical view of the market, the actual
implementation and market trading is never easy because of
these two reasons:
1. Disadvantage of discretionary system (overtrading,premature actions, no actions, consistent decision-making
and emotions: the traders often lose money due to theiremotional decisions).
2. Changing positions from buying to selling or vice versa, andthe ability of the technical analyst to discipline, especially ifthat process will achieve a real break in his personal account(personal portfolio).
And here, I would like to refer to the definition of discipline asmentioned in the Cambridge Dictionary as “training that makes
people more willing to have energy and the ability to control
themselves.”
So the technical analyst in his early years needs to practiceand fully realize that if the rules are not followed, it will become
more complicated.
Therefore, the technical analyst can classify his behavior in
his first years as a noise player.
Trend is not always your friend
In order for the technical analyst to decide on the specificstrategy for buying or selling, the trend must be determined,
and based on this, the strategy is determined too. And so, thebest strategy for the uptrend is to buy and hold, while thedowntrend strategy is to sell the rebound.
In some emerging markets, such as Saudi Arabia, which does
not have a tool or a mechanism of short selling, and despite therecent implementation for this tool in some Arab markets, suchas the Egyptian market in 2019 and the Dubai Financial Market,
Abu Dhabi and Qatar market had this license in 2017, thatmechanism was not actually applied by investors for Islamic
religious reasons, which prohibits this type of borrowing.
So the trend may not be the real friend, especially during thedowntrend cycle, and then some individuals adopt the buying
strategy only with the steep down waves trying to make a profit
in the downtrend (countertrend strategy1).
Each rule has an exception (interest rate vs. stock market)
With banks raising interest rates on deposits and investment
certificates, investors are heading to these categories as a safe
asset, away from stocks, which are a risky investment. Raisingthese rates will raise the cost to borrowing companies, and someof it may be moving to accelerate their expansion plans and new
projects.
In the end, this will reduce the stock price that listed at themarket and in turn will lead to a decline in the market and its
indices, so we have two technical rules: the first one is threesteps and a stumble,2 and the second is two tumbles and a jump.3
But each rule has an exception. I will show two examples ofthe positive relationship between interest rates and the stock
market.

2021 EDITION

Example one—Discount rates vs. stock market in Argentina
Figure 1. Discount rate in Argentina (Source: Trading
economic | Central Bank of Argentina)

Figure 1 shows that interest rates rose from 20 points to 40
points at a rise rate of about 100% in the first half of 2016 andthen tried to retest the 20 point level in 2017 and then enter intoa sideway range between 20 to 40 points until July 2018. In thefirst half of August 2018, the interest penetrated the 40-point
barrier to reach 60 points in the last half of August 2018, withan increase rate of about 50%, and then the sharp rise wave
continued to reach the level of 83 points, which was the peakformed in the second half of 2018. Then, a correction wave will
start to test the 40 points in the first half of 2019.
Based on this, Argentina’s unprecedented rise in interest
rates from the bottom, which is created around the 20-pointlevel in December 2015 to 83 points in the last half of 2018,
which represents a 300% rise in interest rates over the three
years.
The question arises: What is the feedback of Argentina’s
financial market to the unprecedented rise in interest rates?
Figure 2. The monthly chart of S&P Merval (.MERV) –Index of Argentina

Figure 2 shows an example of the ascending triangle that was
penetrated in the first half of 2016, and after that, the index
direction changed from the sideway trend to the uptrend, beside
its rising from the support level of 9,200 points, at which thebottom formed in January 2016, to 35,500 points at the peak,which formed in January 2018, with a rise rate about 285% in
two years.

IFTA.ORG PAGE 13

IFTA JOURNAL

2021 EDITION

How did that happen? The answer is the flotation (theexception) and the need to determine the real interest rate.
Figure 3. The monthly chart of USD/ARS – U.S. dollar/
Argentinean peso

approximately the nominal interest rate minus the inflation
rate. The real interest rate measures the compensation for
expected losses due to default and regulatory changes as wellas measuring the time value of money; they differ from nominal
rates of interest by excluding the inflation compensation
component.
For example, if an investor was able to lock in a 5% interest
rate for the coming year and anticipated a 2% rise in prices, theywould expect to earn a real interest rate of 3%.
How to measure:Real interest rate = Nominal interest rate – expected inflation
rate.
Example two—Discount rates vs. stock market in Egypt
Figure 5. Discount rate in Egypt (source: investing.com)

Figure 3 shows the rise of the dollar's value against theArgentine peso from 9.80, which is the bottom created in
December 2015, to the peak level of 41.70 formed in the second
half of 2018, with a rise rate of 325%.
I will clarify the effect of this sharp decline in the currency ofArgentina (peso) against the dollar on inflation rate.
Figure 4. The bar chart of the inflation rate in Argentina(Source: tradingeconomics.com, Instituto Nacional de
Estadística y Censos (INDEC))

Figure 5 shows the rise in interest rates from the level of 9.75
at the end of December 2015 to reach the level of 19.25, which isthe peak formed in July 2017, with an increase rate of about 97%.
And in the beginning of 2018, specifically in November, interest
rates started in a correction wave to reach the level of 12.75 by
the end of 2019.
Figure 6. EGX 30 index (.EGX30) – Egyptian Stock Index

Figure 4 shows that the high rates of inflation in Argentina
from the level around 15% at the beginning of 2016, reachingaround 50% by the end of 2018. This means that inflation
rates increased in that period by about 233% as a result of thecurrency flotation decision, which in turn led to interest rates
increasing and controlling inflation.
And this is why we can say that both three steps and astumble and two tumbles and jump rules have exceptions, andthe exception here is the sharp change in the currency value andthe need to determine the real interest rate.
Therefore I would like to highlight more about the realinterest rate, which is the rate of interest an investor, saveror lender receives (or expects to receive) after allowing
for inflation. It can be described more formally by the
Fisher equation, which states that the real interest rate is

PAGE 14 IFTA.ORG

Figure 6 shows the rise of the Egyptian market index fromthe level of 5,550, which is the bottom formed in January 2016,reaching to the level of 15,721, which is the highest level ofthe index in November 2018, with a rise rate about 183%. And
despite the gradual decline of interest rates, this decline camewith a violent correction wave in the index, reaching the support
level of 12,000 points by the end of 2018.

IFTA JOURNAL

Figure 7. The monthly chart of USD/EGP – (US dollar/
Egyptian pound)2021 EDITION

Materials and Methods

This effect appears through two points: the noise trend andchanges in the psychology of some technical patterns.
The Noise Trend

Figure 7 shows the dollar rise against the Egyptian poundfrom the level of 9, which is the bottom formed in November
2016 (floatation decision), to the level of 19.19, which is the peakformed by the end of 2016, with a rise rate of the dollar against
the pound, which reached about 121% in two months, to appear
in 2017 as a correction wave that pushed the dollar down aroundthe level of 15.69, which is the bottom formed in February 2017.
Figure 8. The bar chart of the inflation rate in Egypt(Source: tradingeconomics.com | Central Bank of Egypt)Definition
The noise trend is defined as resulting from a random pricemovement and sharp fluctuations from a series of violent
uptrend waves in both the short and medium term, followed
by waves of sharp downtrends, which will erase the long-termfeatures of the main trend (more than a year). Beside this, thattrend is similar in its shape and composition to the sideway
trend but on a larger scale.
This trend caused sharp fluctuations in stock prices among
traders, which in turn contributed to the appearance of a newkind of market trend.
In general, the oscillating trend aimed at a price that moves
between support zones and other resistance and not at specificlevels.
Profiting from the noise trends: Hot money stole money
In the noise trend, the hot money makes a lot of money
through large-scale price movements that may reach its upward
wave rates exceeding 100% during a period not exceeding two
years after a sharp drop of waves that may range between 60
and 80%. I will show some historical statistics of this and theresults from these examples. (See Appendix A)
Figure 9. Egyptian Transport & Commercial Services Co.
SAE (ETRS.CA) – Equity in Egypt

Figure 8 shows the continuation of inflation rates in Egypt
from the level of 13.56 in October 2016, reaching the levelof 31.916, which is the peak formed in August 2017, with anincrease reached to about 135%.

Figure 9 shows how the price has been moved between thewide range levels (2.50:16.00) during the consecutive violent
waves.

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Table 1. Historical statistics of the noise trend for (ETRS.CA)
Date
The wave number

Price range

The first violent wave

3.47:16.27

July 2007

From

September 2008

To

Percentage (%)
369%

The second violent wave

16.27:4.63

September 2008

October 2008

71.50%The third violent wave

4.63:16.00

October 2008

October 2009245.50%The fourth violent wave

16.00:2.43

October 2009

June 201285%

The fifth violent wave

2.43:8.51

June 2012

September 2012250%

The sixth violent wave

8.51:3.41

September 2012

June 201360%

The seventh violent wave

3.41:9.74

June 2013

October 2014

185%

The eighth violent wave

9.74:2.80

October 2014

January 201671%

The tenth violent wave

2.80:15.96

January 2016

January 2018

470%

Note that these violent waves were measured from the troughs (the low price) to the peaks (the high price).
Statistical results:
Frequent historical price levels produced violent upward waves (3.50-4.60-2.40-3.40-2.80), so we will consider the levels between
2.40 and 4.60 as a significant zone. On the other hand, there were historical price levels that produced sharp declining waves (16.008.50-10.00), so we will consider the levels between 8.50 and 16 as a significant zone.
Figure 10. Ezz Steel (ESRS.CA) – Equity in Egypt

Figure 10 shows how the price has been moved between thewide sideways range (6.00:46.00) levels during the consecutive
violent waves.

Table 2. Historical statistics of the noise trend for (ESRS.CA)
Date
The wave number

Price range

From

To

Percentage (%)

The first violent wave

7.42:46.36

May 2005

February 2006

525%

The second violent wave

46.36:14.64

February 2006

June 2006

68%

The third violent wave

14.64:38.45

June 2006

April 2008

162.50%The fourth violent wave

38.45:5.84

April 2008

February 200985%

The fifth violent wave

5.84:26.00

February 2009

April 2010

345%

The sixth violent wave

26.00:3.28

April 2010

December 201187%

The seventh violent wave

3.28:19.50

December 2011

September 2014494.50%The eighth violent wave

19.50:6.11

September 2014

January 2016

68.50%The tenth violent wave

6.11:30.79

January 2016

April 2019404%

Statistical results:
Frequent historical levels produced violent upward waves (7.50-6.00-3.00), so we will consider the levels between 3.00 and 7.00 as asignificant zone. On the other hand, there were historical levels that produced sharp declining waves (46.00-38.50-19.50-31.00-26), so
we will consider the levels between 19.50 and 46.00 as a significant zone.
PAGE 16 IFTA.ORG

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Figure 11. Misr Chemical Industries (MICH.CA) – Equityin Egypt2021 EDITION

Figure 11 shows how the price has been moved between thewide range levels (3.00:16.00) during the consecutive violent
waves.

Table 3. Historical statistics of the noise trend for MICH.CA
Date
The wave number

Price range

From

To

The first wave
The second wave

Percentage (%)

3.17:16.58

December 2003

January 2006

423%

16.58:4.46

January 2006

July 2006

73%

The third wave

4.46:14.41

July 2006

May 2008

223%

The fourth wave

14.41:2.77

May 2008

October 200880.77%

The fifth wave

2.77:9.31

October 2008

September 2009236%

The sixth wave

9.31:3.00

September 2009

November 201167.77%The seventh wave

3.00:7.88

November 2011

June 2014162.66%The eighth wave

7.88:2.80

June 2014

November 201664%

The tenth wave

2.80:15.73

November 2016

January 2018

462%

The eleventh wave

7.88:2.81

June 2014

November 201664%

The twelfth wave

2.81:15.73

November 2016

January 2018

460%

Statistical results:
Frequent historical price levels produced violent upward waves (3.00-4.50), which will be considered as a significant zone. On theother hand, there were historical price levels that produced sharp declining waves (16.00-14.50-8.00-9.00), so we will consider thelevels between 14.00 and 16.50 as a significant zone.

More patterns, more noise
The appearance of the patterns with targets higher than thecurrent price intensively increases the chances of having thistype of trend.
Figure 12 shows another example of the noise trend between
levels of 5.50 and 42.00. Note that inside this trend there weremany technical patterns, such as a triple top pattern withmaximum target at the 5.50 level and counter target at the
30.50 level. After that, the stock witnessed a double bottom
pattern with a maximum target at the 42.00 level and thecounter target at the 17.00 level. The result is that the presenceof many technical patterns can cause this type of trend.

Figure 12. El Nasr Agricultural Crops (ELNA.CA) – Equityin Egypt

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2021 EDITION

How to reduce the sound of noise
The answer is to identify the zones of support and resistancebased on the historical price movements and then determine therisks of trading in the direction of the price, as the risk of sellingincreases near the historical support areas, even if the pricetrend is the downtrend until this zone is violated, while the risksof buying increases near the historical resistance areas even if
the price trend is the upward trend until this zone is penetrated.
More confidence in the technical indicators signals related todivergences or momentum, especially that which is given nearthese areas of historical support or resistance. There is morereservation when the validity of penetration is tested, suchas the penetration rules (3: 5%), where it is preferable to use
a penetration rule of 5% in this type of trend with sharp pricefluctuations to avoid the false breakouts.

The Technical Patterns
This behavior had an impact on the different technicalpatterns, especially the reversed trend, and its effect is divided
into two parts:
1. Re-testing: It became common not to re-test the neck line in
the most popular model (head and shoulders) besides the lackof return to the apex in most triangle models.
2. The Accurate Determination of Target: The lack of a carefultargeting, and here I mean the maximum target, so the pricecan easily exceed the minimum target and at other times
the maximum target. Besides the model target can be foundsomewhere between the minimum and the maximum target.
And now, I will show some important statistics of a financial
markets expert (Bulkowski 2005), related to the most important
technical patterns presented in his book Encyclopedia of Chart
Patterns.

Table 4. General statistics in a bull market (Source: Encyclopedia of Chart Patterns, 2nd Edition)
Number ofFormations

Pullback or
ThrowbackAverage Rise or
Decline (%)

Rise or Decline Over45.00 (%)

Change After Trend
End

Double bottoms, Adam
& Adam206

64.00%

35.00%

58.00 or 28.00%-33.00%

Double tops, Adam &
Adam

188

61.00%-19.00%9.00 or 5.00%

54.00%

Head and shoulders
tops

640

50.00%-22.00%34.00 or 5.00%

51.00%

Head and shoulders
bottoms

554

45.00%

38.00%

187.00 or 34.00%-31.00%

Rectangle bottom

115

53.00%

46.00%

48.00 or 42.00%-28.00%Descending triangles

312

37.00%

47.00%

125.00 or 40.00%-30.00%Ascending triangle

663

57.00%

35.00%

200.00 or 24.00%-29.00%Symmetrical triangle

476

54.00%

31.00%

144.00 or 30.00%-31.00%Formations

Pennants

173

47.00%

25.00%

27.00 or 16.00%-25.00%

Failing wedge

245

56.00%

32.00%

80.00 or 33.00%-28.00%Glossary explanation:
Change after trend end (See Appendix B): In the double
bottom, Adams & Adam, once prices top out at the ultimate high,then what happens? They tumble about 33%. In a bear market,
the decline gives up all the gains and more. For aggressive
traders in a bear market, wait for prices to peak and then shortthe stock and ride it down.
In an analysis of this previous statistical data we found that:
1. About 50% or more of these models (head and shoulders
top, head and shoulders bottom, the descending triangle
and pennant) didn’t rebound to the neckline or to the apex
after penetration, which refers to the strategy of waiting
for the throwback or pullback to buy or to add, and to shortdoesn’t go with many technical patterns, which leads to theneed for another strategy to avoid the possible unexpected
throwbacks or pullbacks.

PAGE 18 IFTA.ORG

2. Amazing results from the component models, such as
triangles, rectangles, and head and shoulders bottom,
for which targets exceed 45%, and that is a high rate andindicates the ease of exceeding the minimum target with thepossibility of exceeding the maximum of the pattern target,or that target is somewhere between the minimum and the
maximum of a perfect model target.
3. When comparing the average rise or decline ratios withchange after trend end ratios, we will find the following:

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2021 EDITION

Table 5. Comparing the statistical results (average rise or decline/change after trend end)

Formations

Return/Risk Ratio of the Tradingin the Direction of Pattern’s
Breakout

Average Rise or Decline (?)

Change After Trend End (%)

Double bottoms, Adam & Adam

35.00%-33.00%

1:1

Double tops, Adam & Adam

-19.00%54.00%

1:2.8

Head and shoulders tops

-22.00%51.00%

1:2.3

Head and shoulders bottoms

38.00%-31.00%1:0.80

Rectangle bottom

46.00%-28.00%1.65:1

Descending triangles

47.00%-30.00%1.56:1

Ascending triangles

35.00%-29.00%1.20:1

Symmetrical triangles

31.00%-31.00%

1:1

Pennants

25.00%-25.00%

1:1

Failing wedge

32.00%-28.00%1.14:1

A. Some models, such as double bottom, head and shoulders
bottom, symmetrical triangle, and pennant, with averageof rise or decline target ratios are equal to the change
after trend end ratios. This means that after achieving andreaching the target, the stock can witness a strong reboundthat may greatly reduce the profits achieved from the target’s
return.
B. Another form of models like the double top and head andshoulders top which their change after trend end ratios
exceeded more than the double of the average rise or decline
ratios. This means that after achieving and reaching thetarget, the stock can witness a strong rebound that may erase
all the profits achieved from the target’s return.
And this indicates to the following:
1. Trend is not always your friend (sometimes the tradingrisks increase in the direction of the current price).
2. These models had three price targets: the minimum oftarget (average rise or decline), the maximum of target(rise or decline over 45%) and the counter target (change
after trend end).
3. The need and importance of determining where thestock will peak or trough after the pattern breakouts tomaximize profits from these patterns by benefit of thecounter target.
4. The importance of managing the risks of trading in the
direction of the pattern breakout, especially when theprice exceeds the minimum target.
How can we reduce the risks of the counter-pattern targets
using Fibonacci retracement? When is stopping trading in the
direction of the breakout a must for the conservative investors?

Target Zone

The answer is to determine a target zone for each patternwhere the risks of trading in the direction of the pattern's
breakout are higher than the reward.
Definition
As breakouts from the support or resistance run to the nextzone of support or resistance, the breakout of minimum pricetarget in the chart patterns run to the maximum price target,
which represent 100% of the previous trend wave. This gives
the investors a price objective from trading below or above
the minimum target while the risk is the amount that the price
may go against the pattern breakout to bounce or to correct(countertrend bounce) and then risk/reward ratio can becalculated?
How to Measure?
Calculating the risk/reward ratio of trading below or above
the minimum target using Fibonacci retracement:
Step number one: The return ratio below or above theminimum target is the difference between the entry price andthe maximum of target divided by the entry level *100.

Step number two: The percent (38.20%) of Fibonacci
retracements is used to identify a countertrend bounce from thelast peak or trough within the pattern to the maximum target ofthat pattern.
Step number three: The risk ratio of trading in the directionof the patterns’ breakout (counter target) is the difference
between the minimum correction (countertrend bounce) andthe entry level*100.

Step number four: Comparing the risk ratio with reward ratio
to determine the target zone where the risk of correction ishigher than the reward of reaching the maximum target.

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2021 EDITION
Table 7. How to calculate the risk ratios of trading below
the minimum target

Implementation

Entry Level (Short)
Below the Minimum
TargetMinimum Reboundto the 38.20% of
Fibonacci, Which Is
Supposed to Start
From Maximum
TargetRisk Ratio (%)

33.00

40.80

23.63%

30.00

40.80

36%

29.00

40.80

40.68%

Figure 13. UAC of Nigeria PLC (UACN.LG) – Equity in
Nigeria

Based on step number four: risk/reward ratio comparison.
Table 8. Risk/reward ratio comparison of trading below
the minimum target

Figure 13 shows that the stock has witnessed a head andshoulders pattern; the minimum target was the level of 35.00
and the maximum target was the 24.00 level. It also shows how
the price easily exceeded the minimum target at the 35.00 levelto hit the 26.00 level, and then there was a strong bounce wave
from it by 69% to the 44.49 level (counter target).
The processes to handle with this situation:
Based on step number one:
Calculating the reward ratios of reaching the maximum
target at the 24.00 level:
Table 6. How to calculate the return ratios of reaching
the maximum target
Entry Level (Short)
Below the Minimum
TargetMaximum Target

Entry Level (Short)
Below the Minimum
TargetRisk Ratio (%)

Reward Ratio (%)

33.00

23.63%

27%

30.00

36%

20%

29.00

40.68%

17%

The results in Tables 7 and 8 show the increase in the risk of
trading in the direction of a pattern’s breakout compared to the
expected return of reaching to the maximum target startingfrom the level of 30.00, so our target zone that helps us toreduce the risks and avoid the counter target will be between
the 30.00:24.00 levels, where the stop selling is a must for theconservative investors based on the risk ratios exceeding the
reward ratio.
Figure 14. Anel Elektrik Proje Taahhut ve Ticaret AS(ANELE.IS) – Equity in Turkish

Return Ratio (%)

33.00

24.00

27%

30.00

24.00

20%

29.00

24.00

17%

Based on step number two:
The countertrend bounce is near the level of 40.80, which
represents 38.20% of Fibonacci retracement which was takenfrom the distance between the last pattern’s peak at the level of
68.00 and the maximum target at the level of 24.00.
Based on step number three:Calculating the risk ratios of the bounce wave (38.20% of
Fibonacci retracement), which is supposed to start from the
maximum target 24.00:

PAGE 20 IFTA.ORG

Figure 14 shows the double top reversal pattern that was
violated in March 2018, and it had a minimum target at 2.50
and a maximum target at 1.15. The graph also shows how theminimum target was easily broken until it succeeded in creating
a bottom at 1.50 to start a quick initial bounce reached to thelevel of 2.05.

IFTA JOURNAL

The processes to handle with this situation:
Based on step number 4:
Table 9. Risk/reward ratio comparison of trading below
the minimum target
Entry Level (Short)
Below the Minimum
TargetRisk Ratio (%)

Reward Ratio (%)

2.00

9%

42.50%

1.80

21%2021 EDITION

The processes to handle with this situation:
Based on step number one: Calculating the reward ratio of
reaching the maximum target at the 147.50 level:
Table 10. How to calculate the return ratios of reaching
the maximum target
Entry Level (Long)
Abovethe Minimum
TargetMaximum Target

Return Ratio (%)

36%

90

147.5063.88%

147.5047.50%

1.60

36%

28%

100

1.50

45%

23%

120

147.5022.92%

130

147.5013.46%The results in Table 9 show the increase in the risk of tradingin the direction of a pattern’s breakout compared to the
expected return of reaching to the maximum target startingfrom the level of 1.60, so our target zone that helps us to reduce
the risks and to avoid the counter target will be between
the 1.60:1.15 levels, where the stop selling is a must for theconservative investors based on the risk ratios being higher
than the reward ratios.
Figure 15. Brent Oil Futures (LCOc1) – Commodities

Based on step number two: The countertrend bounce will benear the 106.00 level, which represents 38.20% of Fibonacci
retracements (this correction ratio was measured from theright shoulder at the level of 39.00 to the maximum target at thelevel of 147.50).
Based on step number three: Calculating the risk ratios ofthe bounce wave (38.20% of Fibonacci retracement), which is
supposed to start from the maximum target 147.50.
Table 11. How to calculate the risk ratios of trading above
the minimum target

Entry Level (Long)
Above the Minimum
TargetMinimum Reboundto the 38.20% of
Fibonacci, Which Is
Supposed to Start
From Maximum
TargetRisk Ratio (%)

90

106

-

100

106

-

120

106

11.66%

130

106

18.46%

Based on step number four: Risk/reward ratio comparison:
Figure 15 shows a head and shoulders bottom has been
formed since March 2009 after penetrating the 50.00 barrier,and its targets were the minimum target around 68.00 andthe maximum target (100% of the previous downside wave)
at 147.50. The graph also shows how the price easily exceeded
the minimum target to hit the level of 128.50, which is thepeak formed in the month of March 2013. Then, this rally wasfollowed by waves of decline until it reaches a level of 88.50,
with a decrease by 30% from the ultimate high, which we called
the counter target.

Table 12. Risk/reward comparison of trading above theminimum target
Entry Level (Long)
Above the Minimum
TargetRisk Ratio (%)

Reward Ratio (%)

90-

63.88%

100-

47.50%

120

11.66%

22.92%

130

18.46%

13.46%

Table 12 shows the increase in the risk of trading in the
direction of a pattern’s breakout compared to the expected
return of reaching the maximum target starting from thelevel of 130.00, so our target zone that helps us to reduce
the risks and to avoid the counter target will be between the
130.00:147.50 levels, where the stop buying is a must for theconservative investors based on the risk ratios exceeding the
reward ratios.
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Figure 16. Crude oil (CLc1) – Commodities

Figure 17. Copper future (HGc1) – Commodities

Figure 16 shows a symmetrical triangle has been formed since
October 2014 after violating the support level of 85.00, and its
targets were the minimum target around the 41.00 level and the
maximum target (100% of the previous upward wave) was at the32.50 level. The graph also shows how the price easily exceededboth the minimum and maximum targets to hit the level of
26.00, which is the trough formed in the month of January 2016.
Then, this decline was followed by rising waves until it reacheda level of 75.50, with an increase by 190% from the ultimate low,which we called the counter target.

Figure 17 shows the double top reversal pattern that has been
violated in September 2011, and it had a minimum target at 3.10
and a maximum target at level of 1.25. The graph also shows
how the minimum target was easily broken until it succeededin creating a bottom at 1.95 (ultimate low) to start a bounce
reached to the level of 3.30 (counter target).

The processes to handle with this situation:
Based on step number two: The countertrend bounce will benear the level of 61.00, which represents 38.20% of Fibonacci
retracement (this correction ratio was measured from the last
peak of the symmetrical pattern at the level of 108.00 to the
maximum target at the level of 32.50).
Based on step number four:
Table 13. Risk/reward comparison of trading below theminimum target
Entry Level (Short)
Below the Minimum
TargetRisk Ratio (%)

Reward Ratio (%)

40.00

52.50%

18.75%

35.00

74%

7%

The results above show the increase in the risk of trading in
the direction of a pattern’s breakout compared to the expected
return of reaching the maximum target starting from the levelof 41.00, so our target zone that helps us to reduce the risks
and to avoid the counter target will be between the 41.00:32.00
levels, where the stop selling is a must for the conservative
investors based on risk ratios being double the reward ratio.

PAGE 22 IFTA.ORG

The processes to handle with this situation:
Based on step number two: the countertrend bounce will
be around the level of 2.50, which represents the 38.20% of
Fibonacci retracements (this correction ratio was measured
from the second peak of the head and shoulders pattern at thelevel of 4.50 to the maximum target at the level of 1.25).
Based on step number four:
Table 14. Risk/reward ratio comparison of trading below
the minimum target
Entry Level (Short)
Below the Minimum
TargetRisk Ratio (%)

Reward Ratio (%)

2.50

-

50%

2.30

8.69%

45.65%

2.00

25%

37.50%

1.90

31.58%

34.21%

1.80

38.88%

30.55%

Table 14 shows the increase in the risk of trading in the
direction of a pattern’s breakout compared to the expected
return of reaching the maximum target starting from the levelof 1.90, so our target zone which helps us to reduce the risks andto avoid the counter target will be between the 1.90:1.25 levels,
where the stop selling is a must for the conservative investors
based on the risk ratios being equal to or increasing more than
the reward ratio.

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2021 EDITION

Puzzle 3. Ternium Argentina SA (TXAR.BA) – Equity inArgentina

Focus on Failure
When Does This Method Fail to Achieve Its Main
Purpose of Reducing the Risk of Trading in the
Direction of the Pattern’s Breakouts?
From the research, we found that using this method to reduce
the risk of the counter target is consistent with most cases inwhich the patterns did not fail because it is based on the rules
and foundations of technical analysis, and they are correction
ratios (Fibonacci retracement) and the risk/reward ratio, butwhat if the maximum target is unknown, and this is happening
when the pattern’s targets reaches a new historical level (alltime high)?
Puzzle 1. McDonald’s (MCD) – Stock in USA

Puzzle 3 shows that the broadening formation has been
formed after breaking the barrier of 17.40 with minimum target
at the 24.00 level, but the maximum target is unknown (thestock moved at new historical levels).

(Source: investing.com)

Puzzle 1 shows a bullish triangle pattern has been formed
after penetrating the barrier at 102.00 levels with its minimum
target at approximately 122.00 levels. The maximum target is
unknown (the stock was moving in new historical levels).
Puzzle 2. Sociedad Comercial Del Plata (COME.BA) –Equity in Argentina

How to Overcome This Problem toDefine Our Target Zone

The solution for these puzzles is to use the amazing Fibonacci
retracements, especially the 423.00% that will be taken into
consideration as a significant resistance level (maximum
target), and it is calculated by taking the distance from the last
peak to the last bottom within the pattern.
The Implementation
Puzzle 4. Solving the puzzle number 1 McDonald’s (MCD)– Equity in USA(Source: investing.com)

Puzzle 2 shows an ascending triangle has been formed after
penetrating the resistance level of 1.95 with the minimum target
at the 2.65 level, but the maximum target is unknown (the stock
moved at new historical levels).

Puzzle 4 shows how the maximum target of the pattern isdetermined using the ratio 423% of Fibonacci retracement,
especially if the pattern is at new historical levels, and it iscalculated by taking the distance from the last peak at thelevel of 101.40 to the last bottom at the level of 87.50 within thepattern, which is in this case at the 145.00 level, which will beconsidered as a maximum target. Then we can find our target
zone.

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The process to find our target zone:
Based on step number four:

The process to find our target zone:
Based on step number four:

Table 15. Risk/reward ratio comparison of trading above
the minimum target

Table 16. Risk/reward ratio comparison of trading above
the minimum target

Entry Level (Long)
Above the Minimum
TargetRisk Ratio (%) of the Reward Ratio (%) of
Expected Counter Targeting the 145.00
Target at 123.00
Level (423.00%(38.20% of Fibonacciof Fibonacci
Retracement)
Retracement)

Entry Level (Long)
Above the Minimum
TargetRisk Ratio (%) of the
Expected Counter
Target at 2.65
(38.20% of Fibonacci
Retracement)

Reward Ratio (%)
of Targeting the3.37 Level (423.00%
of Fibonacci
Retracement)

125.001.60%

16.00%

2.70

1.85%

24.80%

130.005.38%

11.50%

2.80

5.35%

20.35%

132.00

6.82%

9.85%

3.05

13%

10.50%

133.00

7.50%

9.00%

3.10

14.50%

8.71%

134.00

8.21%

8.20%

Table 15 shows that our target zone will be between the134.00:144.00 levels, where the reward ratios are equal to or lessthan the risk ratio, and the counter target or strong correction is
expected to happen near this zone.
Puzzle 5. Solving the puzzle number 2 Sociedad
Comercial del Plata (COME.BA) – Equity in Argentina

Puzzle 5 shows how the maximum target of the pattern isdetermined using the ratio 423% of Fibonacci retracement, and it
is calculated by taking the distance from the last peak at the levelof 2.77 to the last bottom at the level of 1.67 within the pattern,which is in this case at the 3.37 level, which will be considered asa maximum target. Then we can find our target zone.

Table 16 shows that our target zone will be between the
3.05:3.37 levels, where the reward ratios are less than the risk
ratio, and the counter target or strong correction is expected tohappen near this zone.
Puzzle 6. Solving the puzzle number 3 Ternium
Argentina SA (TXAR.BA) – Equity in Argentina

Puzzle 6 shows how the maximum target of the pattern isdetermined by using the ratio 423% of Fibonacci retracement,
and it is calculated by taking the distance from the last peak at
the level of 15.20 to the last bottom at the level of 10.45 within
the pattern, which is in this case at the 30.60 level, which will
be considered as a maximum target. Then we can find ourtarget zone.
Based on step number four:
Table 17. Risk/reward ratio comparison of trading above
the minimum target

Entry Level (Long)
Above the Minimum
TargetPAGE 24 IFTA.ORG

Risk Ratio (%) of the
Expected Counter
Target at 22.90(38.20% of Fibonacci
Retracement)

Reward Ratio (%) ofTargeting the 30.60
Level (423.00%
of Fibonacci
Retracement)

26.00

11.92%

17.69%

27.00

15.19%

13.33%

28.00

18.21%

9.82%

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Table 17 shows that our target zone will be between the
27.00:30.70 levels, where the reward ratios are less than the risk
ratio, and the counter target or strong correction is expected tohappen near this zone.
Stock prices can go up endlessly. What will we do if the
supposed maximum target (423% of Fibonacci) is penetrated?
After breaking the 423%, which is considered as a strong
resistance level, we will use the penetration rule of 5% forconfirmation and then the stop loss has to activate (re-entry).

Results

Determining the risk/reward ratio enables us to manage therisks and in case of technical patterns that exceed the minimum
target, the reward is the maximum target and the potential risks
are the lowest rebound wave, which we suppose is a correction
rate of 38.20% of the Fibonacci ratios, which we also assume will
begin after reaching the maximum target. Ultimately, price levels
where the risk is higher than the reward rate are called target
zone make technical patterns more profitable for investors bytaking advantage of the countertrend.

Discussion

As for the two rules (two tumbles and jump and three steps
and a stumble), we should clarify how effective and importantthese rules are in developed markets. For emerging markets,
where currency rates are changing dramatically and where
inflation rates are affecting them, there is an urgent need todetermine the real interest rate and the real inflation rate
as well. There are some countries that do not give real dataon annual inflation rates so as not to stir up public opinion,
especially when the government makes difficult economic
reforms to the people, as they take a long time to show theirresults.

2021 EDITION

References
Bulkowski, Thomas N., Encyclopedia of Chart Patterns. New Jersey:
Wiley 2 edition, 2005.
Kirkpatrick II, Charles D. and , Julie R. Dahlquist, Technical Analysis:
The Complete Resource for Financial Market Technicians. New
Jersey: FT Press, 2010.

Notes

A countertrend strategy is a trading method that attempts to makesmall gains by trading against the current trend. Traders also referto the practice as countertrend trading.
2 Three steps and a stumble, which states that “whenever theFederal Reserve raises either the federal fund target rate, margin,requirement or reserve requirements three consecutive times
without a decline, the stock market is likely to suffer substantially.”
3 Two tumbles and jump, which is based on looking for two
consecutive declines, or tumbles, in the federal fund target rate andreserve requirements, and then the stock market rises. But every
rule has an exception.
1

Software and Data

All Data obtained from MetaStock Version 17.1.1932602,
and I would like to thank each of the sites (Investing.com &Tradingeconomics.com).

Appendices
Appendix A
Figure of Access Bank (ACCESS.LG) – Equity in Nigeria

Conclusion

The strength of technical analysis and its spread among
dealers had a significant impact on price movement, especiallyin emerging markets, which in turn led to the creation of a new
type of trend whose waves are characterized by violence. This
made it difficult to accurately determine targets (maximum
targets) in the more common technical patterns, and thisarranges the difficulty of calculating the return/risk ratio of
the trade in the direction of the pattern breakouts, especially
if the price exceeds the minimum target. Technical analysis did
not leave anything. Fibonacci retracements were the effective
tool to overcome the violent price movements. The 38.20% of
Fibonacci retracements is used as a trailing stop to determine
the target zone, while the ratio of 423% is used to find theunknown maximum target. The target zone has become anurgent necessity recently because it supports reducing therisks, which is a major goal of technical analysis, and not only tomaximize profits.

The monthly chart above is another example of a noise trend(similar to a sideways trend), which was moving between thelevels of 4.00:12.50. To reduce this sound of noise, it is necessary
to identify the support area, where the selling risks increase,and the resistance zone, where the buying risks increase, fromthe historical price movements of the stock.

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Appendix B
Table for Explanation (Source: Encyclopedia of Chart Patterns, 2nd Edition)
Formation

Explanation(Throwbacks OR Pullbacks and change after trend end)

Double Bottoms, Adam & Adam

*Since a throwback occurs 64% of the time in a bull market, you can initiate a position after thethrowback is complete or add to your position.
*Change after trend ends. Once prices top out at the ultimate high, then what happens?
They tumble about 33%. In a bear market, the decline gives up all the gains and more. For aggressive
traders in a bear market, wait for prices to peak then short the stock and ride it down.

Double Tops, Adam & Adam

*Change after trend ends. Once a price reaches the ultimate low, it soars by climbing 54% in a bull. Thus,
if you can determine when the price trend changes from down to up, buy.

Head and shoulders tops

*Change after trend ends. After a price reaches the ultimate low, it soars, climbing 51% in a bull market. If
you can determine when the trend changes, then buy the stock and hold on.

Head and shoulders bottoms*Change after trend ends. After a price reached the ultimate high, it tumbled by over 30%. That behavior
is a good reason for selling buy-and-hold positions.

Descending triangles*Change after trend ends. Once a price reaches the ultimate high or low, what happens? For upward
breakouts, prices tumble about 32%, on average. Thus, if you can determine when the price has changed,
even if you are late, you can still profit from it.

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Momen Atef El Shayal, CFTe, MFTA

Determination of Time Target Zones forPrice Targets of Classic Price Patterns

SIGMA Capital
37 Gameat El Dowal El Arabeya Street
Mohandessinm, Giza 12411
Egypt

By Momen Atef El Shayal, CFTe, MFTA

Abstract

With the claim that it is possible to begin to scientificallyanswer the difficult question of how long it takes to get thispotential profit, this paper came to uncover a new dimensionof some classic price patterns that reflect information about
the approximate duration within which their price targets are
expected to be achieved.
A new method that uses concepts of linear regression, and thestandard deviation is introduced here to determine time target
zones for the traditionally determined price targets of classic
price patterns.
Seven classic price patterns were tested to prove the thesis.
They include double top (and bottom); triple top (and bottom);bullish and bearish: rectangle; ascending triangle; descending
triangle; symmetrical triangle; and head and shoulders.
I believe that if we could get such information, we can makemore effective and well-planned investment decisions and it
will be much easier to choose from various coinciding trading
opportunities.

+201202257088
momen.elshayal@gmail.com

Technical Analysis Explained (Pring), and Technical Analysis: The
Complete Resource for Financial Market Technicians (Kirkpatrickand Dahlquist), tried to simplify them; moreover, others, like
Encyclopedia of Chart Patterns (Bulkowski) tried to collectmost of them. Although some of the classic price patterns canbe categorized as reversal patterns and others can do a dual
job as reversal and continuation patterns, in this paper, they
were categorized as bullish and bearish ones according to thebreakout direction (Figure 1).
Figure 1. Bullish and bearish price patterns

Introduction

In fact, most, if not all, efficient investment decisions aretaken after answering two main questions; the first is how
much is the expected return? And the second is how muchtime is expected to get that return? Since the decision to invest
in financial markets should be an efficient one, it needs to besupported by information about the expected time that isrequired to earn an expected return. At the same time, mosttechnical analysis theories focus on studying price action todetermine future price targets; however, they don’t focus withthe same degree on determining the time targets of these pricetargets.
For instance, classic price patterns are a cornerstone in the
body of technical analysis; they reflect how history repeats
itself throughout frequent battles—based on fear and greed—
between buyers and sellers. These battles appear on charts asrecognizable repetitive sideways price patterns, and an easyminimum price target can be determined once a price breakout
occurs by measuring the largest vertical distance between thepattern’s boundaries and projecting it from the breakout point.
So far it is good, but what about the time it takes to achieve thisprice target?!!
Actually, there are a lot of classic price patterns with different
degrees of fame. Some books, such as Technical Analysis of
Stock Trends (Edwards, Magee and Bassetti) and Trading Classic
Chart Patterns (Bulkowski), tried to explain them in details;
others, like Technical Analysis of Financial Markets (Murphy),On one side, theoretically, the last price swing in the pricepattern represents the beginning of imposing control from thewarrior who finally wins the trading battle. So, the focus on thelast price swing can certainly reveal something worthy about
the remaining power of the victor.
On the other side, the flashing sentence that Perry J. Kaufman
included in the 5th edition of his book Trading Systems andMethods—“A time series is not just a series of numbers, butordered pairs of price and time.”—made me think about the fact

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that we already have two axes (price and time), and completedclassic price patterns enable us to determine a future point on
the price axis, so there must be a way to get the opposite pointon the other axis (the time axis).
After gathering all together with the aid of a simple
statistically based tool, which is the standard deviation channel,
a new method for determining time target zones of the pricetargets generated by classic price patterns is introduced here.
Figure 2. Standard deviation channel

The standard deviation channel (which is included in mostcharting programs) is also alternatively referred to as the linear
regression channel. And as the name implies, it consists of two
standard deviation bands above and below an x-period linear
regression line (Figure 2).
In origin, this price analysis’ tool is used for identifying theprice trend and the standard deviation bands usually act as
support and resistance levels and are used for trading purposes,by the way. However, in this paper, it is used differently.
Theoretically, the linear regression is a statistical tool thatis used to find a linear relationship between two variables (Xand Y), which enables one to find the value of Y (the dependent
variable) for each value of X (the independent variable), and in
the case of Price and Time, we can find the value of time for thevalue of a specific price. It uses the least-squares method to plota straight line through data to minimize the distances between
the actual data and the resulting line, and to find the beststraight-line fit, the following formula is used: ŷ = b(x) + a
Where [ŷ] is the predicted value, [b] is the slope, and [a] is they-intercept (the value of y when x = 0).
A distinct advantage of linear regression is that it canforecast future price movement by simply extending theresulting straight line. If we can consider that the regression
values give the mean over the calculation period, then theforecast is an extension of the mean.
Besides, the bands of the standard deviation channel areplotted to determine the approximate range of the dispersion
of price movements around the regression line. By default, thestandard deviation bands are set to two as according to thenormal distribution; 95% of the observations lie within two
standard deviations of the mean.
By applying the standard deviation channel tool on the lastswing of the price pattern and extend it straight forward; we
can assign a time target zone for the assigned price target of thecompleted price pattern.

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Materials and Methods
Data and Period of Investigation
To test the thesis presented in this paper, daily historical dataof three different liquid financial markets from January 2008
until December 2018 was examined. It should also be noted thatall price data used throughout the paper were obtained from
Reuters.
The financial markets that were examined are:• Equity market (S&P 500)
• Commodity market (Light crude oil)
• Currency market (EUR/USD pair)Here, I’ve chosen seven famous classic price patterns—headand shoulders, double bottoms and tops, triple bottoms andtops, rectangle, ascending triangle, descending triangle, andsymmetrical triangle—and extracted them manually from daily
charts of the three aforementioned instruments. The period
of investigation from January 2008 until December 2018 wasselected, as it included a great variety of up and down trends
and nearly witnessed all market conditions where plenty ofclassic price patterns could be captured.
Types of Completed Price Patterns
In theory, classic price patterns can’t be considered ascompleted ones until an actual price breakout occurs. Further,
prices usually react in one of two certain ways after the
breakout. Either a pullback movement to the broken boundaryof the completed price pattern will occur before achieving theprice target or prices will continue directly toward the target ofthe pattern.
In this section, I will explain the method of determining thetime target zone for the price target of a completed price patternin each case. Also, it should be mentioned that this method canbe applied to six of the seven price patterns that are examinedin this paper exactly in the same way; however, the head andshoulders price pattern is considered a special case, which willbe also explained later in this section.

Completed Price Patterns Without Pullback Movement Afterthe Breakout
We can determine a time target zone for the price targetof a completed price pattern that isn’t followed by a pullback
movement to the broken boundary once a price breakout occurs
by plotting the standard deviation channel on prices startingfrom the lowest day in the last price swing before the actual
bullish breakout till the breakout day then extend the channel tothe right (Figure 3). The time target zone is the period between
the two vertical lines projected from interception points ofthe price target level and the bands of the extended standard
deviation channel. The opposite is true in the case of bearish
breakouts (Figure 4).

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Figure 3. The time target zone for the price target ofa completed bullish price pattern without a pullback
movement

Figure 4. The time target zone for the price target ofa completed bearish price pattern without a pullback
movement

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Figure 6. The time target zone for the price target of acompleted bearish price pattern followed by a pullback
movement

The special case of head and shoulders price pattern
The head and shoulders price pattern has a special measuring
criterion. In bullish cases, the standard deviation channel
should start from the lowest day of the pattern (the head) until
the breakout day or the last day of the pullback movementthat follows the breakout. The opposite is true in bearish cases(Figures 7 and 8).
Figure 7. The time target zone for the price target ofbullish H&S price pattern without a pullback movement

Completed price patterns followed by pullback movements
after the breakout
If a pullback movement to the broken band of the completed
price pattern followed the actual breakout, we should add thispiece of data to the measurement. The standard deviation
channel should be started from the lowest day of the last priceswing before the breakout (or the highest day in case of bearish
breakouts) until the last day in the pullback movement after the
breakout, and then extend the channel to the right (Figures 5
and 6).

Figure 8. The time target zone for the price targetof bearish H&S price pattern followed by a pullback
movement

Figure 5. The time target zone for the price target of acompleted bullish price pattern followed by a pullback
movement

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Examples From Various Financial Markets

Figure 12. Itau Unibanco holding (ITUB4.SA)—Daily

Double bottom and double top price patterns
Figure 9. Credit Agricole bank (CAGR.PA)—Daily

An example from the French stock market is shown in adaily chart of Credit Agricole bank (Figure 9). After finishing adouble bottom price pattern then a pullback movement to the
middle peak, the price target of the pattern was achieved lateron within the determined time target zone between August 18,
2012, and September 14, 2012.

From the Brazilian stock market, Figure 12 shows a daily
chart of Itau Unibanco holding, which spotted another double
top price pattern, but this time without a pullback movement.
The price target was also achieved within the determined timetarget zone between November 26, 2010, and December 20,
2010.

Triple bottom and triple top price patterns
Figure 13. JPY (JPY=)—Daily

Figure 10. Jazira bank (1020.SE)—Daily

Figure 14. Light crude oil (CLc1)—Daily

Figure 10 shows a case of a double bottom pattern without apullback movement that occurred in a daily chart of the Jazirabank in the Saudi stock market. The price target of the pattern
was achieved within the determined time target zone between
December 13, 2017, and January 28, 2018.
On the other hand, Figure 11 shows a completed double top
price pattern, which is followed by a pullback movement in
the Silver. Subsequently, the price hit the target of the pattern
within the determined time target zone between October 18,
2012, and November 6, 2012.
Figure 11. Silver (XAG=)—Daily

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Figure 13 shows a case of a triple bottom price pattern
followed by a pullback movement that appeared in a daily chartof the Japanese Yen. Again, the price target of the pattern wasachieved within the determined time target zone between
November 15, 2012, and January 12, 2013.
Figure 14 shows a daily chart of the light crude oil which
illustrates a triple bottom pattern without a pullback movement
followed by a minimum target that was achieved within thedetermined time target zone between January 2, 2013, andJanuary 25, 2013.
Here in Figure 15, a triple top price pattern appeared in a daily
chart of the Gold followed by a pullback movement afterward;
the minimum target of the completed pattern was achieved
within the determined time target zone between June 20, 2018,
and September 19, 2018.

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Figure 15. Gold (XAU=)—Daily

Figure 18. Deutsche Bank (DBKGn.DE)—Weekly

From the London stock market, Figure 16 shows another triple
top price pattern without a pullback movement appeared in adaily chart of Barclays PLC. The price target of the completed
price pattern was achieved later on within the determined timetarget zone between October 28, 2009, and November 16, 2009.

In another example from the London stock exchange, Figure
19 shows a bearish rectangle price pattern followed by apullback movement. The weekly chart of the Anglo-Americanstock illustrates that the price target of the completed pricepattern was achieved later on within the determined time target
zone between February 20, 2015, and November 20, 2015.
From the French stock exchange, an example of a bearish
rectangle price pattern without a pullback movement is shownin Figure 20. The daily chart of Danone shows that price target ofthe completed price pattern was hit within the determined timetarget zone between February 8, 2018, and February 28, 2018.

Figure 16. Barclays PLC (BARC.L)—Daily

Figure 19. Anglo American (AAL.L)—Weekly

Bullish and bearish rectangle price patterns
The GBP pair daily chart shows a bullish continuation
rectangle price pattern followed by a pullback movement(Figure 17). The price target of the completed price pattern
was hit subsequently within the determined time target zone
between December 10, 2013, and January 31, 2014.
Figure 18 shows another bullish rectangle price pattern, but
this time without a pullback movement. It was spotted in aweekly chart of Deutsche Bank which is traded in the German
stock market. Here again, the price target of the pattern was hit
within the determined time target zone between April 24, 1998,
and July 24, 1998.

Figure 20. Danone (DANO.PA)—Daily

Figure 17. GBP (GBP=)—Daily

Bullish and bearish ascending triangle price patterns
The weekly chart of the Canadian Dollar pair (Figure 21) shows
an example of a bullish ascending triangle pattern followed bya pullback movement. The price target of the pattern was hit
within the determined time target zone between September 26,
2008, and January 16, 2009.

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Figure 21. CAD (CAD=)—Weekly

Also, from the New York stock exchange, Figure 24 showsa daily chart of Bank of America’s stock, which captured abearish ascending triangle price pattern without any pullback
movements; its target was hit within the determined timetarget zone between April 2, 2018, and April 19, 2018.

Bullish and bearish descending triangle price patterns
Figure 25. Ten-year U.S. treasury notes (TYc1)—Daily

Figure 22. Ten-year treasury notes (TYc1)—Daily

Figure 22 shows a ten-year U.S. treasury notes daily chart,
which spotted a bullish ascending triangle price pattern thatwasn’t followed by a pullback movement. After finishing theprice pattern, the minimum price target was achieved within
the determined time target zone between November 2, 2001,
and November 14, 2001.
In Figure 23, an example of a bearish ascending triangle pricepattern followed by a pullback movement was spotted on a daily
chart of General Electric Company’s stock, which is traded in the
New York stock exchange. The target of the completed pattern
was hit within the determined time target zone between March
12, 2001, and March 23, 2001.

Another daily chart of the ten-year U.S. treasury notes (Figure25) shows a bullish descending triangle pattern followed by apullback movement, and the minimum target of the pattern wasachieved within the time target zone between August 26, 1998,
and November 6, 1998.
Figure 26 shows a daily chart of Tadawul all shares index that
captures a bullish descending price pattern without a pullback
movement. The price target of the pattern also was achieved
within the determined time target zone.
In Figure 27, a bearish descending triangle price pattern,which is followed by a pullback movement, was spotted in anhourly chart of the Euro pair. Here again, the price target of thecompleted pattern was hit within the determined time target
zone.
Figure 26. Tadawul all shares index (.TASI)—Daily

Figure 23. General Electric Co. (GE.N)—Daily

Figure 27. EUR (EUR=)—Hourly
Figure 24. Bank of America (BAC.N)—Daily

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Figure 28. CHF (CHF=)—Daily

Figure 31. Wanhua Chemical Group Co. LTD (600309.
ss)—Daily

The last example of descending triangles was a bearish one
without a pullback movement that was captured in a daily chartof the Swiss Franc pair (Figure 28). One more time, the pricetarget of the pattern was hit within the determined time target
zone.

Figure 31 shows an example from the Shanghai stock
exchange; a bearish symmetrical triangle pattern followed bya pullback movement is spotted in a daily chart of the Wanhua
Chemical Group. The price target of the pattern was hit within
the determined time target zone between September 3, 2008,
and October 13, 2008.
Figure 32 shows another bearish symmetrical triangle pricepattern, but without a pullback movement. The daily chart ofAlphabet INC illustrates that the price hit the target within thedetermined time target.

Bullish and bearish symmetrical triangle price patterns
Figure 29. Commercial International Bank (COMI.CA)—
WeeklyFigure 32. Alphabet INC (GOOGL.O)—Daily

Figure 30. Ezz Steel (ESRS.CA)—Weekly

Bullish and bearish head and shoulders price patterns
Figure 33. Orascom Development (ORHD.CA)—Hourly

From the Egyptian stock market, a case of a bullish
symmetrical triangle price pattern appeared in a weekly chartof the Commercial International Bank (Figure 29). After apullback movement to the cleared boundary of the pattern, theprice trusted and hit the price target within the determined
time target zone between December 26, 2013, and June 5, 2013.
Another case from the Egyptian stock market is in a weekly
chart of Ezz Steel’s stock, which shows a bullish symmetricaltriangle price pattern and its price target that was also achieved
within the determined time target zone (Figure 30).

Figure 34. Deutsche boerse dax index (.GDAXI)—Daily

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An hourly chart of Orascom Development Egypt shows abullish head and shoulders price pattern followed by a pullback
movement and its target that was hit within the determined
time target zone between November 22, 2018 at 1:00 pm, andDecember 25, 2018 at noon (Figure 33).
Figure 34 shows another bullish head and shoulders pricepattern but with no pullback movement. The daily chart of themain index of the Germain stock market illustrates that theprice target of the completed pattern was achieved within thedetermined time target zone.

Figure 37. Frequency distribution of successful pricepatterns that appeared in the three instruments acrossthe investigation period

Figure 35. Egyptian Financial and Industrial SAE (EFIC.
CA)—Weekly

Figure 36. EGX30 (.EGX30)—Daily

Figure 38 shows that 92% of double tops and bottoms
patterns succeeded in achieving the price target of the pattern
within the determined time target zone, while only 8% failed;
95% of triple tops and bottoms price patterns succeeded while
only 5% failed; 100% of rectangle price patterns succeeded; 84%
of ascending triangle price patterns succeeded while 16% failed;
91% of descending triangle price patterns succeeded while only
9% failed; 71% of symmetrical price patterns succeeded while
only 29% failed; and finally, 84% of the H&S price patterns
succeeded in achieving the price target within the determined
time target zone while only 16% failed.
Figure 38. The success rate of achieving the price target
within the determined time target zone for all patterns

Two more examples of bearish head and shoulders pricepattern from the Egyptian stock market appear in Figures
35 and 36. The weekly chart of the Egyptian Financial andIndustrial stock shows an example of the pattern followed
by a pullback movement while the daily chart of the mainindex shows another example without a pullback movement.
In the two cases, the price targets were achieved within thedetermined time target zone.

Statistical Analysis of Time Target
Zones of Price Targets of Classic
Price Patterns

In a visual inspection of the daily data of the S&P 500 index,
the Light crude oil and the EUR/USD pair from January 1, 2008,
until December 31, 2018, disclosed 38 double top or bottom
patterns, 21 triple top or bottom patterns, 27 rectangle patterns,
19 ascending triangle patterns, 23 descending triangle patterns,
28 symmetrical triangle patterns, and 49 head and shoulders
patterns that achieved their price targets (Figure 37).

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Figure 39 shows that most of the bullish patterns havesucceeded in hitting their price target within the determined
time target zone more than triple the failure times except forthe bullish symmetrical triangle pattern, which recorded amoderate success rate.

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Figure 41. Frequency distribution of successful pricepatterns that followed by pullback movementsthat appeared in the three instruments across theinvestigation period

Figure 39. The success rate of achieving the price target
within the determined time target zone for bullish
patterns

Figure 42. The success rate of achieving the price target
within the determined time target zone for bullish
patterns with pullback movements

Figure 40. The success rate of achieving the price target
within the determined time target zone for bearish
patterns

Figure 42 shows that again, except for the bullish symmetricaltriangle patterns followed by pullback movements, which
recorded a 1:1 success rate, five patterns recorded the full mark,and the rest recorded sufficiently high success rate.
Figure 43 shows superiorly high success rates for bearish
patterns followed by pullback movements, including higher
rates for the bearish symmetrical triangle and bearish
ascending triangle patterns than the bullish ones.
On the other hand, Figure 40 reflects the superiority of the
bearish patterns with four patterns recording the full mark andthe rest recording sufficiently high success rates.
Figure 41 shows the number of patterns followed by a pullback
movement before achieving the price target out of the total
patterns that appeared.

Figure 43. The success rate of achieving the price target
within the determined time target zone for bearish
patterns with pullback movements

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Figure 44. Frequency distribution of successful pricepatterns that weren’t followed by pullback movements
which appeared in the three instruments across theinvestigation period

Figure 44 shows the number of successful patterns that
achieved their price target directly after the breakout without
pullback movements out of the total patterns that appeared.
Figure 45. The success rate of achieving the price target
within the determined time target zone for bullish
patterns without pullback movements

Figures 45 and 46 show that the bearish patterns that weren’t
followed by a pullback movement recorded higher success rates
than the bullish ones except for the symmetrical triangle andthe head and shoulders patterns.
Figure 46. The success rate of achieving the price target
within the determined time target zone for bearish
patterns without pullback movements

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Figure 47. Percentages achieving the price target within
the first or second half of the determined time target
zone for bullish patterns

Figures 47 and 48 show that in general, price targets of the
bearish patterns are achieved most often within the first halfof their time target zones, despite the bullish ascending anddescending triangle patterns proving better performance inthat matter.
Figure 48. Percentages achieving the price target within
the first or second half of the determined time target
zone for bearish patterns

Summary

Identifying an expected return (or—as in technical analysis’terminology—a price target) is an advantage in the financial
markets environment, however definitely, if it is accompanied
by a time target to be achieved in, it will be superior.
This paper introduced an approach that tried to add morevalue to seven of the classic price patterns through the ability
to identify time target zones for their traditional price targets
by applying linear regression on the last price swing in theprice pattern.
A variety of examples from various financial markets on
multiple timeframes and throughout multiple decades were
included in this paper as proof of the validity and applicability ofthe thesis throughout the age of the classic price patterns.
Finally, like any tool or approach, our approach also is notinfallible, despite the result of the statistical analysis of theexamined data reflecting satisfying overall success rates.
Moreover, the bearish patterns seemed to have the upper hand,

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especially in achieving the targets within the first half of thedetermined time target zone.

References
Aronson, David R., Evidence-Based Technical Analysis. New Jersey: John
Wiley & Sons, 2007.
Bulkowski, Thomas N., Encyclopedia of Chart Patterns. New York, UnitedStates: Wiley, 2005.
—. Trading Classic Chart Patterns. United States: Wiley, 2002.
Edwards, Robert D., John Magee and W.H.C. Bassetti, Technical Analysisof Stock Trends, 9th ed. CRC Press, 2007.
Kaufman, Perry J., Trading Systems and Methods, 5th ed. Hoboken, New
Jersey: John Wiley & Sons, 2013.
Kirkpatrick, Charles D. and Julie R. Dahlquist, Technical Analysis: The
Complete Resource for Financial Market Technicians. FT Press, 2006.
Murphy, John J., Technical Analysis of the Financial Markets. Welwyn
Garden City, United Kingdom: NYIF, 1998.
Pring, Martin J., Technical Analysis Explained, Fifth Edition. UnitedStates: MCGRAW-HILL, 2014.

Notes

Murphy, John J. Technical Analysis of the Financial Markets. Welwyn
Garden City, United Kingdom: NYIF, 1998, P. 108-110.
2 Kirkpatrick, Charles D. and Julie R. Dahlquist, Technical Analysis: The
Complete Resource for Financial Market Technicians. FT Press, 2006,
P. 302-303.
3 Pring, Martin J., Technical Analysis Explained, 5th ed. United States:
MCGRAW-HILL, 2014, P. 65.
4 Kaufman, Perry J., Trading Systems and Methods, 5th ed. Hoboken,
New Jersey: John Wiley & Sons, 2013, P. 236.
5 Kaufman, Perry J., Trading Systems and Methods, 5th ed. Hoboken,
New Jersey: John Wiley & Sons, 2013, P. 238-239.
6 Kaufman, Perry J., Trading Systems and Methods, 5th ed. Hoboken,
New Jersey: John Wiley & Sons, 2013, P. 250.
7 Aronson, David R., Evidence-Based Technical Analysis. New Jersey:
John Wiley & Sons, 2007, P. 212.
8 Edwards, Robert D., John Magee and W.H.C. Bassetti, TechnicalAnalysis of Stock Trends, 9th ed. CRC Press, 2007, P. 62.
9 Murphy, John J. Technical Analysis of the Financial Markets, Welwyn
Garden City, United Kingdom: NYIF, 1998, P. 106.
1

Software and Data

Data and charts provided by Reuters.

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IFTA JOURNAL

2021 EDITION

Emerging Currencies as Equity Earthquake
Indicator
By Ron Albert Marcelino Acoba, CFTe, CMT, MFTA

Abstract

Ron Albert Marcelino Acoba, CFTe, CMT, MFTA
39 Guatemala Street, Loyola Grand Villa
Barangay Pansol
Quezon City, Metro Manila 1108
Philippines+639175814766
ronacoba@gmail.com

Historical Anecdotal Precedents

Investing in the equity markets traditionally entails
institutional analysts and investors alike to primarily focus
on corporate earnings and the health of the economy bylooking at macro indicators such as GDP growth, inflation, an
unemployment rate, among others, along with the price andvolume actions of the general market and individual issues.
Not much attention, however, is given to the impact of adverse
movements in currencies in relation to equities. Unbeknownst
to many retail investors, currency volatility is a major catalystof hot money flight. The aim of this paper is to demonstratethat the movements in emerging currencies can be used as aseismograph to forecast short-run equity movements and ascues to actively defend against market aftershocks.

Acknowledgements

I would like to thank my wife Charlene, who is also mybusiness partner in Trading Edge, for continually inspiring me
and motivating me in advancing our research in the financial
markets particularly in the field of technical analysis. I would
also like to thank my family, parents, and brother for theirconstant support in our professional endeavor. Last but not
the least, I would like to express my gratitude to my students,
subscribers, Trading Edge’s corporate clients, and media
partners for their confidence and trust in our work.

Introduction
Seismograph
A seismograph or a seismometer, first invented in the 2nd
century in China, is an instrument that measures and records
the movements in the earth or “seismic waves” caused byan earthquake, volcanic activity, explosion, or other earthmoving phenomena. A seismograph produces a record of earth
movements onto a screen or a paper printout. This is called aseismogram or, in terms of the financial markets, a price chart.
History is rich with notable earthquakes—from the deadliest
ever recorded in Shaanxi, China, in 1556, to Kobe in Japan
in 1995, to Aceh, Indonesia, in 2004, and to Haiti in 2010. In
a similar fashion, the financial markets are likewise rich innotable currency events that placed a lot of economies andinvestors alike to their knees. This paper first looks at thehistory of different currency crises in the emerging markets
and assesses their consequent impact on their respective equity
market. We can then use the quantitative observations of thepast to actively defend our portfolio from aftershocks in theequity markets should they occur later on.

PAGE 38 IFTA.ORG

Asian financial crisis
The epicenter of the Asian financial crisis that crumbled muchof East Asia’s economy was in Thailand. For most of the 1980s
and 1990s, Thailand, along with South Korea, Singapore, Taiwan,
and Hong Kong, was in a boom cycle. The group was actuallytermed as “Tiger economies” due to the high GDP growth thatthey were seeing in the period. Thailand’s economy for its part at
one point grew by a high of 11.2% in the 1990s. The high growththat the region, particularly Thailand, was seeing attracted a
lot of foreign funds. Cheap interest rates of dollar denominatedborrowings, false security by corporate borrowers due to theThai government’s encouragement or “guarantee,” and a fixedexchange rate regime eventually led to over borrowing. Therecame a point where funds were being channeled to negative NPV
ventures (Corsetti and others 1998).
An unprecedented amount of capital inflows, together with adrop in exports, widened the country’s current account deficit(Aghevli 1999). What made matters worse was that a good
portion of the capital inflows came in as short-term borrowings(Carson and Clark 2013). This situation eventually sparked an
external attack by shorters on the Thai Baht (Dumlao 2017).
It also did not help that the government burnt a good amountof their dollar reserves in their attempt to keep and protect
the peg. The 25 Baht to $1 peg was shortly uncorked to 30
Baht to $1 on July 2, 1997. This event led to a series of currency
devaluations that griped East Asia. In the first seven months
after the central bank of Thailand unpegged their exchange
rate, the Baht fell to a low of 56.75 against the USD. Similarly,
the Korean Won fell to a low of 1,995 against 1 USD from only
888 at the end of June 1997. The Indonesian Rupiah suffered a far
worse blow as it weakened to a low of 15,650 against 1 USD fromonly 2,431.50. The Malaysian Ringgit and the Philippine Peso
likewise suffered more or less the same fate as their respective
currencies melted to 4.88 from 2.52 and 46.80 from 26.38
against the greenback.
As investors dropped the Asian currency denominated assets
like hot stone fresh from a spewing volcano, the Asian equity
markets likewise faced a similar demise. The Thailand Stock
Market (SET50) fell from a high of 535 in July 1997 to a low of 134
in the next 18 months. The Korea Stock Exchange Composite
Index (KOSPI) fell from a high of 782 to a low of 277 in the same
period. Indonesia’s Jakarta Stock Price Index (JCI) fell from ahigh of 742 to a low of 255. Malaysia’s Kuala Lumpur Composite
Index (KLCI) declined to a low of 261 from a high of 1,085 while
the Philippine Stock Exchange Index (PHISIX) slumped to a lowof 1,075 from a high of 2,815 in the 18 months after July 1997.

IFTA JOURNAL

Table 1. THBUSD versus SET 50 (July 1, 1997, to December
30, 1998)

Table 2. KRWUSD versus KOSPI (October 17, 1997, to April
19, 1999)

Table 3. IDRUSD versus JCI (July 9, 1997, to January 11,
1999)

2021 EDITION

bribes and kickbacks to different politicians while the rest
were allegedly embezzled for personal the use of Najib and
Low (Wright 2015). Swiss authorities said that as much as $7
billion flowed from 1MDB and its units (Adam and others 2018).
The news of embezzlement, corruption, money laundering,and betrayal of public trust, among others, by no less than theprime minister of Malaysia, cast an ash cloud on the country’s
economy and political leadership.
Prior to the 1MDB scandal, the Malaysian Ringgit was alreadyreeling because of the 45% slide in the global prices of crude(WTIC) from its June 2014 high to the end of June 2015. Crude
was Malaysia’s number 1 export and a slip in price naturally
meant lesser dollars flowing into the government’s coffers(Y-Sing 2014). The scandal that came into light in July all themore gave foreign investors the reason to flee the country. This
was then aggravated further when China devalued the Yuan inAugust 2015 in their attempt to shore up their exports (Farrer2015). From July 1, 2015, the US dollar to Malaysian Ringgit
exchange rate zoomed from 3.78 to a high 4.48 inside just 3
months. In the same time period, the Kuala Lumpur Composite
Index fell from 1,706 to a low of 1,503.
Table 6. MYRUSD versus KLCI (July 1, 2015, to January 2,
2017)

Table 4. MYRUSD versus KLCI (July 10, 1997, to January
11, 1999)

Table 5. PHPUSD versus PHISIX (July 10, 1997, to January
11, 1999)

1Malaysia Development Berhad (1MDB) Scandal
The 1MDB scandal is an incident that placed a spotlight on
Malaysia’s corruption-laden government dealings. 1MDB wasa government-run investment company with the mission topromote the country’s long term development and foreign
capital investments. Its interests were mainly centered onenergy, tourism, real estate, and agriculture. In July 2015,
then Prime Minister Najib Razak, who was the advisory board
chairman, was accused of siphoning roughly $700 million fromthe company to his personal accounts (Wright and Clark 2015).
The U.S. DOJ claimed that the in-crowd of the prime minister
led by businessman Jho Low channeled money from 1MDB tovarious shell companies. Some of the funds were used to pay

Indian Rupee Slide
The US dollar to Indian Rupee exchange rate of the USDINR
pair had surged 16.68% to a high of 74.48 in October 2018 fromjust 63.83 at the beginning of the year. The steep slide of the
Rupee can be attributed to a confluence of global economic
developments. Firstly, the U.S. Federal Reserve embarked
on a more aggressive monetary policy. It continued to raise
its benchmark interest from 1.5% to 1.75% in March 2018
after already raising it three times in the prior year. Robust
employment figures and GDP growth led the Fed to hike its rates
anew to 2% in June while signaling that it would do so for a totalof four times in all of 2018. The Fed’s actions sparked a massive
capital outflow from emerging markets as U.S. yields became
more attractive. Consequently, the Fed’s policy also lessened
the availability of the USD in the global system, which further
pushed its value and likewise placed downward pressure on
emerging currencies like the Rupee (Sengupta 2018).
Another event that contributed to the Rupee’s slide was the
28.3% surge in the global prices of crude as measured in theWest Texas Intermediate Crude (WTIC). WTIC jumped from just$60.07 per barrel at the start of 2018 to a high of $77.07 perbarrel in October. A decrease in daily production by OPEC andRussia plus the United States’s sanctions on Iran curbed the
supply of crude, which therefore translated into an increase in
its price. With India importing about 70% of its crude demand,the increase in its price only worsened India’s current accountdeficit. In addition, the “trade war” between the United States
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and China plus the latter’s currency devaluation further
triggered more emerging market-to-develop markets flows(Singh 2019). In the period where the USDINR rose by 16.68% to74.48 in October 2018 from 63.83 at the start of January, India’s
BSE SENSEX still managed to edge higher by 2.07% to 34,760.
This came despite a strong GDP growth of 7.1% to 8.2% in its
most recent quarters. It would have fared much higher if only
the Rupee had been stable.
Table 7. INRUSD versus BSE SENSEX (January 1, 2018, toJanuary 1, 2019)

Turkey Currency Crisis
Turkey’s currency crisis bore as a result of the country’s
appetite for foreign-denominated debt. In the recent years,
Turkey’s economy has been growing at a very robust clip, evenrising by a high of 7.5% for the entire 2017. Much of its growth,
however, was fueled by high levels of government expenditure
funded by foreign-denominated debt (Lee 2018). As of the endof 2017, Turkey’s gross external debt, which includes public andprivate loans, reached $453.20 billion. With roughly 40% of thesaid amount, or ~$180 billion, set to mature in 2018, plus thefact that the country only has a gross currency reserve of $85
billion, investors started to become wary of Turkey’s capability
to service its maturing debt (Holland 2018). To add fuel to thefire, the USD had likewise started to strengthen on the backof the U.S. Federal Reserve’s tightening policies. Additionally,
the Central Bank of the Republic of Turkey’s decision to keep
its interest rates steady at 8% even at the face of a doubledigit inflation rate added more pressure on the currency. It
was actually Turkish President Recep Tayyip Erdogan who
prevented the central bank from increasing their interest rates.
He most famously, or infamously, rather stated that “interest
rates are the mother and father of all evil” (Baccardax 2018).
The president of Turkey’s interference with the central bankran contrary to the notion that central banks should be left
independent. Furthermore, the economic sanction imposed by
the United States to Turkey by doubling its tariffs on Turkish
steel and aluminum placed more pressure on the country’s
already ballooning current account deficit (Harper 2018).
As expected, investors let go of Turkish financial assets
following the steep slide in the Turkish Lira. The above factors
resulted to a sharp depreciation of the Lira against the U.S.
dollar, with the USDTRY exchange rising to a high of 7.083 inAugust 2018 from only 3.791 at the end of December 2017. Thisof course cascaded into a 26.6% drop in the Borsa Istanbul 100
Index.

Table 8. TRYUSD versus BIST 100 Index (January 1, 2018,
to January 1, 2019)2015 Ghost Month: The Yuan Devaluation
The global financial markets, particularly the Asian emerging
markets, were visited early by the “hungry ghosts” as China
enacted a series of unexpected devaluations of its currency,
the Yuan, against the U.S. dollar. On August 11, 2015, exactly
three days before the beginning of the Chinese Ghost Month,
the Chinese central bank weakened the Renminbi by 1.86%
to 6.2298 per U.S. dollar (Mo 2015). A day later, a huge tidal
wave of selling continued to grip the financial markets as thePeople’s Bank of China pushed their currency down by another
1.62% against the greenback (Riley 2015). It did not actuallystop there, as the bank, for a third day in a row, softened it
further to 6.3975 per U.S. dollar (Inman 2015). The central
bank’s actions came following an 8.3% slide in exports in Julyfrom a year ago (Vaswani 2015). Many believe that the bank’s
moves were intended to shore up the country’s international
trade. According to the PBOC, however, the “adjustment in
the fixing rate formation mechanism was aimed at correcting
the disparity between the fixing rate and the spot rate of theChinese Yuan against the U.S. dollar” since the Yuan, being
pegged with the USD, had been steadily rising in value alongwith the greenback on the back of the planned monetary
tightening in the United States. Additionally, PBOC’s actions
were also meant as a bid to join the IMF’s Special Drawing Rights
basket (Chin 2015).
Nonetheless, shockwaves were sent not just in the currency
markets but likewise in the equities and commodities spaces
following the surprise move from the Chinese. With the USDCNY
exchange rate rising from 6.2093 in August 10, 2015, to a highof 6.4485 in the next three days, capital outflows from China
and even from the rest of Asia accelerated as a consequence. In
the following two weeks, the Shanghai Composite Index fell by
27.44% from 3,928 on August 10 to a low of 2,850.
Table 9. CNYUSD versus SSE Composite (August 10, 2015,
to February 10, 2017)

Literature

John Murphy, CMT, a living legend in the field of technicalanalysis, noted the concept of intermarket analysis in his bookTrading with Intermarket Analysis (2012). Here, he said thatintermarket analysis involves the examination of different
asset classes, bonds, stocks, commodities, and currencies. The
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IFTA JOURNAL

premise of intermarket analysis is that these asset classes are
related. This means that what happens in one market also has
an impact on the other asset classes. In his book Intermarket
Analysis: Profiting from Global Market Relationships, he notedthat “a falling currency usually gives a boost to commodity
prices quoted in that currency. This boost in commodity prices
reawakens inflation fears and puts pressure on central bankers
to raise interest rates, which has a negative impact on thestock market” (Murphy 2004). He actually first introduced
the concept of intermarket analysis in his book Intermarket
Analysis: Trading Strategies for the Global Stock, Bond,
Commodity, and Currency Markets. Here, he mentioned that a“rising dollar will eventually push inflation and interest rates
lower, which is bullish for (US) stocks and a falling dollar will
eventually push (US) stock prices lower because of the rise ininflation and interest rates. However, it is an oversimplificationthat a rising dollar is always bullish for (US) stocks and a falling
dollar is always bearish for (US) equities” (Murphy 1991). It is
the aim of this paper to see whether this premise also applies
in the emerging market economies, specifically in East Asia. Of
course, other dimensions, like the Asian economy’s exposure todollar denominated debt, exposure to hot capital flows, and theaccount of imports and exports in relation to their GDP, also play
important roles.
In a study done by Clive Granger, Bwo-nung Huang, and Chin
Wei Yang (1998), evidence was found of a bivariate causality
between stock prices and exchange rates. Using the Asianflu data of the 1990s, they found that in at least South Korea
and Thailand, exchange rates lead stock prices with a positive
correlation and that the “inclusion of exchange rate variations
is found to have improved the predictable portion of stock pricechanges of the eight Asian economies.” They also found, though,that at times, stock prices lead the changes in the exchange
rates. They further concluded that with the opening of Asian
economies to trade and global financing, capital movement
into and out of the Asian economies can be both beneficial anddetrimental, as in the case of the Asian financial crisis, wherea lot of capital flew out of the region. Abdul Qayyum and A.R.
Kemal (2006) also found that volatility in the exchange rate
has a spill over in the Pakistani stock market and vice versa.
In the same way, Dr. Alok Kumar Mishra, A.K. Swain, and D.K.
Malhotra (2007) found bi-directional volatility spillovers
between the Indian equity market and its exchange rate.
Meanwhile, Usman Umer, Guven Sevil, and Serap Kamisli (2015)
determined that the “comovements between exchange rates
and stock prices become stronger during the crises time, and the
direction of causality originates from stock prices to exchange
rates during the tranquil period; and from exchange rates tostock prices during crisis.”
Additionally, Valentina Bruno and Hyun Song Shin (2018)
found that “non-financial firms that exploit favorable global
financing conditions to issue bonds and build cash balances
are also those whose share price is most vulnerable to local
currency depreciation.” They further found that while currency
depreciation would be favorable for exporting firms, theimpact of the financial burden from their foreign-denominateddebt outweighs their supposed competitiveness. According
to Rudiger Dornbusch and Stanley Fischer (1980), changes in

2021 EDITION

the exchange rates impact the competitiveness of firms, as
it affect the firms’ cost of capital and earnings which in turnget reflected on its share price. They added that on a macrolevel, the impact of exchange rate movements on the equity
market would likewise be hinged on the “degree of openness ofthe economy and the degree of trade imbalance.” Christopher
Ma and G. Wenchi Kao (1990) found that on a macro level, anappreciation of the domestic currency negatively affects thedomestic stock market of an export-oriented economy, while a
depreciation of the local currency positively affects the equity
market of an import-oriented one.

Methodology
Association Analysis
For association analysis, Pearson Correlation Analysis wasused to find the correlation of 10-year historical prices of
emerging market indices against their respective currency(historical data of sample equity indices, their respective
currency exchange, and the US Dollar Index were sourced
from Investing.com and TradingEconomics.com). This type ofcorrelation analysis is used to determine the degree of linear
relationship between two quantitative variables that are
assumed normally distributed. Essentially, three questions will
be answered by the corresponding Pearson’s coefficient: (1) Is
there a linear relationship? (2) How strong is it? (3) What is the
direction of the linear relationship?
Sample Pearson correlation coefficient is computed as

r=

s XY
s X sY

where the numerator estimates the covariance between thetwo variables (say X and Y), and the denominator is the productbetween the two sample standard deviations.
The sample Pearson correlation coefficient, r, takes on values
from -1 to +1. A value very near -1 indicates an almost perfect
inverse (or indirect) linear relationship between two variables,
while a correlation coefficient very near +1 indicates almost
perfect direct linear relationship. On the other hand, a value
very near zero implies absence of linear relationship, but thisdoes not mean absence of another form of relationship (otherthan linear).
As a rule of thumb, the qualitative interpretation for thecorrelation coefficient is given below. If the coefficient isnegative, the variables are moving at opposite directions (asone decreases, the other one increases or vice versa) but if thecoefficient is positive, the variables are moving in the same
direction (as one decreases [or increases], the other one also
decreases [or increases]).
0:
No association
0.01 – 0.20:
Very weak association
0.21 – 0.40:
Weak association
0.41 – 0.60:
Moderate association
0.61 – 0.80:
Strong association
0.81 – 0.99:
Very strong association
1:
Perfect association

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The association tables note the year group used in theanalysis. Year group 2017–2018 used all observations withinthese years, as is the case for year group 2011–2018. Take notethat for Thailand and India, only up until 2011 is available, so
the year groups for these countries are the recent two years
and the recent eight years. For the other countries, year 2009 isavailable, so the year groups are 2017–2018 (recent two years)
and 2009–2018 (recent 10 years).
For each year group, the stock market of the of a sample Asianemerging market represented by its equity index is correlated
to the respective local currency exchange rate and USD Index(reciprocal) resulting to four coefficients stated under the 3rdcolumn. Qualitative interpretation for each coefficient is also
given under the 4th column. The last column lists the p-value
associated with the estimated correlation coefficient. p-values
help us decide for the statistical significance of the estimate.
From this point onward, we reject the null hypothesis (Ho) of
the appropriate test if the p-value is small (e.g., less than 0.10).
Otherwise, Ho is accepted. Rejecting the Ho means that theconclusion will support the alternative hypothesis (Ha). For
Pearson Correlation Analysis, the set of hypotheses are:
Ho: The two variables are not associated.
Ha: The two variables are associated.
Significant results (Ho is rejected) are noted by two asterisks(**) printed beside the p-value while nonsignificant results (Hois accepted) are noted by (ns).
Dependence of Stock Market: Selection of Lag
Order
Lag refers to the interval between present and past values
and is vital when dealing with time series data since past values
are usually correlated to present values. Before modelling, it isimportant to know which lag has the highest contribution to the
behavior of the time series data. The criteria that we will use todetermine the number of lags in the model are final prediction
error (FPE), Akaike’s information criterion (AIC), Schwarz’s
Bayesian information criterion (SBIC), and Hannan-Quinn
information criterion (HQIC). Smaller values for these criteria
mean better modelling capability. If the lag order is suggested
by most of these criteria, that lag order is used. If exactly two ofthese criteria suggested two different lag orders, the lowest orderis chosen due to issue of parsimony and ease of interpretation.
Parsimony refers to the ability of the model to explain thedependent variable given minimal number of predictors.
Dependence of Stock Market: Association Tests
For time-series modeling to be applicable, the time-series
must be stationary. Through time, the behavior of the variablemust not show any trend (increasing or decreasing) or
fluctuations that resemble a pattern. Such time-series must be
detrended or must be transformed into a stationary process.
Augmented Dickey-Fuller Tests can check whether the timeseries data is stationary or not by hypothesizing about theunit-root. If a variable has a unit-root, then the variable follows arandom walk or a behavior with an unpredictable pattern.
Ho: The time-series data has a unit-root. (Not stationary)
Ha: The time-series data is a stationary process.
PAGE 42 IFTA.ORG

In here, we tested for the stationarity of the levels (actual
value) and the first-differences (current minus previous). Firstdifference is a useful concept in time-series modelling since itshows the change between successive values.
Dependence of Stock Market: Regression Models of
Stock Market
Four linear regression models for change in the equity
index (dependent variable) are used. Two models used only theindividual exchange rate as predictor at varying year groups andanother two models used both the exchange rate and the USD
Index (reciprocal) as predictors, also at varying year groups.
Each table under a certain year group lists the coefficients(impact or effect) of the predictor to the change in the equity
index. Accompanying each coefficient is its p-value. Stated in thelast row is the coefficient of determinations R-squared whichtells us the percentage of variation in the dependent variable
that can be explained by the predictors. Higher R-squaredmeans better predictive ability of the model. If the coefficientand p-value are blank, it means that the corresponding
predictor is not included in that model. Take note that predictors
are dictated by the lag order selected in previous analyses.
For example, the THB/USD exchange rate in 2017–2018 haslag order 1, so the lag of the first-differenced THB/USD exchange
rate denoted by L1D is included in the model below. D refers to
the first-difference while L refers to the lag, and the number
attached to it is the order.
MSCI Emerging Market Index Versus MSCI EM
Currency Index Test
On top of the establishment of correlation and causation
between samples of individual emerging market currencies totheir respective equity markets (Baht vs. SET, Rupee vs. BSE
SENSEX, Lira vs. Borsa Istanbul, Ringgit vs. Bursa Malaysia,
Rupiah vs. JCI), and domestic currency along with the reciprocalof the US Dollar Index against the respective equity market forthe periods 2017–2018 and 2009–2018, the paper will also look
for the correlation and causation between the MSCI Emerging
Markets Currency Index and the MSCI Emerging Markets Index.
It is likewise important to subject the MSCI Emerging Markets
Index against its currency index because of the widespreaduse of both indices as a benchmark of global funds. As per
the MSCI.com website, the MSCI Emerging Market Index was
launched in 1988. Back then, it only consisted of 10 countries
representing less than 1% of global market capitalization. Since
then, the index has grown to include 24 economies, namely
Brazil, Chile, China, Colombia, Czech Republic, Egypt, Greece,
Hungary, India, Indonesia, Korea, Malaysia, Mexico, Pakistan,Peru, Philippines, Poland, Russia, Qatar, South Africa, Taiwan,
Thailand, Turkey, and the UAE. As of September 2018, China
has the heaviest weight, accounting for 30.99% of the index.
South Korea and Taiwan account for 14.88% and 12.29%,
respectively, in the index. The index captures both large andmid-cap representations across the said countries and also
covers approximately 85% of the free-float adjusted market
capitalization in each country.
Meanwhile, the MSCI EM Currency Index includes currencies
from 25 emerging market countries. Currencies from AsianIFTA JOURNAL

emerging markets account for a little over half of the weight of
the index at 50.2%. On the other end, currencies from emerging
Europe and the Middle East (EMEA) and emerging Latin America
comprise about a quarter each of the weight of the index. For
emerging Asia, the weights of the Chinese Yuan and the Korean
Won are 14.5% and 13%, respectively. Additionally, the Brazilian
Real has a 16.9% weight under the emerging Latin America.
Theoretically, strength in the EM Currency Index should
translate to a consequent rise in the equity space as measured in
the MSCI Emerging Market Index and vice versa. Using the daily
data from January 12, 2015, to April 1, 2019, we shall establish
a correlation and causation via regression analysis between
the two indices. Additionally, we will also look to quantify the
resulting decline measured in the following month, two andthree months in the equity index for every 2% break in thecurrency index. A “break” in the currency index is defined asa fall below an established swing low in its daily bar chart. The
same will also be measured for a 2% decline from an establishedswing high or peak. These technical breaks in the currency
index shall be deemed as “signals” for a possible drop in theemerging equity index.

Results
Results for Thailand

Association of Bangkok SET50 Index to THB/USD Exchange
Rate and USD Index (reciprocal)
Table 10. Correlation coefficients and P-values of SET50
to THBUSD and US Dollar Index
Association ofYear
Stock Market
Group to:
THB/USD2017– Exchange Rate2018
USD Index(reciprocal)
2011–
2018

Pearson’s
Correlation
Coefficient Interpretation
0.1393very weak &direct0.0541very weak &direct2021 EDITION

Dependence of Bangkok SET50 Index: Selection of Lag Order
Table 11. Selected Lag Orders and LR Test P-value for
SET50 to THBUSD and US Dollar Index
Year Group Variable

2017–2018

2011–2018

Selected
LR TestLag Order Suggested by: p-value

Bangkok
SET50 Index

1

all four<.001 **

THB/USD
Exchange Rate1

all four<.001 **USD Index(reciprocal)

1

HQIC, SBIC<.001 **

Bangkok
SET50 Index

1

HQIC, SBIC<.001 **

THB/USD
Exchange Rate

1

HQIC, SBIC<.001 **USD Index(reciprocal)

2

FPE, AIC,
HQIC

0.035 **For the most recent two years, all variables are somehow
dictated by the most recent previous value (lag of 1). The
behavior of the Bangkok SET50 Index and THB/USD exchange
rate is consistent even for the most recent eight years. However,the USD Index (reciprocal) is shown to be affected generally by
the two previous values.
Note that these lags will be considered in the succeeding
regression models.

Dependence of Bangkok SET50 Index: Stationary Tests
Table 12. Results of Dickey-Fuller Test for Bangkok
SET50 Index to THBUSD and USD Index

p-value
0.002**
0.232 ns

THB/USD
Exchange Rate

0.2400weak & direct<.001 **USD Index(reciprocal)

0.0322very weak &direct0.161 ns

The Bangkok SET50 Index had a very weak (r = 0.1393) direct
linear association to THB/USD Exchange rate for the years 2017
to 2018 but shared stronger (r = 0.2400) direct linear association
for the past eight years. Both were found to be statisticallysignificant. On the other hand, its association to the USD
Index (reciprocal), aside from being very weak, was also notsignificant. This implies that the Thailand stock market does notshare linear association with the reciprocal of the USD Index.
All associations were positive or direct, which means that when
THB/USD exchange rate or USD index (reciprocal) increases(or decreases), the Bangkok SET50 Index also increases (ordecreases).

Dickey-Fuller Test
p-value
Year
Group

2017–
2018

2011–
2018

At thelevel

FirstDifference Remark

Bangkok
SET50 Index

0.609 ns<.001 **Firstdifferenced datais stationary.

THB/USD
Exchange Rate

0.972 ns<.001 **Firstdifferenced datais stationary.

USD Index(reciprocal)

0.881 ns<.001 **Firstdifferenced datais stationary.

Bangkok
SET50 Index

0.854 ns<.001 **Firstdifferenced datais stationary.

THB/USD
Exchange Rate

0.816 ns<.001 **Firstdifferenced datais stationary.

USD Index(reciprocal)

0.883 ns<.001 **Firstdifferenced datais stationary.

Variable

For all variables in either year groups, first-differences follow
a stationary process. Since first-differences follow a stationary
process, then the models for all countries will predict the change
IFTA.ORG PAGE 43

IFTA JOURNAL

2021 EDITION

in the equity index and not the actual observed stock market. Allinterpretations should focus in the change in the equity index.
However, take note that even if interpretations are directed to“change” in the equity index, mathematical characteristics oftime series model can extend the interpretation at the levels(actual or non-differenced values). In other words, since all
variables across different timeframes that were tested showedthat their respective time series is a stationary process, thisessentially means that the change in the Bangkok SET50 Index
is somewhat predictable by the movements in the THB/USD
exchange rate and the USD Index (reciprocal) and is not due to“random walk.”

Table 14. Regression results for Bangkok SET50 Index to
THBUSD and USD Index
2017–2018

2011–2018

Predictor

Coefficient p-value

Coefficient p-value(L1D). Bangkok
SET50 Index

0.000070.999 ns

0.002(D). THB/USD
Exchange Rate

17433.39

0.013 ** 37893.83<.001 **

(L1D). THB/USD
Exchange Rate

9289.26

0.153 ns

4721.590.226 ns

(D). USD Index(reciprocal)-4478.54

0.702 ns-21724.50

0.004 **

Dependence of Bangkok SET50: Regression Models of Bangkok
SET50(L1D). USD Index(reciprocal)

1956.53

0.879 ns

-11770.09

0.058 **

Table 13. Regression results for Bangkok SET50 Index to
THBUSD(L2D). USD Index(reciprocal)---2001.86

0.727 ns

R-squared

2.62%

2017–2018

2011–2018

Predictor

Coefficient p-value

Coefficient p-value(L1D). Bangkok
SET50 Index-0.0003

0.996 ns

0.002(D).THB/USD
Exchange Rate

16455.6

0.010 ** 32884.91<.001 **

(L1D).THB/USD
Exchange Rate

9694.75

0.102 ns

0.626 ns

R-squared

2.59%

1671.32

0.949 ns

5.78%

Clearly, the THB/USD exchange rates are significantly
positively associated to the Bangkok SET50 Index (positivecoefficients) which implied that an increase in THB/USD
exchange rates also increases the Bangkok SET50 Index. For
2017–2018, one unit increase in the previous (lag 1) change(difference) in the Bangkok SET50 Index caused a decrease(since it is negative) of 0.0003 to the current change in the
Bangkok SET50 Index. Its effect is different for the recent eight
years for the previous change in the Bangkok SET50 Indexcaused an increase of 0.002 to the current change. However,these estimated effects were found to be not statisticallysignificant. Another non-significant predictor in the model isthe previous change in THB/USD exchange rate. For both year
groups, its effect is positive.
Significant effect was determined in the current change in the
THB/USD exchange rate. A unit increase in the current change
in the THB/USD exchange rate caused an increase of 16455.6 tothe current change in the Bangkok SET50 Index in 2017–2018
and a higher increase of 32884.91 in 2011–2018. This can provide
evidence that the Bangkok SET50 Index has linear dependence
to THB/USD exchange rate.
Only around 2.59% of changes in the Bangkok SET50 Index
can be explained by the three predictors during 2017–2018.
Extending it to the most recent eight years, the predictors can
explain 5.78% of variations in the Bangkok SET50 Index.

PAGE 44 IFTA.ORG

0.934 ns

6.58%

Incorporating the USD Index (reciprocal) into the model
switched the coefficient of previous change in the Bangkok
SET50 Index from negative to positive. With a small effect, thetest suggested that changes in the Bangkok SET50 Index are notsignificantly dependent to previous changes. With or without
the USD Index (reciprocal), the THB/USD exchange rate still has
significant direct effect to the Bangkok SET50 Index, especiallythe current (D) change in the THB/USD exchange rate.
The models above suggest that the USD Index (reciprocal)
appears to have long-term effect to Thailand stock market. The
USD Index (reciprocal) is not significant in the 2017–2018 year
group but is significant in the 2011√2018 year group up to thefirst lag difference (L1D). The model of the longer year group alsosuggests that the USD Index (reciprocal) has indirect effect tothe Bangkok SET50 Index, as seen in its negative coefficients.
Specifically, an increase in the difference between successive
USD Index (reciprocal) values causes a decrease of about21724.50 units in the change in the Bangkok SET50 Index.
Results for Indonesia

Association of the Jakarta Stock Exchange Composite Index to
IDR/USD Exchange Rate and USD Index (reciprocal)
Table 15. Correlation coefficients and P-values of Jakarta
Stock Exchange to IDRUSD and US Dollar Index
Year
Group
2017–
2018

2009–
2018

Association of
Stock Market
to:Pearson’s
Correlation
Coefficient Interpretation p-value

IDR/USD
Exchange Rate

0.3312

weak & direct<.001 **USD Index(reciprocal)

0.1060very weak &direct0.020**

IDR/USD
Exchange Rate

0.3491

weak & direct<.001 **USD Index(reciprocal)

0.0321very weak &direct0.114 ns

IFTA JOURNAL

The Jakarta Stock Exchange Composite Index (JSX) had aweak (r = 0.3312) direct linear association to IDR/USD Exchange
rate for the years 2017 to 2018 but shared stronger (r = 0.3491)
direct linear association for the past 10 years. Both were foundto be statistically significant. Its association to USD Index(reciprocal), on the other hand, is very weak but direct andsignificant for 2017–2018. This implies that the Jakarta Stock
Exchange Composite Index does share linear association withthe reciprocal of the USD Index (reciprocal) in the short run. All
associations were positive or direct, which means that when
IDR/USD exchange rate or USD index (reciprocal) increases (ordecreases), the Jakarta Stock Exchange Composite Index also
increases (or decreases).

Dependence of Jakarta Stock Exchange Composite Index:
Selection of Lag Order

Dependence of Jakarta Stock Exchange Composite Index:
Stationary Tests
Table 17. Results of Dickey-Fuller Test for Jakarta Stock
Exchange to IDRUSD and USD Index
Dickey-Fuller Test
p-value
Year
Group

2017–
2018

2009–
2018

Variable

Selected
Lag
Order

Suggested by:

2017–
2018

LR Testp-value

Jakarta Stock
Exchange
Composite Index

1

all four<.001 **

IDR/USD
Exchange Rate2

all four

0.002 **USD Index(reciprocal)

1

all four<.001 **

Jakarta Stock
Exchange
Composite Index

4

all four<.001 **

IDR/USD
Exchange Rate

1

HQIC, SBIC<.001 **USD Index(reciprocal)

1

all four<.001 **For the most recent two years, the Jakarta Stock Exchange
Composite Index and the USD Index (reciprocal) are somehow
dictated by the most recent previous value (lag of 1), while the
IDR/USD exchange rate is shown to be affected by the two
previous values. For the last eight years, both the IDR/UDS
and the USD Index (reciprocal) are dictated by the most recent
previous value, while the Jakarta Stock Exchange Composite
Index is shown to be affected generally by the four previous
values.

VariableAt thelevel

Jakarta Stock
Exchange
0.667 ns
Composite Index

Table 16. Selected lag orders and LR Test p-value forJakarta Stock Exchange to IDRUSD and US Dollar Index
Year
Group

2021 EDITION

IDR/USD
Exchange Rate

0.709

ns

USD Index(reciprocal)

0.926

ns

Jakarta Stock
Exchange
0.961 ns
Composite Index
2009–
2018

IDR/USD
Exchange Rate

USD Index(reciprocal)

0.616

ns

0.484 ns

FirstDifference

Remark<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

All variables across different timeframes that were tested
showed that their respective time series is a stationary process.
This essentially means that the change in the Jakarta Stock
Exchange Composite Index is somewhat predictable by the
movements in the IDR/USD exchange rate and the USD Index(reciprocal) and is not due to “random walk.”

IFTA.ORG PAGE 45

IFTA JOURNAL

2021 EDITION

Dependence of Jakarta Stock Exchange: Regression Models of
Jakarta Stock Exchange
Table 18. Regression result for Indonesia (IDRUSD versus
JSX)
2017–2018

2009–2018

Predictor

Coefficientp-value Coefficientp-value(L1D). Jakarta
Composite Index

-0.03

0.615

0.01

0.747

(L2D). Jakarta
Composite Index

-

-

-0.02

0.508

(L3D). Jakarta
Composite Index

-

-

-0.10

<.001 **

(L4D). Jakarta
Composite Index-(D). IDR/USD
Exchange Rate

7.96 x 107(L1D). IDR/USD
Exchange Rate

-6570148.00 0.634(L2D). IDR/USD
Exchange Rate

1.81 x 107

R-squared

11.71%

ns

-

-0.04

<.001
**

4.39 x 107
ns37248360.111 ns

ns

ns

0.072 **<.001 **
0.212

ns

13.52%

Significant effect was determined in the current change
in the IDR/USD exchange rate. A unit increase in the current
change in the IDR/USD exchange rate caused an increase of
7.96x107 to the current change in the Jakarta Stock Exchange
Composite Index in 2017–2018 and an increase of 4.39 x 107 in
2009–2018. This can provide evidence that the Jakarta Stock
Exchange Composite Index has linear dependence to the IDR/
USD exchange rate.
Only around 11.71% of changes in the Jakarta Stock Exchange
Composite Index can be explained by the three predictors
during 2017–2018. Extending it to the most recent 10 years, thepredictors can explain 13.52% of variations in the Jakarta Stock
Exchange Composite Index.

PAGE 46 IFTA.ORG

Table 19. Regression result for Indonesia (IDRUSD and
USD Index reciprocal versus JSX)
2017–2018

2009–2018

Predictor

Coefficient p-value

Coefficientp-value(L1D). Jakarta
Composite Index-0.04

0.515 ns

0.01

0.732 ns

(L2D). Jakarta
Composite Index

-

-

-0.02

0.500 ns

(L3D). Jakarta
Composite Index-0.10<.001 **

(L4D). Jakarta
Composite Index-0.04

0.074 **

(D). IDR/USD
Exchange Rate

7.83 x 107<.001 ** 4.40 x 107<.001 **

(L1D). IDR/USD
Exchange Rate

-1.41 x 107

0.307 ns

3857481.00

0.214 ns

(L2D). IDR/USD
Exchange Rate

1.53 x 107

0.199 ns--

(D). USD Index(reciprocal)

95492.15

0.108 ns

-5646.01

0.762 ns

(L1D). USD Index(reciprocal)

79895.24

0.174 ns

3053.63

0.831 ns

R-squared

12.62%

13.52%

Incorporating the USD Index into the model showed thatsignificant effect remained to be determined in the current
change in the IDR/USD exchange rate. A unit increase in current
change in the IDR/USD exchange rate caused an increase of
7.83 x 107 to current change in the Jakarta Stock Exchange
Composite Index in 2017–2018 and an increase of 4.40 x 107 in
2009–2018. This can likewise provide evidence that the Jakarta
Stock Exchange Composite Index has linear dependence to the
IDR/USD exchange rate.
Only around 12.62% of changes in the Jakarta Stock Exchange
Composite Index can be explained by the above predictors
during 2017–2018. Extending it to the most recent 10 years, thepredictors can explain 13.52% of variations in the Jakarta Stock
Exchange Composite Index.

IFTA JOURNAL

Results for Malaysia

Association of Bursa Malaysia KLCI Index to MYR/USD
Exchange Rate and USD Index (reciprocal)
Table 20. Correlation coefficients and p-values for
Malaysia
Association ofYear
Stock Market
Group to:
2017–
2018

Interpretation p-value

Dependence of Bursa Malaysia KLCI Index: Stationary Tests

MYR/USD
Exchange Rate

0.2349weak & direct<.001 **

Table 22. Result of Dicker-Fuller Test on Malaysia

USD Index(reciprocal)

0.0213very weak &direct0.639 ns

0.3663

weak & direct<.001 **

0.0213very weak &direct0.291 ns

The Bursa Malaysia KLCI Index had a weak (r = 0.2349)
direct linear association to the MYR/USD Exchange rate forthe years 2017 to 2018 but shared stronger (r = 0.3663) direct
linear association for the past 10 years. Both were found to bestatistically significant. On the other hand, its associations
to the USD Index, aside from being very weak, were also notsignificant. This implies that the Bursa Malaysia KLCI Index
does not share linear association with USD Index (reciprocal).
All associations were positive or direct, which means that when
the MYR/USD exchange rate or USD index (reciprocal) increases(or decreases), the Bursa Malaysia KLCI Index also increases (ordecreases).

Dependence of Bursa Malaysia KLCI Index: Selection of Lag
Order
Table 21. Selected lag order and LR Test p-value for
Malaysia

2017–
2018

2009–
2018

For the most recent two years, the Bursa Malaysia KLCI
Index and the USD Index (reciprocal) are somehow dictatedby the most recent previous value (lag of 1), while the MYR/
USD exchange rate is shown to be affected by the two previous
values. For the last 10 years, both the Bursa Malaysia KLCI Index
and MYR/USD exchange rate are dictated by the two mostrecent values, while the USD Index (reciprocal) is shown to beaffected by the most recent previous value.

Pearson’s
Correlation
Coefficient

MYR/USD
2009– Exchange Rate2018
USD Index(reciprocal)

Year
Group

2021 EDITION

Selected
Lag Order

Suggested by:

LR Testp-value

Bursa Malaysia
KLCI Index

1

HQIC, SBIC<.001 **

MYR/USD
Exchange Rate2

all four<.001 **USD Index(reciprocal)

1

all four<.001 **

Bursa Malaysia
KLCI Index

2

all four<.001 **

MYR/USD
Exchange Rate

2

HQIC, SBIC<.001 **USD Index(reciprocal)

1

all four<.001 **

Variable

Dickey-Fuller Test
p-value
Year
Group

2017–
2018

VariableAt thelevel

Bursa Malaysia
KLCI Index

0.417

MYR/USD
Exchange Rate

0.989

USD Index(reciprocal)

Bursa Malaysia
KLCI Index

2009– MYR/USD
2018
Exchange Rate

USD Index(reciprocal)

FirstDifference Remark

ns

ns

0.926 ns

0.993 ns

0.632 ns

0.490

ns

<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

<.001 **Firstdifferenced
data isstationary.

All variables across different timeframes that were tested
showed that their respective time series is a stationary process.
This essentially means that the change in Bursa Malaysia KLCI
Index is somewhat predictable by the movements in the MYR/
USD exchange rate and is not due to “random walk.”

IFTA.ORG PAGE 47

2021 EDITIONIFTA JOURNAL

Dependence of Bursa Malaysia KLCI Index: Regression Models
of Bursa Malaysia KLCI Index
Table 23. Regression result for Malaysia (MYRUSD versus
Bursa Malaysia)
2017–2018

2009–2018

Incorporating the USD Index (reciprocal) into the model
showed that significant effect remained to be determined in thecurrent change in MYR/USD exchange rate. A unit increase incurrent change in MYR/USD exchange rate caused an increaseof 4778.66 to the current change in the Bursa Malaysia KLCI
Index in 2017-2018 and an increase of 2655.89 in 2009-2018. Thiscan likewise provide evidence that Bursa Malaysia KLCI Index
has linear dependence to MYR/USD exchange rate.
Only around 6.13% of changes in Bursa Malaysia KLCI Index
can be explained by the above predictors during 2017–2018.
Extending it to the most recent 10 years, the predictors can
explain 14.14% of the variations in the Bursa Malaysia KLCI
Index.

Predictor

Coefficient p-value

Coefficient p-value(L1D). Bursa
Malaysia KLCI
Index

0.06

0.408 ns

0.06

0.050 **

(L2D). Bursa
Malaysia KLCI
Index

-

-

-0.02

0.544 ns

(D). MYR/USD
Exchange Rate

4639.47<.001 **

2577.79<.001 **

(L1D). MYR/USD
Exchange Rate

-357.71

0.696 ns-97.750.548 ns

Association of the BSE SENSEX to INR/USD Exchange Rate and
USD Index (reciprocal)(L2D). MYR/USD-45.97
Exchange Rate

0.960 ns336.320.031 **

Table 25. Correlation coefficients and p-values for India

R-squared

5.82%

13.86%

Year
Group

Significant effect was determined in the current change in
MYR/USD exchange rate. A unit increase in current change
in MYR/USD exchange rate caused an increase of 4639.47 tocurrent change in the Bursa Malaysia KLCI Index in 2017–2018
and an increase of 2577.79 in 2009–2018. This can provide
evidence that Bursa Malaysia KLCI Index has linear dependence
to MYR/USD exchange rate.
Around 5.82% only of changes in Bursa Malaysia KLCI Index
can be explained by the three predictors during 2017–2018.
Extending it to the most recent 10 years, the predictors can
explain 13.86% of variations in the Bursa Malaysia KLCI Index.
Table 24. Regression result for Malaysia (MYRUSD and
USD Index reciprocal versus Bursa Malaysia)
2017–2018
Predictor

Results for India

2009–2018

Coefficient p-value Coefficient p-value(L1D). Bursa
0.06
Malaysia KLCI Index

0.378 ns

0.06

0.049 **

(L2D). Bursa
Malaysia KLCI Index

-

-0.02

0.551 ns

(D). MYR/USD
Exchange Rate

4778.66<.001 ** 2655.89<.001 **

(L1D). MYR/USD
Exchange Rate

-241.480.814 ns

29.00

0.866 ns

(L2D). MYR/USD
Exchange Rate

-472.05

0.615 ns334.110.040 **

(D). USD Index(reciprocal)-8183.29

0.480 ns

-9172.82

0.006 **

(L1D). USD Index(reciprocal)

11455.66

0.337 ns-685.82

0.832 ns

R-squared

6.13%

14.14%

PAGE 48 IFTA.ORG

2017–
2018

2011–
2018

Association of
Stock Market
to:Pearson’s
Correlation
CoefficientInterpretation p-value

INR/USD
0.3489Exchange Rate

weak & direct<.001 **USD Index(reciprocal)

0.0675very weak &direct0.134 ns

INR/USD
0.3832
Exchange Rate

weak & direct<.001 **USD Index(reciprocal)

very weak &direct0.985 ns

0.0004The BSE SENSEX had a weak (r = 0.3489) direct linear
association to INR/USD Exchange rate for the years 2017 to 2018
but shared stronger (r = 0.3832) direct linear association for the
past 10 years. Both were found to be statistically significant. On
the other hand, its association to USD Index (reciprocal) in both
timeframes are both very weak and not significant. This implies
that the BSE SENSEX does not share a linear association with the
USD Index (reciprocal). All associations were positive or direct,
which means that when the INR/USD exchange rate increases(or decreases), the BSE SENSEX also increases (or decreases).

IFTA JOURNAL

2021 EDITION

Dependence of BSE SENSEX: Selection of Lag Order

Dependence of BSE SENSEX: Regression Models of BSE SENSEX

Table 26. Selected lag order and LR Test p-value for India

Table 28. Regression result for India (INRUSD versus BSE
SENSEX)

Year
Group

2017–
2018

2011–
2018

Variable

Selected
Lag Order

Suggested by:

LR Testp-value

BSE SENSEX

1

all four<.001 **

INR/USD
Exchange Rate

1

USD Index(reciprocal)

1

all four<.001 **

BSE SENSEX

2

all four

0.001 **

INR/USD
Exchange Rate

3

FPE, AIC, HQIC

0.001 **

FPE, AIC, HQIC

0.020
**

USD Index(reciprocal)

2

all four<.001 **For the most recent two years, the BSE SENSEX, INRUSD
exchange rate, and USD Index (reciprocal) are somehow dictatedby the most recent previous value (lag of 1). For the last eight
years, both the BSE SENSEX and the USD Index are dictated by
the two most recent values, while the INR/USD exchange rate isshown to be affected by the three most recent values.

Coefficientp-value

Coefficientp-value(L1D). BSE SENSEX

0.04

0.497

0.05

0.055 **

(L2D). BSE SENSEX

-

-

-0.03

0.249 ns

(D). INR/USD
Exchange Rate

1573548.00 <.001 **

1055073.00 <.001 **(L1D). INR/USD
Exchange Rate

-9195.62

0.965 ns

-8908.05

0.892 ns

(L2D). INR/USD
Exchange Rate--

70482.62

0.264 ns

(L3D). INR/USD
Exchange Rate--

132600.10

0.024 **

R-squared

12.33%

ns

15.38%<.001 **First-differenced
data is stationary.

Table 29. Regression result for India (INRUSD and USD
Index reciprocal versus BSE SENSEX)<.001 **First-differenced
data is stationary.

2017–2018

Dickey-Fuller Test
p-value

2011–
2018

Predictor

RemarkTable 27. Result of Dicker-Fuller Test on India

2017–
2018

2011-2018

Significant effect was determined in the current change in
INR/USD exchange rate. A unit increase in current change in
the INR/USD exchange rate caused an increase of 1573548.00
to current change in the BSE SENSEX in 2017-2018 and anincrease of 1055073.00 in 2009-2018. This can provide
evidence that the BSE SENSEX has linear dependence to the
TRY/USD exchange rate.
Only around 12.33% of changes in BSE SENSEX can be
explained by the three predictors during 2017–2018. Extending
it to the most recent 10 years, the predictors can explain 15.38%
of variations in the BSE SENSEX.

Dependence of BSE SENSEX: Stationary Tests

Year
Group

2017-2018

VariableAt thelevel

FirstDifference

BSE SENSEX

0.974 ns

INR/USD
Exchange
Rate

0.726 ns

2011–2018

Predictor

Coefficientp-value

Coefficient p-value
0.05

0.056 **

-0.03

0.218 ns

USD Index(reciprocal)

0.944 ns<.001 **First-differenced
data is stationary.

(L1D). BSE SENSEX

0.04

0.492

(L2D). BSE SENSEX--

BSE SENSEX

0.582 ns<.001 **First-differenced
data is stationary.

(D). INR/USD
Exchange Rate

1599073.00 <.001 ** 1120737.00 <.001 **INR/USD
Exchange
Rate

0.987 ns<.001 **First-differenced
data is stationary.

(L1D). INR/USD
Exchange Rate

34645.80

0.872 ns

26361.22

0.711 ns

USD Index(reciprocal)

0.811 ns<.001 **First-differenced
data is stationary.

(L2D). INR/USD
Exchange Rate--

84261.07

0.208 ns

(L3D). INR/USD
Exchange Rate--

137791.70

0.020 **

(D). USD Index(reciprocal)-125281.40

0.602 ns

-445374.10 <.001 **(L1D). USD Index(reciprocal)-168917.50

0.507 ns

-94560.75

0.364 ns

(L2D). USD Index(reciprocal)---57774.91

0.541 ns

R-squared

12.44%All variables across different timeframes that were tested
showed that their respective time series is a stationary process.
This essentially means that the change in the BSE SENSEX
is somewhat predictable by the movements in the INR/USD
exchange rate and is not due to “random walk.”

IFTA.ORG PAGE 49

ns

16.42%IFTA JOURNAL

2021 EDITION

Incorporating the USD Index (reciprocal) into the model
showed that significant effect remained to be determined in
the current change in INR/USD exchange rate. A unit increasein the current change in the INR/USD exchange rate caused anincrease of 1599073.00 to the current change in the BSE SENSEX
in 2017–2018 and an increase of 1120737.00 in 2011–2018.
This can likewise provide evidence that BSE SENSEX has linear
dependence to the INR/USD exchange rate.
Only around 12.44% of changes in BSE SENSEX can be
explained by the above predictors during 2017–2018. Extending
it to the most recent eight years, the predictors can explain
16.42% of variations in the BSE SENSEX.

Dependence of Borsa Istanbul 100 Index: Selection of Lag Order
Table 31. Selected lag order and LR Test p-value for
TurkeySelected
Lag
Order

Suggested by:

LR Testp-value

Borsa Istanbul
100 Index

1

all four<.001 **

TRY/USD
Exchange Rate

4

FPE, AIC, HQIC

0.020
**

USD Index(reciprocal)

1

all four<.001 **

Borsa Istanbul
100 Index

1

all four<.001 **

2009– TRY/USD
2018
Exchange Rate4

all four

0.001 **

1

all four<.001 **

Year
Group

2017–
2018

Results for Turkey

Association of Borsa Istanbul 100 Index to TRY/USD Exchange
Rate and USD Index (reciprocal)
Table 30. Correlation coefficients and p-values for
Turkey
Year
Group
2017–
2018

2009–
2018

Variable

USD Index(reciprocal)

Association of
Stock Market
to:Pearson’s
Correlation
Coefficient Interpretation p-value

TRY/USD
Exchange Rate

0.3556

weak & direct<.001 **USD Index(reciprocal)

0.1675very weak &direct<.001 **

TRY/USD
Exchange Rate

0.3624

weak & direct<.001 **For the most recent two years, the Borsa Istanbul 100 Index
and the USD Index (reciprocal) are somehow dictated by
the most recent previous value (lag of 1), while the TRY/USD
exchange rate is shown to be affected by the four previous
values. For the last 10 years, both the Borsa Istanbul 100 Index
and the USD Index are dictated by the most recent value, while
the TRY/USD exchange rate is shown to be affected by the fourmost recent values.

USD Index(reciprocal)

0.1192very weak &direct<.001 **

Dependence of Borsa Istanbul 100 Index: Stationary Tests
Table 32. Result of Dicker-Fuller Test on Turkey

The Borsa Istanbul 100 Index had a weak (r = 0.3556) direct
linear association to the TRY/USD Exchange rate for theyears 2017 to 2018 but shared stronger (r = 0.3624) direct
linear association for the past 10 years. Both were found to bestatistically significant. On the other hand, its association to
the USD Index (reciprocal) in both timeframes is very weak but
nonetheless significant. This implies that the Borsa Istanbul 100
Index does share to some degree a linear association with the
USD Index (reciprocal). All associations were positive or direct,
which means that when the TRY/USD exchange rate or the USD
index (reciprocal) increases (or decreases), the Borsa Istanbul
100 Index also increases (or decreases).

Year
Variable
Group

2017–
2018

RemarkAt thelevel

FirstDifference

Borsa
Istanbul 100
Index

0.877 ns<.001 **First-differenced
data is stationary.

TRY/USD
Exchange
Rate

0.652 ns<.001 **First-differenced
data is stationary.

USD Index(reciprocal)

0.939 ns<.001 **First-differenced
data is stationary.

Borsa
Istanbul 100
Index

0.903 ns<.001 **First-differenced
data is stationary.

0.601 ns<.001 **First-differenced
data is stationary.

0.488 ns<.001 **First-differenced
data is stationary.

2009– TRY/USD
2018
Exchange
Rate
USD Index(reciprocal)

PAGE 50 IFTA.ORG

Dickey-Fuller Test
p-valueIFTA JOURNAL

All variables across different timeframes that were tested
showed that their respective time series is a stationary process.
This essentially means that the change in Borsa Istanbul 100
Index is somewhat predictable by the movements in the TRY/
USD exchange rate and is not due to “random walk.”

2021 EDITION

Table 34. Regression result for Turkey (TRYUSD and USD
Index reciprocal versus Borsa Istanbul)
2017–2018
Predictor

Coefficient p-value

2009–2018
Coefficientp-value-0.08

0.005 **

(L1D). Borsa-0.004Istanbul 100 Index

0.946 ns

(D). TRY/USD
Exchange Rate

148116.60<.001 ** 108792.00<.001 **

Table 33. Regression result for Turkey (TRYUSD versus
Borsa Istanbul)(L1D). TRY/USD
Exchange Rate

-20644.26

0.468 ns

19100.34

0.006 **

2017–2018

2009–2018

4007.380.844 ns

-4058.99

0.422 ns

Predictor

Coefficient p-value

Coefficient p-value(L2D). TRY/USD
Exchange Rate

(L1D). Borsa
Istanbul 100 Index

0.01

0.903 ns

-0.08

0.004 **

(L3D). TRY/USD
Exchange Rate

-23985.19

0.169 ns-805.700.865 ns

(D). TRY/USD
Exchange Rate

157071.60<.001 ** 101407.30<.001 **

(L4D). TRY/USD
Exchange Rate

11836.40

0.507 ns-5431.19

0.270 ns

(L1D). TRY/USD
Exchange Rate

-20281.53

0.450 ns

17330.87

0.003 **

(D). USD Index(reciprocal)

1969789.00 0.148 ns-1053689.00 0.010 **(L2D). TRY/USD
Exchange Rate

3030.97

0.882 ns

-4339.60

0.391 ns

(L1D). USD Index(reciprocal)

897357.40-272519.20(L3D). TRY/USD
Exchange Rate

R-squared

13.60%-24735.01

0.156 ns

-1031.25

0.827 ns

(L4D). TRY/USD
Exchange Rate

11432.70

0.522 ns

-5184.45

0.290 ns

R-squared

13.17%

Dependence of Borsa Istanbul 100 Index: Regression Models of
Borsa Istanbul 100 Index

13.90%

Significant effect was determined in the current change in
the TRY/USD exchange rate. A unit increase in current change
in TRY/USD exchange rate caused an increase of 157071.60
to the current change in the Borsa Istanbul 100 Index in2017–2018 and an increase of 101407.30 in 2009–2018. This canprovide evidence that the Borsa Istanbul 100 Index has linear
dependence to the TRY/USD exchange rate.
Only around 13.17% of changes in the Borsa Istanbul 100 Index
can be explained by the three predictors during 2017–2018.
Extending it to the most recent 10 years, the predictors can
explain 13.90% of variations in the Borsa Istanbul 100 Index.

0.515 ns

0.472 ns

14.18%

Incorporating the USD Index (reciprocal) into the model
showed that significant effect remained to be determined in thecurrent change in the TRY/USD exchange rate. A unit increasein the current change in TRY/USD exchange rate caused anincrease of 148116.60 to the current change in the Borsa
Istanbul 100 Index in 2017–2018 and an increase of 108792.00
in 2009–2018. This can likewise provide evidence that the
Borsa Istanbul 100 Index has linear dependence to the TRY/USD
exchange rate.
Only around 13.60% of changes in the Borsa Istanbul 100
Index can be explained by the above predictors during 2017–2018. Extending it to the most recent 10 years, the predictors
can explain 14.18% of variations in the Borsa Istanbul 100 Index.
Results for MSCI Emerging Markets

Correlation of MSCI Emerging Markets Index to MSCI EM
Currency Index
Table 35. Correlation coefficients and p-values for MSCIEM Index vs. MSCI EM Currency Index

Year Group

Pearson’s
Correlation
CoefficientInterpretation

p-value

2017–2018

0.9418very strong & direct<.001 **

2015–2019

0.9673very strong & direct<.001 **The MSCI Emerging Markets Index had a very strong (r =0.9418) direct linear association to the MSCI EM Currency Index
rate for the years 2017 to 2018 and shared an even stronger (r= 0.9673) direct linear association for the past five years. Bothwere found to be statistically significant. This implies that the
MSCI Emerging Markets Index shares to a huge degree a linear
association with the MSCI EM Currency Index. All associations
IFTA.ORG PAGE 51

IFTA JOURNAL

2021 EDITION

were positive or direct, which means that when MSCI EM
Currency Index increases (or decreases), the MSCI Emerging
Market Index also increases (or decreases).

Tally of “Signals” generated from the MSCI EM Currency Index
Figure 1. Chart of MSCI EM Index versus MSCI EM
Currency Index (Jan. 12, 2015, to April 1, 2019)

Dependence of MSCI Emerging Markets Index to MSCI EM
Currency Index
Table 36. Regression result for MSCI EM Index versus
MSCI EM Currency Index
2017–2018

2015–2019

Predictor

Coefficient p-value Coefficient p-value(L1D). MSCIEmerging Market
Index

0.11

(L2D). MSCIEmerging Market
Index

0.05

0.101 ns

-0.02

0.542 ns

(D). MSCI EM
Currency Index

1.34<.001 ** 1.29<.001 **

(L1D). MSCI EM
Currency Index-0.13

0.136 ns

-0.08

0.161 ns

(L2D). MSCI EM
Currency Index

-

-

0.09

0.114 ns

(L3D). MSCI EM
Currency Index

-

-

0.03

0.452 ns

R-squared

46.91%

0.011 ** 0.10

0.002 **

53.74%

Significant effect was determined in the current change in the
MSCI EM Currency Index. A unit increase in the current change
in the MSCI EM Currency Index caused an increase of 1.34 to thecurrent change in the MSCI Emerging Markets Index in 2017–2018 and an increase of 1.29 in 2015–2019. This can provide
notable evidence that MSCI Emerging Markets Index has linear
dependence to the MSCI EM Currency Index.
Around 46.91% of the changes in the MSCI Emerging Markets
Index can be explained by the two predictors during 2017–2018.
Extending it to the most recent five years, the predictors can
explain 53.74% of variations in the MSCI Emerging Markets
Index.

Using the daily data from January 12, 2015, to April 1, 2019,it was found that there were 17 instances where the MSCI EM
Currency Index declined by 2% from an established swing high.
With approximately 20 trading days of duration, the said 2%
or so drop in MSCI EM Currency Index translated to an average
4.64% decline in the EM equity index in the same period. The
MSCI EM Index continued to decline by another 1.10% on averagein the succeeding month. Two months post the 2% dip in thecurrency basket, the EM equity index has remained weak,
dropping by another 1.34% on average. Three months post the
2% dip in the currency basket, the EM equity index was down by
an average of 3.67% on top of its initial decline.
There were also 14 instances where the MSCI EM Currency
Index broke down by 2% from an established swing low. With
approximately 44 trading days in duration, the said 2% or so slip
in the EM currency basket translated to an average 6.69% decline
in the EM equity index in the same period. The EM equity index
continued to falter by another 3.22% on average in the succeeding
month. Two months post the 2% dip from the swing low of thecurrency basket, the EM equity index has mildly recovered but
remained down by another 1.53% on average. Three months postthe 2% dip in the currency basket, the EM equity index rallied by
2.92% on average from its low on the 44th day.
In the 31 instances where the MSCI EM Currency Index
declined by 2% or so from both swing high and swing low withan average duration of 30 trading days, the MSCI Emerging
Markets Index consequently declined by an average of 5.22% in
the same period. The EM equity index continued to weaken byanother 2.51% on average one month after. In the two months
after the 2% dip in the currency basket, the EM equity index
recovered a bit but remained down by another 1.78%. In thethree months after the 2% dip in the currency basket, the EM
equity index continued to recover but nonetheless remained
down by 1.02% on average on top of its initial slip.

Table 37. Percentage changes following swing high declines and swing low breaks (MSCI)

2% Slip in EM Currency Index # Signals Duration in Days
Swing High Decline
11
19.82
Swing Low Break
8
44.25
Total
19
30.11
PAGE 52 IFTA.ORG% Decline in EM Equities
During 1 Month After 2 Months After 3 Months After-4.64%-1.10%-1.34%-3.67%-6.69%-3.22%-1.53%
2.92%-5.22%-2.51%-1.78%-1.02%IFTA JOURNAL

Figure 2. Graph of % changes in EM equities following
swing high declines and swing low breaks

2021 EDITION

explain as much as 53% of the changes in the latter. Even if thechanges in the currency only explains 12–14% of the changes in
its respective equities covering 8 to 10 years, the author deems
that such is already heavy enough to warrant a closer analysisof the movements in the exchange rate when actively managing
a domestic equity portfolio. Adverse domestic currency
movements can indeed be used as an indicator of a potential
earthquake in the corresponding equity space both in the one tothree months ahead and in the long run.

References

Discussion

This paper provided an argument and ample evidence tosupport the use of the changes in the Asian emerging market
currencies as signals for forecasting consequent changes intheir respective equity markets. While from the correlation
analysis that was used covering the periods of the last one year
and the last 8 to 10 years showed a weak linear relationshipbetween the domestic Asian currencies against their respective
equity indices, their association nonetheless remained
statistically significant. The analysis and the results of the“signals” that were generated actually supports the findings ofUsman Umer et al. in that the “comovements between exchange
rates and stock prices become stronger during the crises time”
or when there was an increased volatility in the exchange
rate in the short run. It was determined that the sensitivity orbeta of equities from the movement in the exchange rate canbe as much as 2.6 times. Furthermore, it was also seen thatthe resulting weakness in equities have not just persisted but
even deepened in the succeeding one to three months. One
may therefore use a “breakdown” in the domestic currency
as a signal to hedge against a possible drop of as much as 10%
in the respective domestic equities in the next two and halfmonths. Additionally, it was determined that the associationand proportion of the variance (R squared) between domestic
currencies and equity markets notably improve with longer
historical data of 8 to 10 years relative to a year’s data alone.

Conclusion

For the periods covering the last 8 to 10 years, the stationary
tests that were done all showed that the sample Asian equity
indices were to a certain degree predictable by the movementsin their domestic currency and, more importantly, that their
movements were not due to “random walk.” It was also foundthat significant effect was determined in the current change inthe domestic currencies vis a vis their respective equity index
using regression analysis. While the changes in the currency
only explain as low as 2.58% in the past year that was tested and5.78% in the past eight years the changes in the equity index
as in the case of Thailand, the dependence can be found muchhigher in Indonesia, Malaysia, India, and Turkey. Meanwhile,the correlation and regression analyses done in the MSCI EM
Currency Index against the MSCI EM Index actually found a verystrong direct correlation of at least 94%, while the former could

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IFTA JOURNAL

2021 EDITION

Coefficient Moving Average

Mohamed Fawzy ElSayed Ali AbdAlla,
CETA, CFTe, MFTA

By Mohamed Fawzy ElSayed Ali AbdAlla, CETA, CFTe, MFTA

Abstract

Data averaging is not only one of the most important but
also the most commonly used tool in all money-related fields;from upwards to downwards, it is used in econometrics toanalyse a country’s macro-economic factors. It is also applicableto fundamental and technical analysis for financial ratios
and price charts of sectors and companies. Data average isperformed by many different types and techniques; the mostused and popular are the simple arithmetic, the geometric, theweighted, and the exponential average. Comparing a sequenceof successive averages is known as moving average.
Before using a popular tool and depending on its outcome, it
is logically mandatory to understand deeply the advantages anddisadvantages of each type of moving average. The majority oftechnicians consider the exponential moving average (EMA)
to be the most suitable and efficient average in terms of its
concept, which some believe to be a myth. Moreover, the EMA
is embedded in the calculation of many popular technicalindicators and is used by technicians in coding the formula oftheir developed indicators.
This paper will attempt to deeply understand the drawbacksof the EMA and introduce the the coefficient moving average(CMA)—a moving average with a new concept in its calculationthat is thought to be a better combination, with more realadvantages and fewer disadvantages than other well-known
moving averages. Besides, CMA is more flexible and able to be
adjusted by the user to fit its intended use.

Introduction
Overview on Moving Averages in TechnicalAnalysisMoving averages seem to be the most successful tool in
the objective of technical analysis—mainly because averages
as lagging indicators profit from the trend by allowing entry
and exit points after sufficient confirmation and dampen theeffects of short-term oscillations. Additionally, the averages
are essential components in the calculation of almost all otherindicators and oscillators, e.g., MACD, Stochastic, CCI, ADX, RSI(Kirkpatrick, 2016).
Therefore, it is mandatory to fully understand the drawbacksof each of the most commonly used moving averages—simple,
weighted, and exponentia—to know how much we were misled
due to their limitations and how restricted we were in our choices.
Popular Commonly Used Moving Averages (SMA,
WMA, EMA)
The simple moving average (SMA), also named truncated+201005004322
fawzyuro@gmail.com

moving average, is the real average of the data over the period
as being the arithmetic mean, which is equal to the summationof the data divided by the number of the data.
SMA(t) = Σ(Pi) / n
Where Pi is the price at a time point (i), n is the number of prices
or the time points.
However, SMA has disadvantages:
1. It gives equal weights to all the data from the recent
significant datum until the old less important datum.
2. It is affected by extreme value, as only one extremely larger
or extremely smaller value will drag the average to it andaway from the rest of the repeating values. This disadvantage
occurs twice; first, when the extreme value has recently comeinto the average window, and second, when it is late to drop off
the period of the average. Mainly, when it exists, the averagecauses a lot of misleading average movmente. This flaw isworse with a shorter moving average (Alexander Elder, 2014).
3. It does not contain the historical data.
The weighted moving average (WMA) gives less weight (=1) tothe oldest datum in the period that increases regularly (by +1)
until the most important recent datum in the period.
WMA(t) = Σ(Pi.Wi) / n
Where Pi is the price at a time point (i), Wi is the weight of theprice Pi, n is the number of prices or the time points.
Therefore, WMA overcomes the disadvantages of not giving
more importance to the recent data. In addition, the extreme
value will greatly affect the average only when entering it,
which is accepted, but its weight will decrease gradually until
exiting the average unnoticed. However, the WMA still does notcontain the historical data as with the SMA.
The exponential moving average (EMA) not only gives more
importance to the recent data but also contains the historicaldata.
EMA(t) = [ K .P(t) ] + [ (1-K) .EMA(t-1) ]K = 2 / (n-1)
Where K is the smoothing coefficient of the EMA, P(t) is
the price at a time of EMA, EMA(t-1) is the previous EMA,
n is the period of the average. Huston has described thesmoothing constant “k” (Perry Kaufman, 2013).

Weighted and Exponential Moving
Averages Explained
Digging Deeper Into WMA Misses
The WMA is weighted by multiples of the weight of the oldest
datum, which is acceptable. The progress of the cumulative
weight for the data weights of a WMA with a period of 100,
starting from the highest weight of the most recent datum (100)IFTA.ORG PAGE 55

IFTA JOURNAL

2021 EDITION

and cumulating to the smallest weight of the oldest datum (1), is
illustrated in Table 1 and is compared to the linear progressing
cumulative weight of SMA in Figure 1.
Table 1. The relation between the number of datum andthe percentage completed from the whole weights of
WMA
Number of
Datum

Completed % of the
WMA weights

Number of Completed % of
Datum
the WMA weights

14

25.9%

43

67.3%

19

34.2%

51

75.7%

25

43.6%

67

88.9%

30

50.8%

75

93.6%

34

56.2%

91

99.1%

Figure 1. The progress of the cumulative weight for a
WMA 100 compared to that of the equal weights SMA 100

Therefore, it is evident, for example, that half the data
contributes to three-quarters the weight of the average and theoldest tenth of data is nearly neglected. Besides, it is worthy
to note that the data in the previous table and chart also
applies for WMA with periods of 20 or more, and near figures
are obtained for WMA with periods of 3 to 19. Those results
reveal a new disadvantage of the WMA: its weighting techniqueseems to be overweighted and biased significantly to the recent
data. Moreover, there are no other weighting options between
the unweighted SMA technique and the overweighted WMA
technique.
Digging Deeper Into EMA Misses
In this section, we will explain in detail the EMA using themethodology of applying logical questions and showing thesurprising answers that will all together expose the fragility of
the EMA technique.

Question 1: What does the (K) factor in the EMA equation
stands for?
For the WMA of the period (n):The weight of the current datum (highest weight of the mostrecent datum) is = n / Σ weights
The weight of the datum previous to most recent datum is =(n-1) / Σ weights
The weight of the oldest datum (smallest weight) is = 1 / Σweights.

PAGE 56 IFTA.ORG

And since the weights are the arithmetic series of the natural
numbers “1, 2, 3, 4, 5, …. , n” its summation is= (n²+n)/2 = (n * (n+1)) / 2.
Thus, the weight of the current datum = n / [ (n * (n+1)) / 2 ] =2n / (n * (n+1) = 2/(n+1), which is equal to the “K” constant usedin the EMA calculation.
The earlier conclusion means that in the EMA technique:
1. The weight of the most recent datum for the WMA and EMA
are equal, and both are equal to 2/(n+1) which is the (K).
2. In the WMA, the weight of the previous datum is calculated
again by decreasing rate and so on until the oldest datum
takes the weight of 2 / (n²+n). While in the EMA, the weightof the previous datum is the same as the weight of the firstdatum, but it is calculated from the residual of the average
weights. And so on, until the oldest datum of the period takes
the weight from the last residual of the average weights, andthen the left is set for the historical data weight (data fromthe start of the chart until before the calculated period).
3. Accordingly, the weight of the historical data takes nothingfrom the weight of the most recent datum in the EMA if
compared to the weight of the current datum in the similar
WMA.

Question 2: What is the weight of the historical data (before theperiod) in the average weight?
By understanding the calculation of the EMA; where therecent datum takes (K) weight, and the previous average takes(1-K) weight. From the weight of the previous average, whichis (1-K), again the before previous datum takes (K) weight andthe before previous average takes (1-K) weight. And so on, thisprocess is repeated until the oldest datum in the period takes(K) weight, and the last residual (1-K) weight is left for the data
before the average period, which stands for all the previous
historical data before the EMA period.
Thus, this final residual weight is the residual from theresidual from the residual and so on, which is equal to (1-k) from(1-k) from (1-k) and so on for (n) loops. Therefore, it is concluded
that we can calculate the weight of the historical data in the
EMA by the equation:
W(H) = (1-k)^n = [ 1- ( 2/(n+1) ) ]^n
Where W(H) is the weight of the historical data before the(n) period of the EMA.
By this equation, we can plot a curve displaying the weightsof the historical data before the period in an EMA against
all periods of EMA from 2 to 200; the resulting data showedthat the historical data always has 13.5% weight of the EMA.
Moreover, the curve turned out to look like an exponential
curve that may be the origin of the name of the average. This is
illustrated in Table 2 and Figure 2.

IFTA JOURNAL

Table 2. The weight of the historical data before theperiod in an EMA
Period (n) of EMA

W(H) = (1-k)^n = (1-(2/(n+1)))^n

2

11%

3

12.5%

4

13%

14

13.4%

17

13.5%

200

13.53%

Figure 2. The weight of the historical data before theperiod in different EMA

Knowing the weight of the historical data as a percentage
is a good issue, but the most important is knowing its relative
weight to other data weights in the average. To start, we haveto standardize an allocation system for the data in the average,
so we will consider the location (L) of the most recent current
datum as “0” and the previous datum as “1” and so on till theoldest datum in the period having a location of “n-1”. Therefore,
we can calculate the weight (W) of any location (L) in a WMA
with any period (n) by the following equation:
W(L) in WMA(n) = [ 2* (n-L) ] / [ n*(n+1) ]And for the EMA: W(L) = K * (1-k)^L and since 1-k = (n+1 / n+1) – (2/ n+1) = n-1 / n+1 so W(L) in EMA(n) = (2/n+1) * [ (n-1/n+1)^L ] = (2/n+1) * [ (n-1)^L / (n+1)^L ] so finally:
W(L) in EMA(n) = [ 2 * ( (n-1)^L ) ] / [ (n+1)^(L+1) ]By applying these equations to different locations and thehistorical data in different EMA of different periods, we will findsurprising drawbacks in the weighting system of the EMA as
following:
1. The weight of the historical data before the period may be
more than the weight of the oldest datum in the period; thisoccurs very early in the period of (4):
EMA(3): W(0)=50%, W(1)=25%, W(2)=12.5%, W(H)=12.5% so
W(H)=W(oldest).
EMA(4): W(0)=40%, W(1)=24%, W(2)=14.4%, W(3)=8.64%,
W(H)=12.96% so W(H)>W(oldest).
2. Moreover, the weight of the historical data before the period
may be more than the weight of the most recent (mostimportant) current datum in the period; this occurs early inthe period of (14). The relation between them in different EMA
periods are illustrated in Table 3.

2021 EDITION

Table 3. The relation between the weight of current
datum and the weight of historical data in different
periods of EMA
Period (n)of EMA

Weight of current
datum W(0)Weight of historical Ratio
data W(H)
W(H):W(0)1314.28%

13.48%<

14

13.33%

13.49%

>

20

9.52%

13.51%

1.42 times

50

3.92%

13.53%

3.45 times

100

1.98%

13.53%

6.83 times

200

0.99%

13.53%

13.66 times

Question 3: From where and how are the 13.5% weight of thehistorical data obtained from the weights of the data in the
EMA period?
For the WMA, the weight of the current datum is the highest,
and then the weights decrease regularly (with fixed value =weight of the oldest datum) until the lowest weight of the oldest
datum to cumulatively form 100% of the WMA weights. Whilefor the EMA, the weight of the current datum is the highest(= weight of current datum in WMA), and then the weights
decrease irregularly (with increasing rate then decreasing rate)until the lowest weight of the oldest datum to cumulatively form
86.46% of the EMA weights.
For the EMA, the weights of the data in the period showchronological decreasing rate, as illustrated in Figure 3.
Figure 3. The irregular decrease in weights throughlocations in the EMA

1. During the recent 36% of the data (from the most recent
current datum backwards to third the data), a rapid decrease
in weights occurs to form cumulatively 52.29% of average
weights. Also the weights are less than their similar locationsin the WMA where the difference reachs its maximum at theend of this partition.
2. From 37% to 79% of the data (middle 43% of data), a slow
decrease in weights occurs. At the end of this portion, thecumulative weight reaches 79.81% of the average weights.
Also the weights are less than their similar locations in the
WMA, where at the end of this partition the cumulative
weight is equal in EMA and WMA.
3. From 80% to the oldest datum (oldest 21% of data), a very slow
decrease in weights occurs. At the end of this portion, thecumulative weight reaches 86.46% of the average (and 13.54%
is left to the historical data before the period). Also theweights are more than their similar locations in the WMA.

IFTA.ORG PAGE 57

IFTA JOURNAL

2021 EDITION

Therefore, it is obvious that the weight of the historical data
(13.5%) are obtained irregularly from the weights of the data inthe period causing an irregular rate of decrease in the weightsof the data in the period. In fact, this is because the decreasein the weights is by a regular percentage rate not a value rate,
where this percentage rate of decrease is equal to (K). Therefore,
each location weight is less than the previous location weightby K% of that previous location weight. So, EMA is a percentage
smoothing average.

Question 4: How far does the EMA go in history?
Theoretically and mathematically, the SMA for the oldest
period in the early beginning of the data has a calculated weightin the recent EMA occurring after many decades of data. But
practically speaking, the weight of the EMA is divided into
86.46% to the recent period and 13.54% to all the previous data,
where this 13.54%, in turn, is divided into 86.46% of it to theprevious period, and 13.54% of it is left to the data before therecent two periods, and so on. The cumulative weight of periodsof data in the EMA is illustrated in Table 4.
Table 4. The cumulative weight of previous periods in
EMA
Previous periods of data

Cumulative weight in current EMA

1

86.46%

1.5

95.02%

2

98.17%

2.3

99.01%

3

99.75%

3.5

99.91%

4.5

99.99%

This result uncovers the fact that the EMA considers (nearlyonly) the recent two and third periods of data and does nothandle the aggregate historical data.

Question 5: What does the EMA actually perform in the period?
The EMA does not perform any specific action on the data inthe period other than that on the data before the period. So in
fact, the EMA does not respect the period at all. It seems that the
EMA misleads the user by entering a value in a data field called“EMA Period”; while this value will not be used as its name, theEMA uses the period value only to create a smoothing factor(K) simulating the first weight in the WMA with a period likethe entered value. Afterwards, the EMA uses this generated“constant” to smooth all the data, disregarding the data in the
period from the data before the period and giving no advantage
to the data in the period.
Therefore, the EMA really does not “average” the data in a
specific period like SMA and WMA but smoothes all the data.
Also, each SMA or WMA at a new point in time considers a new
datum and neglects an old datum, so actually it “moves” throughthe data. However, each EMA at any point in time smoothes
the all the data before it, so it is misnamed as “moving” and as“average” too.

PAGE 58 IFTA.ORG

The SMA can be officially named “moving arithmetic mean”,
and the WMA is best named as “weighted moving average”. As apoint of view, it is more apt to describe the EMA as “exponential
smoother” rather than “exponential moving average”.

Question 6: What does the EMA actually perform on every data
feed?
The EMA does not deserve the name average, as it acts only
on the current new feed by giving it (K) weight, and it does notconsider averaging it with the data in the period precedingit (depending on being included in the previous EMA). So theresidual weight is easily directed toward the previous EMA, andso on through the entire data.
For the SMA and the WMA, with every new feed, the oldest
datum in the previous average is neglected, and the new feed isaveraged again with the other data on a statistical concept ofarithmetic mean or weighted mean, so the non-used portion of
the current datum is dismissed due to the averaging, which hasa tremendous psychological consideration.
However, EMA is not an average; therefore, we will look to it
from another view than the weights in an average. So with every
new feed, the vertical amplitude between the previous EMA andthe current new feed is calculated, the new EMA moves in the
direction of the new datum by (K) percentage of this distance,
and the residual of the distance is thrown with no statisticalbases and without any psychological rational. The user may notunderstand that the EMA does nothing except slow the entire
chart by following the data line and covering only a certain
percentage of the lag in each step.
Question 7: When does the EMA deflect?
Since the calculation of the current EMA takes a certain
percentage from the current datum and the remaining
percentage from the previous EMA, by logic, the current EMA
must be located between the current datum and the previous
EMA and can not break beyond the current datum or theprevious EMA. Accordingly, when the current datum is higher
than the previous EMA, the current EMA will rise higher thanthe previous EMA, disregarding if the current datum is rising orin the same level or declining relative to its previous datum.
Now we can conclude that the rising EMA will never reverse
downwards except when crossed by its data to the downside(i.e., the current data is lower than the previous EMA). Then,the current EMA will turn to decrease the downward distance
between the EMA line and the data line (the EMA line reverses
only after crossed over by its data line). Unlike the smart SMA
or WMA, when the current datum is higher than the previous
average, but the current datum is much lower than its previous
datum, this extreme value (especially in WMA) will significantly
affect the current average, so that the current average may
be lower than the previous average. Therefore, the moving
average reversal can occur before the occurrence of actual (late)crossover of the current datum below the previous EMA. Finally,
we can describe the EMA as a slow follower, which resembles anidiot slowly dog walking towards his master who rides a horse.
All these answers now uncover the major mathematical andtechnical disadvantages of the EMA:IFTA JOURNAL

1. It considers almost only an additional 1.3 period of thehistorical data.
2. This historical data has a weight more than the weight of thecurrent datum in periods greater than 13.
3. The decrease in weights of the data through the period isirregular.
4. It does not consider the period by any specific action.
5. It is not an average to the period but a smoother to the entiredata.
6. It is a slow follower (reverse only after crossed over by its
data line).

Coefficient Moving Average (CMA)
Calculation and Concept of CMA
Two user-entered parameters are the data array to beaveraged (D) and the period of averaging (n). Each average value
will act on (n+1) number of data, arranged as follows: the CMA of
the previous period, then the oldest datum in the period. Then,proceed chronologically forward until the most recent current
datum. Other two user-entered parameters are the value of
averaging coefficient (C), ranging from “0” to “1”, and the type of
averaging (T), which is either simple “0” or compound “1”.
• The CMA of the previous period is given a weight of (1).
• When simple averaging is selected, summation is used: weightof each datum = weight of previous datum + coefficient.
• When compound averaging is selected, multiplication isused: weight of each datum = weight of previous datum * ( 1 +coefficient ).
• The current CMA is calculated like the WMA: Current CMA =summation of weighted data / summation of weights.
The weighting system is clarified by an example in Table 5.
Table 5. Example for CMA weighting system
Data (D) = C “closing price”, Period (n) = 5, Coefficient (C) = 0.5
CMA ofprevious
periodSimpleCMA(C,5,0.5,0)1Compound 1
CMA(C,5,0.5,1)

1

5(oldestdatum inperiod)2

34(current
datum inperiod)Totalweights

1.5

2

2.5

3

3.5

13.5

1.5

2.25

3.375

5.063 7.594

20.782At the beginning of the data:
In the oldest period, from the oldest datum until the datum
before the most recent, there is no calculated CMA.
In the period after the oldest period, starting from the mostrecent datum in the first period until the datum before the mostrecent datum in the second period, the CMA is calculated for(n) data (not n +1)—i.e., without previous CMA, with the oldest
datum in the period taking the weight of (1), and the coefficientaugments the weights through the period.
Starting from the most recent datum in the second period
until the end of the data, CMA is calculated normally.

2021 EDITION

Advantages of CMA
1. When using the simple averaging method of CMA andselecting a coefficient zero, this simulates the SMA, as alldata have equal weights, but with the edge of including thehistorical data.
2. When using the simple averaging method of CMA andselecting a coefficient one, this simulates the WMA, but withthe edge of including the historical data. Taking advantage
over the simple CMA with coefficient zero as being weighted
and affected by extreme values only upon entering the
average.
3. When using the simple averaging method of CMA andselecting a coefficient between zero and one, this breaks thecuffs and opens the horizon for the user to customise his
own suitable regular tested weighting intervals between theunweighted SMA and the overweighted WMA.
4. CMA is a real average that not only greatly considers theaveraged period but also includes historical data in the formof the averaged previous period value, then calculates thestatistically logical arithmetic or weighted mean. The previous
average also contains the before previous average and soon, so CMA is smart to track its own way backwards in thehistorical data by including only its average before the period.
5. Regarding the depth of the significant considered history,
at least one previous period from the historical data isguaranteed, as it is already logically embedded in the
averaging mathematical equation, while theoretically all thehistorical data are included.
6. In addition to including the historical data, CMA ensures:a) The decrease in weights of data as we go backwards is not
only regular but also user-customised.
b) The weight of the historical data is definitely less than theweight of the oldest datum in any averaged period.
c) A continuous regular dilation of weights is performed
through the data in the averaged period, and then throughthe historical data.
7. The compound averaging method of the CMA allowed a regularpercentage weighting system (not as the regular value weighting
system of the simple CMA) that also averaged a considered
period and added the historical data with the least weight.

CMA Amibroker Formula Language(AFL) Code
Indicator Inputs
AveragedData = ParamField(“Data Field”,3);
Period = Param(“Period”, 14, 2, 300, 1, 14);
Coefficient = Param(“Weight Coefficient”, 0.5, 0, 2, 0.01, 0.1);
WeightType = ParamToggle(“Weight Type”, “Simple|Compound”, 0);Calculate Weights and Total Weights
Weight[0] = 0; TotalWeights = 0; TotalWeightsWithoutHistory = 0;
for ( Multiplier = 0; Multiplier <= Period; Multiplier++ ){ if ( (WeightType) == 0 ) Weight[Multiplier] = 1 + ( Coefficient *Multiplier );if ( (WeightType) == 1 ) Weight[Multiplier] = ( 1 + Coefficient )^Multiplier;
TotalWeights = TotalWeights + Weight[Multiplier];
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2021 EDITION

if ( Multiplier == Period ) TotalWeightsWithoutHistory =TotalWeights - Weight[Multiplier]; }Find the Start Position and Calculate First CMAs
Without Historical Data
CoefficientMovingAverage[0] = 0;
for ( i = 0; i <= BarCount -1; i++ ) if ( AveragedData[i] > 0 OR
AveragedData[i] < 0) { StartPosition = i; break; }for ( BarPosition = StartPosition + Period -1; BarPosition <=StartPosition + ( 2* ( Period -1 ) ); BarPosition++){ CummulativeWeightedData = 0; Counter = 0;
for ( AveragedBars = 1- Period; AveragedBars <= 0;
AveragedBars++){ CummulativeWeightedData = CummulativeWeightedData + (AveragedData[BarPosition+AveragedBars] * Weight[Counter] );Counter++; }CoefficientMovingAverage[BarPosition] =CummulativeWeightedData / TotalWeightsWithoutHistory; }Calculate the Following CMAs With Historical Datafor ( BarPosition = StartPosition + ( 2* Period ) -1; BarPosition <=BarCount -1; BarPosition++){ CummulativeWeightedData = CoefficientMovingAverage[BarPosition-Period]; Counter=1;
for ( AveragedBars = 1- Period; AveragedBars <= 0;
AveragedBars++){ CummulativeWeightedData = CummulativeWeightedData + (AveragedData[BarPosition+AveragedBars] * Weight[Counter] );Counter++; }CoefficientMovingAverage[BarPosition] =CummulativeWeightedData / TotalWeights; }Plot CMA
Plot ( CoefficientMovingAverage, “CMA”, ParamColor( “CMA
Color”, colorCycle ), ParamStyle(“CMA Style”), 0, 0, 0 );Testing Results

The testing of the CMA was performed on the stocks of
the S&P 100 index for three years, from January 2016 to
December 2018 on the daily timeframe. The strategy used
was the simplest, which was the single crossover tactic; buy
when the closing price crosses the moving average upwards(Bullish crossover), and sell when the closing price crosses themoving average downwards (Bearish crossover). Also, only longpositions were taken for the sake of simplicity.
The portfolio was adjusted to allow the maximum of 100
simultaneous open positions to allow open trades in all available
stocks at the same time. Besides, the position size for each trade
was set as 1% of the portfolio to allow equal exposure.
Different types of moving averages were tested using theclosing price data, and the period of 19 days was selected (basedon previous mapping for the best CMA period selection). Then,the results were clarified in Table 6.

Table 6. Testing CMA against other averages on the S&P 100
CMA (0.45,
Simple)Item

CMA (0.1,
Compound)

SMA

WMA

EMA

Whole Trades

Number of All Trades

94

92

92

129

89

Average Outcome Percentage per Trade

1.04%

0.92%

-0.06%-0.56%

0.07%

Average Exposure Percentage

98%

98.7%

97.5%

97.6%

98.1%

Winning Trades

Number of Winning Trades

30

29

26

32

24

Average Profit Percentage per Winning Trade

6.19%

6.4%

4.39%

3.01%

5%

Average Bars Held per Winning Trade

19.9

21.2

20.3

15.8

23

Maximum Consecutive Winning Trades

3

4

2

4

3

Losing Trades

Number of Losing Trades

64

63

66

97

65

Average Loss Percentage per Losing Trade-1.37%-1.61%-1.81%-1.74%-1.75%

Average Bars Held per Losing Trade

3.7

3.5

4.5

3.7

4.3

Maximum Consecutive Losing Trades

10

7

7

19

11

System Return & Risk

Compounded Annual Return32.57%

26.43%-3.61%-23.15%-0.74%

Maximum Trade Percentage Drawdown

-7.9%

-7.9%

-8.1%

-14.8%-11.4%

Maximum System Percentage Drawdown-16%-23.9%-28.2%-56.8%-26.5%

Ulcer Risk Index

5.73

8.59

8.57

31.35

9.4

PAGE 60 IFTA.ORG

IFTA JOURNAL

Item

CMA (0.45,
Simple)CMA (0.1,
Compound)2021 EDITION

SMA

WMA

EMA

System Risk/Reward Ratios

Profit Factor

1.89

1.62

0.91

0.55

0.98

Payoff Ratio

4.02

3.52

2.32

1.66

2.66

Probability of Winning Trade

31.9%

31.5%

28.3%

24.8%

27%

Compounded Annual Return / Maximum System
Percentage Drawdown

2.03

1.11

-0.13

-0.41

-0.03

Ulcer Performance Index

4.81

2.49

-1.01

-0.9

-0.61

K-Ratio

0.12

0.09

0.02

-0.04

0.04

Discussion
CMA Combined With Time Analysis
The technical analyst identifies an active cycle in theprices when a three or more successive waves (move and its
correction) take nearly the same duration. Therefore, it ismandatory to adjust the period of the moving average equal tothis cycle duration. However, many times, the simple moving
average (before backward shifting) lies far below some pricebottoms, and some other price bottoms false break this moving
average. Moreover, it is common that the channels around thecentered moving average can barely contain the price peaks andbottoms accurately without false breaks or being far from priceturning points.
An ultimate solution is to use the CMA that is not only
committed to the period and averages only the prices throughthe wave duration. But also, the CMA coefficient has a wide
range to be fine-tuned, allowing the CMA line to convincingly
touch the price bottoms (before backward shifting), and leading
the channels to accurately hug the price turning points (after
centering the CMA). Therefore, the CMA opens the limits for thetime analysts to enhance their use of channels in time cycles.
Coefficient adjustment enhance Risk-adjusted Return:In the past, we were blocked to use only the unweighted SMA
or the overweighted WMA, but the CMA opens the wide range of
weighting system between both of them. To know whether that
adjustable coefficient is beneficial, we studied the CMA over theconstituents of the S&P 100 by the single crossover tactic forthree years starting from 2016 until 2018.
Ulcer Risk Index = Square root of [ Sum of Squared System
Percentage Drawdowns / Number of Bars ]Unlike the standard deviation, the ulcer risk index measures
only the downward volatility, and from the previous peakrather than from the mean. Therefore, it represents how muchthe system can maintain achieved returns, by less in number
and shallow (depth of drawdown) and rapidly recoverable
drawdowns (duration of drawdown).
Ulcer Performance Index (UPI) or Martin ratio = [ Total Return- Risk Free Return ] / Ulcer Risk Index
UPI measures the risk-adjusted return of the equity curve ofthe trading strategy.

Figure 4. UPI of CMA with different periods andcoefficients on the S&P 100

Figure 4 represents a 3D chart where the X-axis shows thedifferent periods of the CMA ranging from 2 to 50; the Y-axis
shows the different coefficients of the CMA ranging from 0 to 1
(with intervals of 0.05); the Z-axis shows the Ulcer Performance
Index (with a water level at 0.5 to facilitate visual analysis). This
chart proves the following:
1. Some CMAs, with coefficients between zero and one, have UPI
not only higher than the UPI of the CMA with the same period
and coefficient zero (that resemble the SMA with considering
the history), but also higher than the UPI of the CMA withthe same period and coefficient one (that resemble the WMA
considering the history).
2. This situation is not a single spike in the chart but a chainof mountains as occurring in the periods from 17 to 27 withnearly all the coefficients between zero and one. This provesthat it is not an exceptional case found by a wicked data
mining, but it is the fact that we were in a bad need to open
the field of weighting systems to enhance the moving average
strategies.

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2021 EDITION

Conclusion• The EMA has a lot of statistical and logical pitfalls that makeits use questionable and should be revised.
• The averages used in the equations of many technicalindicators are logically to be arithmetic average, but SMA
is usually avoided because it can give zero value that ruins
the equation if the average value is in the denominatorof a division. However, it is advised that the CMA with acoefficient zero replace all these averages, as it resembles the
SMA but with the advantage of containing the historical data,
so it can rarely give a zero value.
• The averages used as a technical tool in a trading strategy areadvised to be the logical WMA, as it has a statistical concept
in averaging, with the advantage of weighting the recent
data. However, it is advised that the CMA with a coefficient
one replace all these averages, as it resembles the WMA but
with the advantage of containing the historical data, and it
can give better results.

PAGE 62 IFTA.ORG• CMA with the adjustable coefficient between zero and one
allows the user to apply his own suitable tested weights;
also the compound coefficient introduces a new edge in theweighting system.
• CMA with the adjustable coefficient allows better time
analysis using channels around the centered moving average.

References
Elder, Alexander, The New Trading for a Living. John Wiley & Sons, 2014.
Kaufman, Perry, Trading Systems and Methods. 5th ed. John Wiley &Sons, 2013.
Kirkpatrick, Charles D., and Julie A. Dahlquist. Technical Analysis: The
Complete Resource for Financial Market Technicians. 3rd ed., FT Press,
2016.

Software and Data
Testing was performed by AmiBroker software, version 6.2.1.
Data were provided by Thomson Reuters data feed.

IFTA JOURNAL

2021 EDITION

Mohamed Ashraf Mahfouz, CETA, CFTe, MFTA

The Ripples Effect: A Clearer View forMarket Action and Price Patterns

c/o Commercial International Brokerage Company (CIBC)
West Tower, Galleria 40,
26 of July axis extension Sheikh Zayed,
6 of October City, Egypt

By Mohamed Ashraf Mahfouz, CETA, CFTe, MFTA

Introduction

How do markets move? It is question that is asked byeveryone, but unfortunately it has no clear or confident answer.
For example, a fundamentalist would say that markets move
randomly around its intrinsic value, without denying thetrending manner the markets move in. A technical analyst or
chartist would say that markets move in trends, surely without
denying the existence of randomness, or doubtful areas, andthe continuous false breaks the markets witness during thesetrends. Can it be inferred, then, that markets move in clear
trends, but in a random way? This article tries to answer thisquestion by discussing the causes for market movements. It
tries to analyze how to identify the general market trend andexpect the random movements that constitute it using a simple
logical tool. The article draws heavily on the work of Brian J.
Millard (1999) on channels.

Price Movement: Questioning theDemand and Supply Equation

When it comes to market action it is always seen as a resultof the demand and supply equation or bulls versus bears. Ifbulls are stronger than bears the trend is believed to be up andconsists of continuous higher lows and higher highs. On theother hand, if bears are stronger than bulls the trend is thenconsidered down and consists of continuous lower highs andlower lows. Sometimes if volume is available we can use it toconfirm those directions or breakouts.
Based on the previous logic, price patterns are defined asgraphical formations that appear on the price chart, havepredictive value, and are created by demand and supply forces.
Accordingly, if we have, for example, a horizontal resistancelevel and a higher low pattern, we identify it as a bullish
ascending triangle, and the next direction bias will be to theupside. On the other hand, if we identify a head and shoulders
pattern thatconsists of same lows between three peaks, but thethird peak is a lower high (after a higher high), the next direction
biasedness will be to the downside etc.
The following charts of Ezz Steel Company (ESRS.CA) in
the Egyptian Stock Market are showing the above-mentioned
situation as follows:+2 01001842420
mohamed.ashraf@cicapital.com
Chart 1. Ezz Steel Company (ESRS.CA) weekly candlestick
chart with volume

As can be seen in Chart 1, the stock was in a downtrend
from Q4 2014 until Q3 2016, forming consecutive lower highs
and lower lows and showing that bears were in control. In Q4
2016, the stock achieved a clear higher low formation as it did
not make a new low and started to rise steeply, breaking theprevious peak to the upside and was accompanied with highvolume. At this point, this kind of breakout indicates that bulls
are in control and the next direction is going to be to the upside.
That is exactly what happened afterwards, as shown in Chart 2.
Chart 2. Ezz Steel Company (ESRS.CA) weekly
candlestick chart with volume (update)

But does it always work like this?
The previous example proves that the market direction isindicated by the supply and demand forces represented by peaks
and bottoms. Unfortunately, this is not totally true, as there arelots of other examples that show the same situation, but the end
result or direction is different. See the following examples.

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2021 EDITION

Example 1:

Example 2:

Chart 3. Qalaa Holding Company (CCAP.CA) weekly
candlestick chart with volume

Chart 5. The Egyptian Stock Market index (.EGX30) daily
candlestick chart

As can be seen in the Chart 3, the stock was in a downtrend
from Q4 2009 until Q2 2012, forming consecutive lower highs
and lower lows, showing that bears were in control. In the Q3
2012, the stock achieved a clear same low formation, as it did not
make a new low and started to rise, breaking the previous peakto the upside and was accompanied with high volume forming
a double bottom formation. At this point, this kind of breakout
indicates that bulls are in control and the next direction is going
to be to the upside. But this time, that did not happen, and thestock, after a minor rise, started to decline, as shown in theChart 4.

Chart 5 displays the EGX30 daily chart; it shows threedifferent directions—two downtrends and one uptrend.
There are three circles on the chart that highlight the break ofresistance levels to the upside after a higher low formation. Thissituation should always indicate a trend reversal, or at least abullish scenario. Despite those upside breakouts, only one ofthem resulted to the upside, and the other two resulted to thedownside. Such situations are referred to as failed breakouts,
whipsaws, or bull traps because, according to the demandand supply analysis, simply nothing can justify that an upside
breakout will result in a downward direction.

Chart 4. Qalaa Holding Company (CCAP.CA) weekly
candlestick chart with volume (update)

Example 3:
Chart 6. The Dow Jones Industrial Average (.DJI) weekly
candlestick chartThus, a new high breakout in this case is considered
misleading, and the stock went down. From demand and supply
perspective, this case does not make any sense; to break aresistance with high volumes and the stock still goes down
is very weird and questions the whole theory of demand andsupply.
The following are examples of two indices of the Egyptian
Stock Market and the U.S. market.

Chart 7. The Dow Jones Industrial Average (.DJI) weekly
candlestick chart (update)

Charts 6 and 7 demonstrate the Dow Jones Industrial Average(.DJI) in year 2010. The index has achieved a clear head andshoulders pattern on the weekly chart during the first half of2010, with a clear breakout to the downside.
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As can be seen in Chart 7, despite this clear downside
breakout, the end result was to the upside, and the index wentto new highs as if nothing happened in the first half of 2010; theonly commentary or justification will be that the pattern failed.
There are many examples that show bearish patterns or
signals that resulted in bull trends or positive direction andbullish patterns or signals that resulted in bear trends ornegative direction. Thus, the market action should be lookedat from a different perspective besides demand and supply(bottoms and peaks).

take profits, but what if the time passed and the stock did not
rise? The trader will still sell it even if he realized a loss because
he is a day trader (time). The same characteristics of the day
trader are applied on the short-term investors that invest during
three weeks or one month duration and are also applied on themedium- or long-term investors that invest in the market every
year or three to five years or more.
The following examples will show six different situations of
how investors with different time spans act together to create
the market trend using the S&P 500 index daily price chart.

Controversy Over Market
Movements

Example 1:
When nearly all types of investors are acting positively inside
the market.

The main reason why the market it is looked at in terms of
demand and supply is that people want to justify the force
behind the market upside and downside movements. But if themain forces are demand and supply, then the economic theory
would imply that markets are moving around an equilibrium
point (demand=supply). So if the demand is bigger than the
supply, the price will increase, then the quantity of supply will
start to increase, and hence, the market will start to decline
until it reaches the equilibrium point. On the other hand, if the
supply is bigger than the demand, the price will decrease, thenthe quantity of demand will increase, and hence, the market will
start to rise until it reaches the equilibrium point. This idea isvery similar to the fundamental analysis explanation to the priceaction, as it suggests that markets are moving randomly aroundtheir intrinsic fair values. Thus, if this justification is completely
right, markets won’t have any trend; they will always move insideways. But practically speaking, this situation cannot be trueat all; we have seen markets that have been in a trend for years,
either to the upside or to the downside. That is why one of themain Dow Theory tenets says that “A trend is assumed to be ineffect until it gives definite signals that it has reversed.”
Main Idea
The main reason why markets move in trends in a zigzag
way is that it is composed of more than one demand and supply
equation, or in other words the market consists of more than one
TIME Span direction that act together at the same point of time.
Dow Theory states that, “The Market has three trends”. Dow
considered a trend to have three parts; primary, secondary andminor which he compared to the tide, waves and ripples of thesea. This explanation is very true about how the market reallyacts. It should be emphasized however that these three parts
should not be considered as three different stages. We believethat the three trends are acting together at the same time all
the time. Thus, instead of dividing the trend into bulls and bears
or long-term versus medium-term trends one should divide it
into long, medium, and short-term investors acting inside themarket at the same time. This was explained by the time cycle
analysis in the sense that the trend consists of all active cycles
or what is referred to as the summation principle.
We believe that the main drive for investment is time before
the amplitude. To clear this point up, let us assume that we havea day trader who entered a trade by buying a stock at a specificlevel. If the stock rose during the session, this trader will sell toChart 8. The S&P 500 index (.SPX) daily candlestick chart

As can be seen from Chart 8, the market rose almost every
day continuously except for a few situations where day traders
affected the close of some sessions, but they do not interrupt thedirection.
Example 2:
When medium- and long-term investors are acting positively
inside the market but short-term investors are acting
negatively.
Chart 9. The S&P 500 index (.SPX) daily candlestick chart

By looking at the chart above, it is very obvious how the
short-term investors’ negative attitude affected the market
direction in some situations. Eventually, the end result is thatthe market will continue to go up as the medium- and long-term
investors are still positive. That is why the market will witness
lots of negative sell signals, but it will resume to the upside, andif there is a bearish pattern appeared it will fail.

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Example 3:
When long-term investors are acting positively inside themarket but medium- and short-term investors are acting
negatively.
Chart 10: The S&P 500 index (.SPX) daily candlestickchart

It can be shown from the above chart how the market trend
can be interrupted for a whole year (Year 2015–inside thecircle). Despite the medium- and short-term selling pressure,the overall trend direction did not shift from up to down, but
it nearly moved sideways, and any downside breakouts failed
to put the market lower because the long-term investors werebuying the market.
This point is extremely important, and it must be clear thatthe next potential direction of any breakout of a support/
resistance or price pattern boundary is not based on its shapeor name but on the direction of the longer term trend. Thus, it
is not based on demand and supply situation; it is based on the
direction of the different time span trends, and thus, the bigger
time span trend will identify the next direction.

Example 5:
When medium- and long-term investors are acting negatively
inside the market but short-term investors are acting positively.
Chart 12: The S&P 500 index (.SPX) daily candlestickchart

It is very obvious from looking at Chart 12 how the short-term
investors’ positive attitude affected the market direction insome situations. Eventually, the end result is that the market
continued to go down as the medium- and long-term investors
are still bearish. That is why the market will witness lots ofpositive buy signals, but it will resume to the downside, and if abullish pattern or upside breakout appears, it will fail.
Example 6:
When long-term investors are acting negatively inside themarket but medium- and short-term investors are acting
positively.
Chart 13. The S&P 500 index (.SPX) daily candlestickchart

Example 4:
When nearly all types of investors are acting negatively inside
the market.
Chart 11. The S&P 500 index (.SPX) daily candlestickchart

In 2017, the market was moving in a high volatility sideways
that contained more than a higher low formation and upside
breakouts, but they all failed to continue the upside movements.
Despite the medium- and short-term buying power, the overall
trend direction did not continue to the upside but rather shiftedfrom up to down after a medium-term sideways struggle
that ended to the downside with the power of the long-term
investors that were selling the market. Thus, the bigger timespan identified the next direction.
As can be seen from Chart 11, the market lost nearly 20
percent of its value in a continuous direction without any
interruption during December 2018.

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Price Ripples
By examining the previous six examples, we will figure outthat the more all types of investors are in harmony taking thesame direction, the less fluctuation the market will witness.
On the other hand, less harmony between different typesof investors leads to more fluctuation in the market. This
fluctuation is created when two or more types of investors are

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moving against each other, which can be called price ripples or
price patterns.
Price patterns can now have a new definition: price ripples
that are created because of the collision of two or more different
trends (different in time and direction), the following directionof any price pattern is based on the direction of the longer term
trend, not on the structure or shape of the pattern.
Price patterns are similar to the water ripples that are created
when two or more water flows are moving against each other, as
shown in Figure 1.

2021 EDITION

Hypothetical Illustration of the Effect of a HighTime Span Trend on the Smaller One
First, let us assume that we have only short-term investors in
the market who buy the market on short-term equal intervalsof time. The expected end result is that the market will move insideways movement with a relatively small magnitude. It willtake the following shape:
Figure 2. Market movement when there are only shortterm investors

Figure 1. Water ripples created as a result of the collisionof different direction water waves

Now let us assume that we have only medium-term investors
in the market who buy the market on medium-term equalintervals of time, which is surely longer than that of the shortterm investors. The expected end result is that the market will
move in sideways movement but this time with relatively bigger
magnitude than that of the short-term investors. It will take thefollowing shape:
Figure 3. Market movement when there are only
medium-term investors

Chart 14 shows how the Ripples Effect appears on a real pricechart when two or more types of investors (in direction andtime) collide.
Chart 14. Emaar Misr for development of daily
candlestick chart

Finally, assuming only the existence of long-term investors:
Figure 4. Market movement when there are only longterm investors

How are these ripples created? This question will be answeredin the following section that explains how the trend is composed
and how different time span trends affect each other.

Identifying the Trend Composition

To try to understand how the trend moves when all types oftrends work together, it will be easier to illustrate first how eachtrend hypothetically works alone and then how the trend of ahigher time span will affect the trend of a smaller time span.

If we hypothetically look at the long-term investors at thesame period of time that we examined the medium and shortterm investors, it will nearly create only one rise and one fall.
The previous three figures showed the hypothetical shapeof the market when we separate each type of investor; we
can clearly see that the end result of each is that the market
will move sideways, and the only difference is that the time
between bottoms will differ and of course the magnitude ofevery rise will differ. Longer term investors will have greater
magnitude and more time elapsed between each successive
bottom. This is what is referred to as “proportionality
principle” in Time Cycles Theory.
After imagining how the market will move if it only includes
one type of investors, Figure 5 will show how the trend will
differ when we add more than one type of investor to the otheror what is referred to as the “summation” principle in Time
Cycles Theory.

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Figure 5. Adding the long-term investor to the mediumterm investor

identify the different types of investors in real prices in order to
expect the next market movement. Figure 6 includes all types ofinvestors together and how the trend will be created.
Figure 6. Putting all types of investors together

As can be seen in Figure 5, we can divide it into three stages:
The first stage: The medium-term investor was affectedby the left side or the rising side of the long-term investor, sowhenever the medium-term investor is declining or pushing themarket lower, it will not reach the previous low like when it wasalone and will make a higher low, and when it rises, it will makea new high, not the same high as when it was alone.
The second stage: This stage is very important when the longterm investor starts to reverse from up to down (from left side
to right side) and the medium-term investor is still involved andgoing upward. This situation creates what we defined as pricepatterns or price ripples. In the above situation, the mediumterm investor nearly took the shape of the sideways as if it isalone. We can identify this sideways as a “double top” pattern.
What makes us claim that it is a double top formation not arectangle formation, or that in such case, the end result will
be to the downside, is that the long-term investor will turn tothe negative side, and the market won’t be able to break to theupside. Thus, the pattern is created by the intersection of bothinvestors (long-term and medium-term), but what identifies thenext direction is the long-term investor.
The third stage: The medium-term investor was affected by
the right side or the falling side of the long-term investor, sowhenever the medium-term investor is rising or pushing themarket higher, it will not reach the previous high like when it
was alone; it will make a lower high, and when it falls, it will
make a new low, not same low as when it was alone.
By realizing the idea of those three stages, we can easily
identify the direction of the investor of a longer time interval
by looking at the peaks and bottoms of the investor of a shortertime interval. If, for example, the medium-term investor ismaking a higher low, that means that the longer term investor
is still looking upward, preventing the medium-term investor
to reach its previous low, and thus, we should be biased for a
new high. And if the medium-term investor is getting weaker
and is not able to make a new high, that means that the longer
term investor is reversing. Lastly, if the medium-term investor
is making a lower high and lower low, that definitely means thatthe longer term investor is declining.
This idea is very important to understand before going
forward with our article because we will rely on it after we

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Identifying Different Types of Investors in Price
ChartsThe big challenge is to isolate different types of investors
as we did previously, but this time on price charts. This can bedone using the “moving average envelopes” or the “percentage
envelopes” or the “envelopes” indicator and without the use oftime cycle analysis.
The envelopes are fixed bands that surround the moving
average by a certain fixed percentage. It is originally made forthe sake of filtration for the price crossover with the moving
average during trending phases, and also made for trading
during sideways by widening the bands in order to trade
between its boundaries.
In this article, we use the envelopes in a different way in order
to isolate the different types of investors by enveloping the priceaction of each investor separately. First, let us establish themain rules when we apply envelopes.
1. Use the daily price chart only. All types of investors willbe identified from the same chart, including the long-term
investors. The chart that will be used is always the shortest
term chart in our analysis, which is the daily chart.
For forex markets, we will use the hourly chart or the halfhour chart as our main chart.
2. Use centered moving average envelopes. It is already known
that envelopes are typically moving averages that are shiftedto the upside and to the downside. However they are averages
for the price, but they are plotted at the last traded price.
They should, however, be plotted in the mid-point of the range
under study. For example if we are using 10-day envelopes,
every new point should be plotted on the fifth day not on the10th day. In this way, the cycle for each type of investor will be
displayed in clearest way.
3. Start drawing your envelopes from the small investor to
the longer term investor. Thus, we will draw from the inside
to the outside. Usually we draw three channels of envelopesthat represent the short-term, medium-term, and long-term
investors. But you can use a longer term channel for seculardegree trends.

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The last general guideline is that you can use time analysis
and price oscillators (Stochastic Slow Oscillator) to help you
choose the right span of the centered envelopes.
The following section will present a case study by applying
envelopes on real stock prices in order to identify different
types of investors in the same price chart.

Case Study: Emaar Misr for Development (EMFD.CA) in the
Egyptian stock market
Charts 15 and 16 are daily price charts for Emaar Misr for
Development, which is a stock in the Egyptian stock market.
This chart was chosen as it nearly includes the three types oftrends (Up – Sideways – Down).
Chart 15. Emaar Misr for development of daily
candlestick chart (Price Only)

2021 EDITION

To solve this problem and try to avoid such false breaks, wehave to examine the different types of investors in order to beable to predict the future direction of the stock based on the
direction of each type of investor separately.
Step 1: Short-term investors
As we have mentioned before in the general rules, that willdraw our envelopes from the inside to the outside. First, we will
choose the envelopes that encompass price randomness. Afterchoosing the right period and percentage of the envelopes toencompass the short-term investors’ fluctuation, remember
to center the envelopes in order to move identically with pricefluctuations.
Chart 17: Emaar Misr for development of daily
candlestick chart with centered five-day envelopes with
3.5% bands

Chart 16. Emaar Misr for development of daily
candlestick chart (with annotations)

In Chart 17, we can identify clearly repetitive bottoms andpeaks that are nearly equal or near in time, which shows
the short-term investors’ attitude or fluctuations. We canalso notice that despite the equally repetitive attitude of theshort-term investors, the trend was not sideways. Sometimes
it resulted in consecutive higher lows and higher highs, othertimes to the downside, and other times sideways. That is
simply because this trend is not created just by the short-term
investors; there are longer time span investors that affected
the trend. This makes us move to the next step of our envelope
analysis.
Looking at Chart 16, we can clearly see a lot of false breaks
in different situations and directions. We can see how manytimes the stock broke a support to the downside but rose
afterwards, and how it broke resistance to the upside but
declined afterwards. We can also see on the left side of Chart
16 a symmetrical triangle formation that was broken to thedownside and should have acted as reversal, but it was a false
break or a failure, and the stock continued to move upward.
These situations happened in many cases and nearly in all
timeframes, especially in the small timeframes like daily, fourhours, hourly, and 30 minute charts.

Step 2: Medium-term investors
To identify the medium-term investors, we will choose theright envelopes that encompass peaks and bottoms of thesmaller channel of the short-term investors. We will try as muchas we can to make the lower band touch most of the bottoms
and the upper band touch most of the peaks of the short-term
investors (smaller channel).

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Chart 18. Emaar Misr for development of daily
candlestick chart with centered 5-day envelopes with
3.5% bands and centered 30-day envelopes with 7.5%
bands

Chart 19. Emaar Misr for development of daily candlestick
chart with centered 5-day envelopes with 3.5% bands,
centered 30-day envelopes with 7.5% bands, and centered
100-day envelopes with 14% and 12.5% bands

Chart 18 shows both short-term investors (red channel) andthe medium-term investors (purple channel). The short-term
investors on the left side of the chart consist of consecutive
higher lows and higher highs because there were longer term
investors that were pushing the market higher. On the other
hand, the short-term investors were making lower highs andlower lows on the right side of the chart because there werelonger term investors that were pushing the market lower. In
cases where the short-term investors moved sideways, thosewere the periods where the medium-term investors werecreating a bottom or a peak within the trend.
In Chart 18, the medium-term investor peaks and bottoms
are now clear. We can also notice that despite the equallyrepetitive attitude of the medium-term investors, the trend
was not sideways. The left side of the chart above consists ofconsecutive higher lows and higher highs, and the right side
consists of consecutive lower highs and lower lows. That is
simply because this trend is not created only by the short-term
and medium-term investors but there are longer term investors
that also contributed in this trend. This makes us move to thelast step of our envelope analysis.

After plotting the long-term channel, we can now
understand the reason behind nearly every move within thetrend. We can always justify the current move or direction of
the trend understudy by understanding the direction of thelonger term trend or investor. For example, on the left side of
the chart, since the biggest channel is moving upward, we canrealize why every downside trigger failed and why, despite theviolation of the lower boundary of the symmetrical triangle—
which we referred to in chart 16—it did not continue to thedownside and end to the upside.
In the top of this chart, we can now understand that the
double-top formation was created because there were two
waves moving against each other; the long-term investors werereversing from up to down, and the medium-term investors
were trying to move upward again, which caused the ripples.
Since the long-term investors were the party that is reversing
to the downside, that meant for sure that this double-top
formation will not fail and will result to the downside.
Let us look again at the same chart after updating the last
price.

Step 3: Long-term investors
In order to identify the long-term investors, we will choose
the right envelopes that encompass peaks and bottoms of its
smaller channel of the medium-term investors. We will try
as much as we can to make the lower band touch most of thebottoms and the upper band touch most of the peaks of themedium-term investors (relatively smaller channel).

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Chart 20. Emaar Misr for development of daily candlestick
chart with centered 5-day envelopes with 3.5% bands,
centered 30-day envelopes with 7.5% bands, and centered
100-day envelopes with 14% and 12.5% bands

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By examining the chart above and by looking at the recent
price action, ignoring the channels we drew, we will see thatthe stock is moving above a strong support and is not violated
to the downside, as can be clearly seen in the drawn circle.
Also, if we try to focus on the short-term price action inside thecircle, we can see that the price did not break the support andalso, there are minor upside breakouts within this sideways
range. This strong support condition that also corresponds
to the major broken peak on the left side of the chart and thementioned minor upside breakouts could make us biased thatthe end result of this situation will be to the upside, or at least tocontinue sideways until we have more confirmation on the next
direction. But as we explained earlier, whatever the situation
and whatever the breakout direction, to anticipate the next
market movement, we have to look at the trend or the long-term
investor; thus, the current sideways movement (price ripples)is created because of the collision of more than one wave at
the same time, and by looking at the long-term channel (green
channel), we can clearly see that it is still looking downward,
which means that the end result of the price ripples in the circle
will be to the downside, and that the strong support will beviolated to the downside. Please take a look at the update ofChart 20 below in Chart 21.

2021 EDITION

Chart 5. The Egyptian Stock Market index (.EGX30) daily
candlestick chart

We are going to apply the envelopes on the chart by drawing
two different time span channels to see what we get.
Chart 22. The Egyptian Stock Market index (.EGX30)
daily candlestick chart with centered 17-day envelopes
with 3.5% bands and centered 55-day envelopes with 6%
bands

Chart 21. Emaar Misr for development of daily
candlestick chart with centered 5-day envelopes with
3.5% bands, centered 30-day envelopes with 7.5% bands,
and centered 100-day envelopes with 14% and 12.5%
bands (updated prices)

In Chart 22, we can easily anticipate which upside breakout
will be successful when we examine the direction of the longer
term channel, when the longer term channel is rising; this is thesituation when any upside breakout will succeed, as it is moving
within the flow of the longer term wave.

As can be seen in Chart 21, the stock breached this strong
support to the downside as expected because of the effect of thelong-term investors that were looking downward.

Case Study 2
In this quick practice, Chart 5 of the Egyptian stock market
index EGX30 (shown below) is revisited to explain why, although
there were three upside breakouts, only one of them succeeded
and the other two turned negative.

Tools and Guidelines That Can Enhance Envelopes
AnalysisLike any tool, using envelopes in that manner has someshortfalls. The main problem is that since envelopes are
centered moving averages, it will always lag the price action by
the amount of centralization. Thus, it is recommended to usesome guidelines while using it or to use another tool besides
using it to overcome this drawback.

Envelopes direction can be identified from the envelopes ofshorter time span
A very important rule is that markets take time to reverse
their direction. In other words, envelopes will take time toreverse from up to down or from down to up.
Thus, if we have envelopes that are looking downward and theenvelopes of shorter time span are moving upward—meaning
two waves are moving against each other—we have to expectthat the smaller wave is the one that will be forced to lookdownward again because of the wave of longer time span.
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2021 EDITION

On the other hand, if the smaller wave started to go down but
it is not able to make a new low (higher low) then this definitely
means that the envelopes of longer time span will move at least
sideways and could be reversing upward. This can be shown
from the example below.

Chart 24 Emaar Misr for development of daily
candlestick chart with centered 30-day envelopes with
7.5% bands and centered 100-day envelopes with 14%
and 12.5% bands (with a hypothetical expectation of thechannels next direction)

Chart 23. Emaar Misr for development of daily
candlestick chart with centered 30-day envelopes with
7.5% bands and centered 100-day envelopes with 14%
and 12.5% bands

Chart 25 will show the real end result of the price action.

In Chart 23, along with two envelopes that represent two
different investors, looking at the red and green arrows, itshows us the collision of the two waves. Whenever the shorter
term investors touch one of the boundaries of the longer term
investors, ripples are created, and the short-term movement
shifts its direction.
At Point 2, the smaller investors were looking down, creating
lower highs. But since the bigger investors were still looking
upward, the smaller channel was forced to look upward again by
the strength of the bigger channel.
At Point 1, we can see how the smaller channel created ahigher low, as shown by the green arrows which makes us
understand, even if the bigger channel is lagging behind, that itis reversing its direction from down to up and hence preventing
the smaller channel from creating a new low. Thus, we should
expect that the smaller channel will eventually make a newhigh.
At Point 3, we can see how the smaller channel createda lower high, as shown by the red arrows, which makes us
understand, even if the bigger channel is lagging behind, that itis reversing its direction from up to down and hence preventing
the smaller channel from creating a new high. Thus, we should
expect that the smaller channel will eventually make a new low.
At Point 4, this is a good practice to understand how we can
expect the next market movement despite the delay of thechannels because of the centralization. As we can see inside
the smaller channel, the prices were moving sideways biased toup, which gave us the feeling that the market will break to theupside. But by looking at the lagging bigger channel, it is still
looking downward; thus, we can imagine that if we extend its
boundaries, it will hit the smaller channel at some point to either
push it to a new low or at least a higher low. Thus, we should
expect that the stock will go down because of the direction of
the bigger channel. Chart 24 is the same as Chart 23 but includes
an imaginary extension for the channels and our expectation.

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Chart 25. Emaar Misr for development of daily
candlestick chart with centered 30-day envelopes with
7.5% bands and centered 100-day envelopes with 14%
and 12.5% bands (real update)

Stochastic Slow Oscillator helps in identifying turning points
After understanding the relation between the different
channels, we can use the Stochastic Slow Oscillator (9,5,3) toprovide the trigger of the turning points through the crossovers
of %K to %D. We believe that the Stochastic Oscillator is the best
tool to provide the turning point at the exact peak or bottom;
thus, it will enhance your trading and solve the lagging problemof the channels.
Chart 26 will show the turning points of the smallest channel.
If you want to anticipate the turning point of the bigger
channels, use the bigger time span chart. So, if you are using thedaily chart, use the weekly or monthly charts; if you are using
hourly chart, use the 4-hour or daily or weekly chart.

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Chart 26. Gold 4-hour candlestick chart with centered 4
4-hour envelopes with 0.4% bands, centered 30 4-hour
envelopes with 0.8% bands, and centered 210 4-hour
envelopes with 2% bands along with Stochastic Slow
Oscillator (9,5,3)

2021 EDITION

Chart 28. Gold daily candlestick chart along with
Stochastic Slow Oscillator (9,5,3)

We can see the clear negative divergence trigger provided by
the Stochastic Oscillator with the price action that anticipatesand confirms the weakness we saw earlier by the channels (inthe 4-hour chart).

As can be seen from Chart 26, all bottoms and peaks of thesmallest channel (purple channel) correspond to the %K & %D
crossovers.
To benefit from the Stochastic Slow Oscillator in the bigger
channels, the following example will clear this point.
Chart 27. Gold 4-hour candlestick chart with centered
30 4-hour envelopes with 0.8% bands and centered 210
4-hour envelopes with 2% bands along with Stochastic
Slow Oscillator (9,5,3)

In the chart above, we have excluded the smallest channel,
and as you can clearly see, the Stochastic Slow Oscillator will not
help to anticipate the turning points of the bigger channels.
As can be shown in the circle in the chart above, the smaller
channel created a lower high formation, indicating that thebigger channel is turning from up to down, and that is why
we could have expected a new low in prices, which actually
happened.
We can also use the Stochastic Slow Oscillator but on thelonger term charts (daily chart in this example) to help us in
confirming the reversal of the bigger channel, as can be seen in
Chart 28.

Momentum indicator can show weakness or strength of thechannels
The envelopes are centered and shifted moving averages;
thus, they may be lagging and they take some time to reverse
from down to up or from up to down. Calculating a four-period
momentum for the envelopes itself could provide us with an
early signal that the channel’s momentum is slowing down in its
direction and is about to reverse. The following example of the
U.S. Dollar versus the Japanese Yen will illustrate this tool.
Chart 29: U.S. Dollar/Japanese Yen daily candlestick
chart with centered 34-day envelopes with 1.8% bands
and centered 120-day envelopes with 3% bands alongwith 4-day momentum of the 120-day envelopes

We can see from Chart 29 how nearly every turning peak orbottom of the big channel (blue channel) was preceded by adivergence with the momentum indicator (in the lower panel). It
was showing the slowdown in the momentum of the rise or fall,indicating that the rising channel will reverse to the downside,
and when it is falling, it indicates that it was about to reverse to
the upside.

Spotlight on IFTA Conference 2019
Predictions

I had the honor to participate as a speaker at the IFTA
conference that was held in Cairo in October 2019. The topic of
my session was actually the topic of this article. To prove thevalidity of the topic, I had provided predictions on the Egyptian
stock market and the U.S. equity market based on the envelopes
analysis as explained. It is my pleasure to show you our
prediction at that time and the update six months later.
IFTA.ORG PAGE 73

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2021 EDITION

The Egyptian Stock Market Prediction

The U.S. Stock Market Prediction

Chart 30. The Egyptian stock market index EGX30 daily
candlestick chart with centered 55-day envelopes with
6% bands and centered 180-day envelopes with 13%
bands, and centered 600-day envelopes with 30% bands

Chart 32. The Dow Jones Industrial Average (DJI) daily
candlestick chart with centered 50-day envelopes
with 7% bands and centered 200-day envelopes with
13% bands along with 4-day momentum of the 200-day
envelopes

In October 2019, EGX30 was almost at 15,000 points. We have
explained before that we should make our analysis from inside
to outside, so by looking at the smaller channel, we will find outthat it was moving sideways and that the bigger channel (thepurple channel) was looking downward; thus, our expectation
then was that the index should test the year low again at
12,500–12,000 points and could extend to make a new low
until it reaches the lower boundary of the long-term investors
channel (orange channel). The following chart is an update six
months later.
Chart 31. The Egyptian stock market index EGX30 daily
candlestick chart with centered 55-day envelopes with
6% bands and centered 180-day envelopes with 13%
bands and centered 600-day envelopes with 30% bands(six-month update)Six months later, the market actions confirmed the previous
expectations. More importantly, staying above 12,000 points
may not be considered as a bearish situation but high volatility
sideways. Being below this area would make most of theinvestors look at the market as a bearish market, as it will
create major lower high and lower low pattern. Yet, since themarket reached the lower boundary of the long-term channel,
we expect that the market will witness a significant medium- tolong-term rally.

PAGE 74 IFTA.ORG

In October 2019 the DJI was at 27,200 points. As you can see
from the chart above, the Ripples Effect was created during twocomplete years (2018–2019), which is—as mentioned before—
the result of the collision of two different investors. Also, bylooking at the 4 momentum of the bigger channel (long-term
investors), it is evident that the momentum of the rising longterm upward envelopes is diminishing, which made us expectthat the end result of this Ripples Effect will be to the downside
as the long-term channel is about to reverse. Thus, we were
expecting that the Dow Jones Industrial Average is at least goingto retest 2019 lows at 22,000–21,500. Chart 33, six months later,
confirms that.
Chart 33. The Dow Jones Industrial Average (DJI) daily
candlestick chart (six-month update)

IFTA JOURNAL

Conclusion

Observing the Ripples Effect or the periods of pricefluctuations brought us closer to understanding how the market
actually works. It made us look at the market action from therelation of different time span investors’ perspectives rather
than looking at it from the demand and supply forces side.
Identifying the different types of investors using envelopes
has the following merits:
1. Having a clearer view for the market action.
2. Understanding how the participation of different time span
investors at the same time creates the market trend.
3. Having a new understanding of price patterns andunderstanding that it is created when two or more different
types of investors (in direction and time) collide, and that is
why we called them price ripples.
4. Not to rely on the price pattern shape to anticipate the next
market movement but to rely on the direction of the longer
term channel.
5. Analyzing the market using short-term channels can help us
in anticipating the direction of the longer term channels.

2021 EDITION

References
Millard, Brian J. Channels & Cycles: A Tribute to J.M. Hurst. Traders Press,
Inc., 1999.
Murphy, John J. Technical Analysis of the Financial Markets: A
Comprehensive Guide to Trading Methods and Applications. New
York: New York Institute of Finance, 1999.
Edwards, Robert D. and Magee, John and Bassetti, W.H.C., TechnicalAnalysis of Stock Trends. Boca Raton: Taylor & Francis Group, 2007.
Kirkpatrick, Charles D., and Dahlquist, Julie R. The Complete Resource
for Financial Market Technicians. New Jersey: Pearson Education,Inc., 2007
Pring, Martin J., Technical Analysis Explained: The successful Investor’s
Guide to Spotting Investment Trends and Turning Points, Fourth
Edition, New York: McGraw-Hill, 2002.

Software and Data

Thomson Reuters Eikon
Metastock, Equis International, a Reuters company
MS Office, 2010 Microsoft office
Data provided by Refinitiv

Also, we have seen how we can use the Stochastic Slow
Oscillator to help us in identifying turning points of the small
channels and to use it on the bigger charts to anticipate thereversal of the bigger channels. Finally, we explained how toapply the momentum indicator on the envelopes itself to provide
us with more information concerning the weakness or strengthof the channels.
In the end, no one will ever have a definite view on the market
because the market is simply inefficient, and if we all know
where the market is going, it will correct itself and move in theother direction. Thus, this article is considered as a trial to help
us look at the market in a clearer and more efficient way andfinally to understand the reason behind the false breaks andrandomness, or what we have called the Ripples Effect.

IFTA.ORG PAGE 75

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2021 EDITION

Yukitoshi Higashino, MFTA

Forecasting Major World Indices on Ichimoku
By Yukitoshi Higashino, MFTA

742-18 Kayadamachi
Yachiyo-shi, Chiba-ken 276-0044
Japan+8190-3499-4252
higashino3z@gmail.com

Abstract

Ichimoku is a technical analysis method developed by Goichi
Hosoda (1898–1982), a Japanese financial market journalist,
through his many years of research in financial markets. Even
30 years after Hosoda’s death, Ichimoku is still widely used bytraders and investors as an effective tool for analyzing markets
and trade with in Japan. Hosada’s grandson took over Ichimoku
and has a study meeting every month in Tokyo.
Many of you may particularly know about “kumo (cloud)”.
However, although Ichimoku is starting to be popularized by anincreasing number of traders and investors around the world,
it does not seem to be utilized as widely and as effectively as
it is in Japan. The reason is because Ichimoku is an integrated
set of multi-faceted market analysis principles and techniques,
including price projection, time projection, wave analysis, andmore.
The wide variety of techniques and concepts included in
the whole Ichimoku theory make it highly challenging to fully
master. And, obviously, there is a language issue. Japanese isa tricky language for many Westerners. Japanese vocabulary
is very different from English, and Japanese grammaticalstructure is also totally different than English, making
translation from Japanese into English quite difficult. It isespecially challenging to find the right English translations ofmany Japanese words used in the original Ichimoku theory.
Therefore I have made an attempt to use plain English words
instead of being “loyal” to the Japanese words in the original
theory.
The following content revises my presentation at the IFTA
Conference in Cairo and adds related theories to make it easierto understand for readers. I hope this will help IFTA colleagues
learn the basics of a unique technical analysis method developedin Japan.

Introduction• Ichimoku is focused on the underlying “powers” in themarket. To know the next market direction, it suffices toknow which side, buyers or sellers, is winning or losing.
The market moves in the direction in which the equilibrium
between the buyers and sellers has been broken. The chartdeveloped by Hosoda allows one to instantly grasp theequilibrium state of the market. This is why it was named“Ichimoku Kinko Hyo,” which literally means “One Glance
Equilibrium Chart” in Japanese.

PAGE 76 IFTA.ORG• The middle-point of the price range is regarded as important.
• The three basic components of the Ichimoku theory are the“time” principle, “wave structure” principle, and the “pricelevel” principle.
• One of the important traits of Ichimoku is its “time” study.
Most market players focus on price moves and tend to makelight of the time factor. In Ichimoku, while price moves areimportant, the time factor is more important than the pricefactor; without a solid “time” study, one cannot have a true
understanding of the markets.

Composition of the Ichimoku Chart

Figure 1 is a daily chart of the S&P 500. Ichimoku consistsof a candle chart and five lines. Each five lines often serves as
support or resistance.
The Conversion line is the middle-point of the high-low range
in the last nine periods (including the current period), and theBase line is the middle-point of the high-low range in the last 26
periods (including the current period). They may look similar
to moving averages, but they are different. They may be called“moving middle-points.” In Ichimoku, it is considered that
middle-points represent better equilibrium points in the market
than moving averages. In moving averages, regardless of thevolatility or the price swing in a given period, the closing priceis the only thing that is counted. That is, even if the price swings
vary widely in a period, it is not reflected. Ichimoku dismisses
this and uses the middle-point indicators, whichinstead reflect
the whole price range in a given period.
The Leading span 1 is the middle-point between theConversion line and the Base line. The Leading span 2 is the
middle-point of the high-low range in the last 52 periods(including the current period). Leading spans 1 and 2 areplotted 26 periods ahead (including the current period). The
cloud-like area formed by the Leading span 1 and 2 is called the“Resistance band.”The lagging span is drawn by plotting the current closing
price 26 periods backward (including the current period). It
becomes a line which runs parallel to the current price line.

IFTA JOURNAL

FigureCompositionof theChart
Ichimoku chart
Figure1.1.
Compositionof the Ichimoku

Three Conditions to Make a Safe Bull
(Bear) Call
When the following three conditions are in place, one cansafely judge that the market is in a bullish state. We call it“San’yaku Koten,” meaning the market is ready to start an
uptrend.
• The Conversion line is above the Base line, which is trending
up or flat.
• The Lagging span is above the price of 26 periods ago.
• The price is above the Resistance band.

On the other hand, when three opposite phenomena are inplace, we call it “San’yaku Gyakuten,” meaning that the market
is ready to start a downtrend.
• The Conversion line is below the Base line, which is trending
down or flat.
• The Lagging span is below the price of 26 periods ago.
• The price is below the Resistance band.

2021 EDITION

Figure 3. Daily chart of 3M as asample of “San’yaku
Koten”
Figure 3. Daily chart of 3M as a sample of “San’yaku Koten”

The Three Basic Principles of
Ichimoku

After confirming San’yaku Koten, you should consider thethree basic principles: “wave structure,” “price level,” and“time”. Figure 4 shows all the points. Notice that the bull market
consists of three waves, and an equilibrium is establishedbetween price and time. The same applies to the bear market. It
is highly probable that the market direction will reverse at theequilibrium point of the horizontal and vertical axes.
FigureThe
threeprinciplesof Ichimoku
Figure 4.4.
The
three
basicbasic
principlesof Ichimoku

Figure 2 is a daily chart of FB as a sample of “San’yaku Koten.”
As of (1), the Lagging span is above the price of 26 periods ago.
As of (2), the conversion line is above the Base line. As of (3), theprice is above the Resistance band. When these three conditions
are confirmed, you can judge “San’yaku Koten.”
Figure 2. Daily chart of FB as a sample of “San’yaku
Koten”
Figure 2. Daily chart of FB as a sample of “San’yaku Koten”

Figure 3 is a daily chart of 3M. (1) to (3) are the same sign;
however as of (4), when the Lagging span is above the Resistanceband, the momentum is often stronger. And, without falling to
the baseline, the Conversion line provides continue to support
price as strong market.

Wave Structure Principle
First of all, the wave structure principle is very simple.
Hosoda classified the wave patterns that appear in financial
markets into a number of groups according to their wave
structure and gave them unique names. I wave is a single
rectilinear or straightish (normally sharp) thrust up or down
without notable corrective moves. V wave consists of two
successive I waves, a sharp thrust up followed by a sharp thrust
down, or a sharp thrust down followed by a sharp thrust up. N
wave is an up-down-up or down-up-down wave. This is the wave
pattern most commonly seen in the market.
When N wave extends to nine waves, it is judged that theuptrend is close to the limit. And when the price falls below
the previous low, the uptrend terminates with the top-forming
pattern.

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2021 EDITION

FigureWavestructure
Figure 5.5.
Wave
Structure
Principleprinciple

Price Level Principle
In ichimoku, there are four basic projection methods asshown below.
N projection -- Up: N = C + (B - A) Down: N = C - (A - B)
[Figure7]* These two equations are effectively the same, but I am
showing both as I believe this makes it intuitively easier forreaders to understand for. The same applies to the following.
• Up: N projection adds the distance of the last upleg to the lastlow.
• Down: N projection subtracts the distance of the last downleg
from the last high.

Figure 6 is a daily chart of the Nikkei Stock Average fromMarch 19, 2019, to January 28, 2020.
• The wave from A to B is an “I wave.”• The wave from B to C is also an “I wave.”• The wave from A to C is a “V wave.”• The wave from A to D is an “N wave.”• The wave from D to K is an “N wave” structured upward wave.
The uptrend would terminate if the price falls below the low J.
FigureDaily
chart
theStock
Nikkeistock average
Figure 6.6.
Daily
chart
of the of
NikkeiAverage

I

G
A
C

E

Up: N = C + (B - A)

N projection
N

B

=

K

Down: N = C - (A - B)

A

=

=

C

=
C

B
N

A

H

J

F
B

Figure 7.
Figure7. NNProjection
projection

D

V projection – Up: V = B + (B - C) Down: V = B - (C - B) [Figure 8]• Up: V projection adds the distance of the last downleg to thelast high.
• Down: V projection subtracts the distance of the last uplegfrom the last low.
This is the target to sell half of the position.
Figure8. V Projection
A 8.
FigureV projection

Up: V = B + (B- C)

E

V
B

Down: V = B - (C - B)

V projection
A

=

C

=

=

C

B

=
V

A

The target to sell half of the position

E projection – Up: E = B + (B - A) Down: E = B - (A - B) [Figure 9]• Up: E projection adds the distance of the last upleg to the lasthigh.
• Down: E projection subtracts the distance of the last downleg
from the last low.
This is the target to sell all the positions.

PAGE 78 IFTA.ORG

IFTA JOURNAL
Figure 9. E Projection
Figure9. E Projection

Up: E = B + (B - A)

E projection
E

B

Down: E = B - (A - B)

A

=

=

=
B
C

C

2021 EDITION

and lows) are used (Figure 12). When the market is about tomake a big move up, typically, those distances are multiplied byfour to project higher targets.
Figure 12.
Fourtimestimes
higher ishigher
the targetisinthe
a bigtarget
move up in a big move up
Figure12.
Four

3E projection

Up: 3E = B +3 (B - A)

3E

=

=

A

E

=

The target to sell all the positions

NT projection -- Up: NT = C + (C - A) Down: NT = C - (A - C)
[Figure 10]• Up: NT projection adds the distance between the last two lows
to the last low.
• Down: NT projection subtracts the distance between the last
The thetargetsell all the positions
two highs fromlasttohigh.

B=

Down: 3E = B -3 (A - B)
A

2E

=
B

E

=
=
=

=
A

C

E
2E

C

3E

FigureDownward
wave of Amazon
Figure 13.13.
Downward
wave of Amazon.com

Figure 10. NT Projection

Figure 10. NT projection
Up: NT = C + (C - A)

NT projection
NT

B

Down: NT = C - (A - C)

A

=C

=
C

=

A

B

=
NT

Figure 11 is a daily chart of Wisesoft, which is listed on theShenzhen market in China. In a short-term wave, a high wasreached at N projection of 18.77, and a correction was reachedat E projection of 13.72. And then, it was reached at V projectionof 16.35 with a smaller upward N wave, and a correction wasreached at N projection of 12.7. Currently, you can see a larger
downward N wave because it was below the previous low.
Figure11.
Dailyof Wisesoft
Figure 11.
Dailychartchartof Wisesoft

Figure 13 is the downward wave of Amazon.com in thesecond half of 2018. The lower targets are multiplied by fourin the initial range. When N wave extended to nine waves, thedowntrend was over.
Habitual Price Range (applied type)
There is also an applied type that uses past price range topredict future price range (Figure 14). This is the idea that theprice range from A to B and from C to D is the same.
FigureHabitual
price range
Figure 14.14.
Habitual
price range(appliedtype) (applied type)

In addition to the four basic projection methods (N, V, E andNT projection), it is also effective to use the middle-point of eachprojection.
Moreover, to project higher-degree targets, using the fourbasic projection methods, whole-number multiples of the
distances used above (e.g., distances between previous highs

Figure 15 is a daily chart of ELSWEDY ELECTRIC, which islisted on the Cairo market. Although V, N, and E projections
can be seen in various places, the left and right range excludingintermediate waves are almost the same.

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2021 EDITION

FigureDaily
chartof ELSWEDY
ELECTRIC
chart of
ELSWEDY
ELECTRIC
Figure 15.15.
Daily

Time Principle
The third subject is the time projection. One striking
characteristic of the Ichimoku theory is the degree ofimportance it places on the time factor. Hosoda taught, “It is notthat time merely passes as prices fluctuate in the market. Timeinfluences the market. The market is dictated by time.”
Ichimoku calculates “Reversal dates” in the following “Basic
number”-based projection and “Time parity”-based projection.
These two methods can be used separately or simultaneously.

a) “Basic number”-based projection
First, I explain “Basic number”-based projection. Following(Table 1) are the “Basic numbers” to be used with period data
as default parameters. In addition to that, there are 83,97,101,
etc. Some Ichimoku researchers claim that they have found that
5,13,21 should be added as Basic numbers when dealing withweekly data.
Table
1. “Basic
numbers”
bedataused with period data
Table 1. “Basic
numbers”
to be used with toperiodBasic number Comments
9‐Useful for calling intermediate tops/bottoms
17‐Useful for calling intermediate tops/bottoms (9+9-1)

33‐Particularly useful in down markets (17+17-1)

26
42
51
65
76

129
172

‐First term, Useful in up markets (9+17)‐Very important in both up and down markets (17+26-1)‐Second term (26+26-1)

Reversal dates are the dates on which the market is projected
to “reverse” directions at relatively high probabilities. However,
the market does not always “reverse” on a Reversal date. In astrongly trending market, the existing move sometimes simply“accelerates” instead of “reverses” on a Reversal date. This
happens more often in a down-trending market than in an
up-trending market. Suppose there is a market that has beenmoderately declining into a Reversal date. If it cannot reverse
direction during that time window, oftentimes it starts falling
sharply.
Figure 17 is a daily chart of CIMB Group, which is listed on the
Malaysian market. It shows that the market direction changes
when it reaches from a major low to a Basic number or a near
Basic number.
Figure
Daily
CIMB Group
Figure 17.17.
Dailychart chartof CIMB ofGroupb) “Time parity”-based projection
Second is “Time parity”-based projection. In this method,
Reversal dates are projected by adding the same time distance
between two key dates in the past to the high or low on which
the trend reversed, from which to project into the future. Figure
18 illustrates this.
Figure“Timeparity”-based
Figure 1818.
“Time
parity”-based
projection projection‐More useful in up markets than in down markets (33+33-1)‐Third term, More useful in up markets than in down markets (26+26+26-2)‐More useful in up markets than in down markets (65+65-1)‐More useful in up markets than in down markets (33+65+76-2)

Reversal dates are projected by adding “Basic numbers” tothe high or low on which the trend reversed. Figure 16 illustrates
this.
Figure“Basicnumber”-based
projection
Figure 1616.
“Basicnumber”-based
projection

1. Add the time distance (the number of the days) between thehigh C and the low D to the date of the low D into the future.
2. Add the time distance (the number of the days) between the
low B and the low D to the date of the low D into the future.
3. Add the time distance (the number of the days) between thehigh A and the low D to the date of the low D into the future.
4. Add the time distance (the number of the days) between thehigh A and the high C to the date of the low D into the future.

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Basically Nine Patterns of “Time Parity”
There are basically nine patterns of taking Time parity
numbers. Of these, three are shown in the circles(Figure 19). The
left one means that the first I wave determines the V wave in thenext identical period. And the right one means that the N wave
determines the V wave in the next identical period.

2021 EDITION

correction, the uptrend will continue until the coincidence pointof time and price is reached.
Figure21.
Coincidence
ofpricetime and price
Figure 21.
Coincidence
point ofpoint
time andFigure“Timeparity”-based
Figure 1919.
“Time
parity”-based
projection projection

Figure 20 is a daily chart of SIDI KERIR PETROCHEMICALS(Sidpec), which is listed on the Cairo market. Starting from a
low in May 2017, one would notice that the Time parity-based
projection worked well to call market turns. Also, one would
notice that the numbers are close to the Basic numbers. To be
sure, separately run Basic number-based projection and Time
parity-based projection often converge.

Figure 22 is a daily chart of ORIENTAL WEAVERS as a sampleof the N projection. On the left side of the chart, the coincidence
point of the number of the time parity 54 and the N projection in
the N wave was the Reversal date for the upward wave. On theright side of the chart, the coincidence point of the Basic number
65 and the N projection in the N wave was the Reversal date forthe next upward wave.
Figure
22.
Daily
chart
of Oriental
Figure 22.
Dailychart of
ORIENTALWEAVERS Weavers

Figure
20.
Daily
chart
of SidiKerir Petrochemicals
Figure 20.
Dailychart of
SIDI KERIRPETROCHEMICALS

I hope that you all have understood the relationship between
the “wave structure,” “price level,” and “time” principles.

Forecasting Major World Indices
Coincidence Point of Time and Price Is theImportant Reversal Date
Finally, I’d like to summarize the main point. Determine
important Reversal dates such as reversal or acceleration byrecognizing the coincidence point of the price target on thevertical axis and the time on the horizontal axis.
For example, in the upper diagram in Figure 12, thecoincidence point of the price target calculated using N
projection and the Basic number 76 from the low is theimportant Reversal date.
In the lower diagram in Figure 12, the coincidence point of theprice target calculated using 2E projection and the Time parity
number 81 from the low is the important Reversal date. “81” isthe number of days in 29 and 53 V waves in the past N waves.
It means that the time parity number 53 does not coincidewith price projection. Therefore, one should judge that after

Dow Jones Industrial Average, Monthly
Figure 23 is a monthly chart of the Dow Jones Industrial
Average. In July 2019, a high was reached at N projection of
27,251 dollars in N wave from March 2009. After that, the stock
price dropped sharply; however, San’yaku Koten continued. And
the conversion line has accelerated in October 2019; therefore,
the market became more bullish. As a result of responding tochanges in the conversion line, November 2019 was a significant
acceleration point, as it was the month with the time parity 12
and the basic number 129.
And the 2V projection of 29,654 dollars from a low in 2009 wasreached in February 2020. The February high was close to time
parity number 25 from the January 2016 low to the January 2018high. It is a basic number 26 in the first place. This means thatthe time and the price did coincide. After that, the stock pricedropped sharply. If the market can stay San’yaku Koten after the
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2021 EDITION

correction, one should judge that the uptrend will continue until
the coincidence point of time and price is reached again.
Basically, the points where the trend is likely to change are
the timing when the Lagging span touches the stock price. If
the Lagging span falls below the stock price, a bearish sign isconfirmed. However, as with the 2016 correction, if the Lagging
span jumps to support the stock price at that time (January2014), the trend will be further strengthened.

Figure 24. Monthly chart of the Shanghai composite
Figure 24. Monthly chart of the Shanghai composite index
index

Figure 23. Monthly chart of Dow Jones Industrial
Average
Figure 23. Monthly chart of Dow Jones Industrial Average

If it exceeds the high in February 2020, it will be the ninth
wave. The upside target is E projection of 30,232 dollars fromthe low in 2009. Also, there is V projection of 32,190 dollars in
the period after 2018. This is an important target that consistent
with 3E projection of 32,097 dollars from the low in 2009. And
the most important timing on the time projection is from Julyto October in 2021, from January to April 2022, December 2022,
and June 2023.
Shanghai Composite Index, Monthly
Figure 24 is a monthly chart of the Shanghai composite
index. Look at the long-term N wave from a low in 2008. The
coincidence point of 81 months close to the Basic number 83
and the middle-point(VE:5199) of E and V in the N wave was thereversal month for the upward wave.
Currently, it is moving in the downward wave from a high in2015. In the short term, the Conversion line may go above thebase line; however, it is not just San’yaku Koten. That is, thedownward wave can be predicted to continue. In that case, thecoincidence point of the time parity 81, 93, 97 and V projectionof 1,689 in the N wave from a high in 2015 is likely to be an
important reversal month for the downward wave. In addition
to focusing on the move from a high in 2015, the time parity 13,
32, 37, 44 may also be important as the time on the horizontal
axis. The downside target is V projection of 1,689 in the N
wave from a high in 2015 as well as N projection of 2,141 and V
projection of 1,592 in the N wave from a high in 2018.
If it exceeds a high in 2018 with San’yaku Koten, it is likely tolead to a larger N wave starting from a low in 2013. In this case,the month coming with the time parity 32, 37, 44, 68, 81, 93, 97
will be the important reversal month for the upward wave. And
it is considered that the upward wave will continue until the
N projection of 5,698 or the level of 4,909 obtained using thehabitual price range.
PAGE 82 IFTA.ORG

EGX30 Index, Weekly
Figure 25 is a weekly chart of the EGX30 Index. Currently,
it is a small-scale rebound from the low in December 2018. If
it exceeds the high in February 2019, N wave from the low in
December 2018 can be confirmed. And you can predict N(16457)
and E(18566) upward projections. In addition, it is important topredict the middle-point (17511) of N and E projections. After the
short correction, if it exceeds the high in April 2018, a larger N
waves starting from the low in January 2016 can be confirmed.
In that case, the price range of 12701 from the low in January
2016 to the high in April 2018 and the price range of 6338 fromthe high in April 2018 to the low in December 2018 will be an
important factor for the price projection.
However, if it falls below the low in December 2018, themedium-term N wave starting from the high in April 2018
can be confirmed. And you can predict N, V, and E downward
projections.
After 2020, what is important in the time projection is thetime parity 34 for short-term wave and the time parity 66,120
for medium-term waves. In addition, the week coming with thebasic number 76,83,97,101 from the low in December 2018 andthe basic number 129,172 from the high in April 2018 will be theimportant reversal weeks.
I have just written about the future assumptions so far;however, what I want to say is, regardless of the size of N wave,
whether it be an uptrend or a downtrend, it will continue until
the coincidence point of time and price is reached.
Figure25.
Weeklyof EGX30
Index
Figure 25.
Weeklychart ofchart
the EGX30
Index

IFTA JOURNAL

Nikkei Stock Average, Monthly
Figure 26 is a monthly chart of the Nikkei Stock Average.
When you look left and right centering on a high in February
2007 and a low in October 2008, you will notice that thetime parity numbers are major highs and lows. In this case,the months coming with the time parity 160–207 will be
the important reversal timing for the upward wave or the
downward wave.
And after a low in October 2008, what is important in the time
projection is the time parity 29,41–44,56,81 for medium-term
waves. If the price projection (N,V,E, etc.) is realized at the time
parity 29,41–44,56,81 for medium-term waves, this will be theimportant reversal month.
FigureMonthly
ofStock
Nikkeistock average
Figure 26.26.
Monthly
chart ofchart
the Nikkei
Average2021 EDITION

General Disclaimer

This report has been produced by the author for informational
purposes only. It does not constitute solicitation of the sale orpurchase of securities or other investments. The information
contained herein is derived from sources that that author
believes to be reliable, but the author does not guarantee theaccuracy or completeness of said information. In some cases,
such information may be incomplete or summarized. Prices,
numbers, and similar data contained herein include past results,
estimates, and forecasts, all of which may differ from actual
data. These prices, numbers, and similar data may also change
without prior notification. This report does not guarantee
future performance, and the information contained hereinshould, for whatever purpose, be used solely at the discretion
and responsibility of the reader. It is prohibited for this report tobe provided or redistributed to any third person, or modified orchanged in any way.

IFTA.ORG PAGE 83

IFTA JOURNAL

2021 EDITION

Stock Trends and Trend-Based Trading
Strategies—Backed by Large-Scale Back-Testing
Implemented by Automated Software System

c/o NAAIM Founders Award
6732 W. Coal Mine Avenue #446
Littleton, CO 80123
Phone: +1 (888) 261-0787
Web: https://www.naaim.org

By Kevin Luo
Figure 1. Trend chart sample. Source: Archer Daniels
Midland Co. (ADM)

Introduction

The term “stock trends” may evoke many bright anddark images: rising Apple stocks, comeback of the NASDAQ
Composite Index in 2014, meltdown of the financial sector in
2008, and more. What are stock trends? Do all investors who
use stock trend analysis in their investment decision-making
process have a clear view of their stock trends? It can always beargued if stock trends should be vaguely or precisely defined.
It cannot be argued that vagueness creates gray areas. Gray
areas grow uncertainties that can lead to serious risk problems.
This paper describes and discusses a research project in whichstock trends were quantitatively defined with a single factor,
isolated, analyzed, and then summarized. The method is easy toapproach. Its results are precise and black/white. The paper also
demonstrates applications of the trend analysis with introductionof two trading strategies formulated based on a trend technique.

Trend Definition and Description

Edwards and Magee (2007) have developed some exclusive
frameworks for trend analysis based on the Dow Theory.
According to Edwards and Magee, there are two decisive
factors in trend determination. They are price movement andtime frame of such price movement. In terms of “primary
trend,” it is “appreciation or depreciation in value of morethan 20%” and “last for a year or more” (Technical Analysis of
Stock Trends, Edwards and Magee, 2007, P15). This approachprovides some ultimate leads for practical applications ofstock trend analysis. Among the two trend decisive factors,
price movement is obviously independent and time frame
is dependent. Time frame is an important factor; however,it sometimes creates confusion. Questions would definitely
be raised when a market declines 20% and then climbs rightback in less than one year. Do we classify the price movement
as a stock trend? Can money be made or lost during the pricemovement just like in a “qualified” primary trend? Would
trend analysis still work without the time frame? A research
project was conducted to isolate and analyze stock trends
without the time frame factor. In the project, two types oftrends were defined. Uptrend is recognized in case prices move
upward more than 20% and downtrend is recognized in caseprices move downward for more than 20%. The definitions
are practically identical to the condition of “appreciation
or depreciation in value of more than 20%” by Edwards andMagee. A sample trend chart is exhibited in Figure 1.

PAGE 84 IFTA.ORG

The close prices in green belong to the uptrends and theclose prices in red belong to the downtrends. Each trend has its
beginning and ending prices, which are jointly used by linking
trends. The numbers (in 100%) on the chart represent trend
sizes of the respective trends. Trend size is the vertical distance
between beginning and ending levels of a trend divided by the
beginning or ending price. Using the term “trend size” is anattempt to differentiate the concept from existing terms suchas “trend length,” which factors in time frame. In terms ofequations, trend sizes are:
1. Uptrend Size = (Ending Price – Beginning Price) / BeginningPrice
2. Downtrend Size = (Beginning Price – Ending Price) / Ending
Price
Please refer to Appendix One of the Appendices Section for
details regarding the difference of uptrend and downtrend
calculations.

Trend Isolation and Trend
Characteristics Analysis

To prove that the trend analysis with only price factor
still works, the project was designed to isolate stock trends
from a large number of stocks and then summarize trend
characteristics, which are in turn to be used in the trading
strategy development. If the developed strategies work, thetrend analysis works. As the larger scale leads to increased
precision and reliability, 1,816 random stocks were selected forthe research. These stocks were all listed on NYSE and NASDAQ.
Standard daily price data (open, high, low, and close) was used.
Data range covered an approximately 10-year period of timefrom January 1, 2005 to September 1, 2014. Tasks of the projectsuch as trend isolation, characteristics summarization, backtesting, and graph generations were automatically implemented
by a custom-designed software system (the software).

IFTA JOURNAL

In the trend isolation process, the software separated 88,353
stock trends from 1,816 stocks. Figure 2 shows the average trend
sizes by different price range groups. On average, the uptrend
size measured 48.29% and the downtrend size measured 45.37%.
Figure 2. Average trend size (%) by stock price group

The study found no evidence that the cheaper stocks produce
the larger trends; however, as shown in Figure 3, the cheaper
stocks did generate larger trend counts than the expensive
stocks. The stocks priced under $5 produced approximately
three times more uptrends and downtrends than stocks priced
over $40 on average. This probably means that the cheaper
stocks tend to move up and down more frequently. Therefore,
it is reasonable to conclude that the average uptrend size of
48.29% and downtrend size of 45.37% represent the stocks in all
price ranges.

2021 EDITION

sections. Among interpretations of this finding, one stands out
certain and clear. There must be good trading opportunities
for the remaining section of the typical trend. The remaining
section ends when the trend reverses. For the typical uptrend, it
covers 28.9% of the uptrend (after the confirmation section) and
20% of the following downtrend (or the downtrend confirmation
section). Trading strategies may be developed targeting the
28.9% in the uptrend or theentire remaining section. In this research project, twosample trading strategies were formulated targeting theentire remaining section. The remaining section is labelled
as “trading range” in this paper for convenience. A sample of
uptrend trading ranges (gray-shaded sections) is displayed in
Figure 5. Next, the paper details the strategy development andback-testing of two trading strategies. The back-testing of thestrategies determines effectiveness of the trend analysis.
Figure 5. Uptrend trading range sample. Source: Archer
Daniels Midland Co. (ADM)

Figure 3. Average trend count by stock price group

Trading Range Strategy

In practice, a trend is not recognized until it is confirmed.
The confirmation requires a stock to move a minimum of 20%
in a direction. The confirmationportion of the trend is called“confirmation section” in this paper. In Figure 4, the sample
uptrend confirmation sections are covered by the green shades
and the downtrend confirmation sections are in the red shades.
Figure 4. Trend confirmation section sample. Source:
Archer Daniels Midland Co. (ADM)

Strategy
In this paper, trading strategy is defined as a set of methods
or techniques that can be independently and repeatedly used in
trading operations.
Trading Range Strategy was formulated based on the trend
characteristics obtained from the trend analysis. The strategy
aims to maximize per-trade profit with a few simple rules. As
stated, the average uptrend is measured at 48.29%, which ismore than twice larger than a confirmation section. One of thestrategy ideas was to take and hold a position for the whole
trading range. Figure 6 illustrates a sample trade of the strategy.
The blue line connects buy and sell points. It is important to notethat this strategy only allows trades in the trend directions.
Only long trading is discussed in this paper.
Figure 6. Trading Range Strategy trade sample. Source:
Archer Daniels Midland Co. (ADM)

One of the important findings in the research is that stocks
tend to move substantially further beyond confirmation
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2021 EDITION

Mathematically, after taking 40% out of both confirmation
sections, there is still 8.29% left. This 8.29% is the theoretical
per-trade profit for the strategy; however, assuming 8.29% asa figure close to the practical profit is very risky because thetrading is in the same directions with the stock trends. In manycases, price actions such as open-gaps would easily wipe out
all of the profits. In Figure 7, the sample shows the impact of an
open-gap at the entry. Without the open-gap, the entry would
have been recorded at the projected entry price (orange-line
level). With the open-gap, it cost around $1.50 more to get in
the long position (green line level). According to the statistics,
32.43% of trades were affected by open-gaps. Sometimes the
open-gaps are very large. Skipping all open-gaps that cause
higher than projected entries is a practical alternative; however,such attempts often end up losing good opportunities at thesame time.
Figure 7. Open-gap impact on entry price sample. Source:
Akamai Technologies Inc. (AKAM)

It is possible but unlikely for a simple strategy like Trading
Range Strategy to work well on all stocks and all the time. It
is necessary to select some suitable stocks from the random
stocks for the back-testing. In theory, the larger the trend is,
the greater the profit is. The suitable stocks for this strategy
are considered as those with the larger trends. To confirm thetheory, the research selected the top 500, top 200, and top 50
stocks at the top of a trend size sorted list. The back-testing
generated summary statistics separately for the portfolios.
These trading portfolios are labelled as Top-500 Portfolio,
Top-200 Portfolio, and Top-50 Portfolio. The stock prices in theportfolios average $21.81, $19.94, and $13.83 for the Top 500
Portfolio, Top 200 Portfolio, and Top 50 Portfolio, respectively.
The average trend sizes are 51.67%, 54.13%, and 56.12% for therespective portfolios.
Back-Testing
Back-testing is one of the most effective approaches for new
strategy evaluations. In the back-testing of this project, thesoftware tests each of the stocks individually by generating
trades according to the trading rules and then summarizes thetest outputs by the trading portfolios. No costs or volumes arefactored in.
There are a couple of rules for the test:
1. Enter a long trade at the first available price after each of theuptrends is confirmed.
2. A trade exits at the first available price after the uptrend
reverses or at the end of the test. Close price is used for
liquidating a position at the end of the test.
PAGE 86 IFTA.ORG

A set of test trade samples are graphically presented in Figure
8. The blue lines connect buy and sell points of the trades.
Figure 8. Trading Range Strategy back-testing sample
output. Source: MicroStrategy Inc. (MSTR)

After implementation of the test tasks, the software
produced some summary statistics for evaluation. The
computation approach and equations are discussed in Appendix
Two of the Appendices section.
Back-Testing Outputs
The software generates a set of performance statistics,
including cumulative P&L, drawdown (graphically presented
in charts of the Figures), average annual return, average pertrade return, Sharpe ratio, and max drawdown (in footnotes ofthe figures). In Figure 9, the summary shows that the strategy
generated a 16.90% average annual return for the Top 500
Portfolio. The per-trade returns averaged at 3.95%, which ismuch smaller than the theoretical figure of 8.29%. The Sharpe
Ratio was computed based on annual returns. The figure wascalculated as 0.54. On the risk side, the max drawdown wasrecorded as 48.00%. Surge of the cumulative return in the endof chart was resulted from liquidating all positions described inRule 2 of the back-testing. The software books profit/loss of thetest trades at the exits.
Figure 9. Back-testing performance (500 stocks);
Trading Range Strategy back-testing.

For the Top 200 Portfolio, the average annual P&L arrived
at 25.90% which, as expected, is approximately 50% higher
than the return from the Top 500 Portfolio (Figure 10). The
average per-trade return is also higher by approximately 35%
at 5.32%. The Sharpe Ratio and max drawdown are and 44.50%,
respectively.

IFTA JOURNAL

Figure 10. Back-testing performance (200 stocks);
Trading Range Strategy back-testing

In Figure 11, the test performance for the Top 50 Portfolio ispresented. The strategy worked better for this portfolio than
the Top 200 Portfolio by 42%, as the average annual return
reached 37.00%. The per-trade return of 6.11% is also greaterthan the 5.32% of the Top 200 Portfolio. The Sharpe Ratio is 0.77
and the max drawdown is 47.10%.

2021 EDITION

Using a stock index as the benchmark for the performancecomparison may cause concerns in this case because thisstrategy involves frequent trading; however, such comparison
does make some sense at this level of evaluation. This researchputs performance statistics of the back-testing and two stockcomposite indices together for the comparison and evaluation.
The two stock composite indices are NASDAQ (^IXIC) and NYSE(^NYA) of which the stocks in the back-testing belonged to.
In comparison of the cumulative returns, it appears that theportfolios outperformed the indices most of the time (Figure13). The slopes of the cumulative return curves for the portfolios
could have been adjusted steeper upward for 2012–2014 if theunrealized profits (the pop-up amounts in the end of the curves)
were accounted for.
Figure 13. Test performance comparison; Trading Range
Strategy vs. indicesFigure 11. Back-testing performance (50 stocks); Trading
Range Strategy back-testing

Evaluation
Evaluation of the Trading Range Strategy focuses on the backtesting performance, including returns and risks. Comparing
returns among thethree portfolios can determine if there isa tendency for stocks with the larger trends to outperform
other stocks. As shown in Figure 12, the Top 200 Portfolio
outperformed the Top 500 Portfolio and the Top 50 Portfolio
topped the Top 200 Portfolio by visible amounts consistently
throughout the years. It confirms that there is a positive
relationship between the stock trend size and the performancefor the strategy. Knowing this relationship is helpful in future
optimization and trading operations.
Figure 12. Test returns by portfolio and year; Trading
Range Strategy back-testing

Observations from Figure 13 (or Figures 9–11) also indicatethat the strategy took some large losses. The drawdown
readings reached as low as 48% for the Top 500 Portfolio. Those
drawdown figures were primarily driven by the substantial
declines of overall markets during 2008–2009. Under suchmarket conditions, the uptrends shrunk and the larger open
gaps at the exits pushed the returns further in the red. There isno easy fix for this issue unless adding managerial techniquessuch as long/short strategy or optimizing the trading model.
On the bright side, the losses were not as bad as the indicessuffered. The indices have carried up to 74% and 85% drawdowns
throughout the period of time. This resulted from the trading on
the uptrends only. The stocks have stayed on the sideline during
the larger-than-average downtrends.

MACD Strategy

Back-testing results of Trading Range Strategy have provedthat there are good opportunities for trading range in thetrend analysis. Now the question is to what extent this trend
technique can be applied. Here is another example strategy
that connects trading range with one of the popular technicalindicators, Moving Average Convergence/Divergence (MACD).
This strategy is called MACD Strategy because MACD is the mainbase of the strategy.
Created by Gerald Appel, MACD is considered a simple andeffective momentum indicator. MACD signal line crossover isa method showing bullishness and bearishness of the prices. A
bullish crossover occurs when the MACD line rises and crosses
the signal line from below. A bearish crossover happens when
the MACD line falls and crosses the signal line from above.
According to statistics obtained from the trend analysis of
IFTA.ORG PAGE 87

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2021 EDITION

the 1,816 random stocks, the average price changes (calculated
as Close today – Close Prior in percent) are 2.18% and -1.92%
for the bullish and bearish crossover days, respectively. Thesefigures are considered significant when compared to 0.44% and-0.55% of price changes for the typical uptrend and downtrendday. It appears that MACD timely signals bullish/bearish pricemomentums. Can the signals be used as trading signals forlong/short stocks? A back- testing was conducted to find theanswer to the question. Test rules for long trades were buying atbullish crossover and selling at the following bearish crossover.
The rules for short trades were shorting at bearish crossoverand covering at the following bullish crossover. Close pricesof the signal days were used as entry and exit prices. A timing
sample is exhibited in Figure 14. The up arrow points to a bullish
crossover and the down arrow points to a bearish crossover.

Figure 15. MACD strategy short trades. Source: AlleghenyTechnologies Inc. (ATI)

Figure 16. MACD strategy short trades with trading
range. Source: Allegheny Technologies Inc. (ATI)

Figure 14. Sample MACD signal line crossover timing

Back-testing of MACD signal line crossover was implemented
by the software that generated long or short trades for eachcrossover of the stocks. The average annual return for the longtrades is 1.45% but the short trades lost (15.94%) annually on
average. A finding of this research suggests that there are alarge number of short trades in the uptrends and long trades inthe downtrends. MACD is a good momentum indicator as thestatistics suggest; however, the momentum signals generated
by MACD are probably too frequent for a normal low-frequency
trading strategy.
When trading against the trends too often over time, theprofits are expected to be poor. Trading range is supposed to becapable of reducing some of the unfavorable trades to improve
the performance. Another back- test was conducted with thetrading range technique factored in. A couple of new rules wereadded.
1. MACD long trades are only allowed in uptrend trading ranges
while short trades are only allowed in downtrend trading
ranges.
2. At the end of the trading ranges, all holding positions are
liquidated at the close price of the day.
Impacts of the trading range technique can be easily observedby comparing sample test trades shown in Figure 15 and Figure 16.
Figure 15 indicates the short trades generated without trading
range for Allegheny Technologies Inc. (ATI). In Figure 16, thechart shows the short trades for ATI with trading range added.
The trade count reduction is significant and the performance
improvement from -10.71% to 7.30% is impressive.

PAGE 88 IFTA.ORG

The summary of the second test shows that trading range
improved the long trading return by 9.97% and the short trading
return by 43.71% for the 1,816 random stocks. For the Top 200
Portfolio, the average annual returns were recorded as 16.82%
for the long trades and -8.34% for the short trades. The test
results are strong enough to conclude that the trend technique
can be effectively incorporated with external strategies for
performance improvement.

Conclusion

Success in testing the strategies suggests that stock trends
defined with price factor alone worked effectively. The trend
characteristics analysis offers valuable statistics for many
areas of the investment decision-making process. It is easy
to approach and does not require a great deal of effort tounderstand. Outputs of the trend analysis are precise. There isno gray area of confusion. From a strategy design standpoint,
the trend analysis offers great flexibilities. Stock trends canbe isolated into major, intermediate, minor, or custom-defined
trends to meet user needs. There is huge potential for thetechniques to grow in new strategy development and existing
strategy improvement.
Trading Range Strategy is one of the simplest but mosteffective trading strategies. It is straightforward and easy tofollow. There is large room for optimization of the strategy.
Through the introduction of MACD Strategy, this paper has
shown how effective the trend techniques can be in termsof incorporating with an external strategy. Results fromthe separated projects have confirmed that trading range
technique also worked well with some other technicalindicators such as RSI.

IFTA JOURNAL

Appendices
Appendix A
Conventional calculation of downside percentage works forthe majority of situations, but not the downtrend calculationin this research. The easiest way to describe the problems is toshow an example (Figure A1).
Figure A1. Downtrend calculation method comparison
Source: Archer Daniels Midland Co. (ADM)

2021 EDITION

For the purpose of comparison, test results are also calculated
conventionally. It utilizes equal-weighted approach, in which$1,000 is assigned to every trade regardless of share prices. The
related equations are listed below.
1. Number of Shares (Trade) = $1,000 / Entry price
2. P&L (Trade) = Exit Price * Number of Shares - $1,000
3. Daily P&L (Portfolio) = P&Ls (All Daily Completed Trades) /Portfolio Value (100%)
4. Cumulative P&L (Portfolio) = (Cumulative Daily P&L (Prior to A
Given Day) + Daily P&L (A Given Day)) / Portfolio Value (100%)
5. Average Annual Return (Portfolio) = End-Test Cumulative P&L /Years (100%)In this case, the stock starts to move lower after reaching
the uptrend (green bars) high. By the conventional method,which is expressed in Equation 1 at the end of this paragraph,the uptrend would not reverse until the stock reaches
approximately $36 (red line level in chart). That level issignificantly lower than where the uptrend begins. It isreasonable to assume that this is a major concern for the
majority of the trend users. To fix the problem, this research
employed an alternative method which utilizes an easy equation(Equation 2). With this equation, an uptrend reversal level
never falls below its beginning level anymore (blue line levelin Figure A1). More importantly, the alternative method makes
the trend analysis more meaningful. Under the convention, themaximum size for a downtrend is 100%. It is not comparable
and combinable with an uptrend because the uptrend canbe measured up to infinity percent. Without the alternative
method, simple strategies such as Trading Range Strategy
would have been more difficult to formulate. In this research, allstatistics tied to downtrend measures were computed based on
the alternative method.
1. Downtrend-Size (Convention) =(Ending Price – Beginning Price) / Beginning Price (100%)
2. Downtrend-Size (Alternative) =(Beginning Price – Ending Price) / Ending Price (100%)
Appendix A
Back-testing returns are expressed in two different ways.
The first one is average per-trade method, which is verystraightforward. The equations used in the calculations are
exhibited below.
1. Per-trade P&L =(Exit Price – Entry Price) / Entry Price (100%)

Because there were no cost or volume factors in theperformance computation, the process was simplified by using
a fix value as “Portfolio Value.” “Portfolio Value” is computed
based on stock counts in a portfolio. Trading a portfolio ofstocks requires less capital to operate than holding the same
stock portfolio. It is unreasonable to use full counts of stocks
for “Portfolio Value” computation in this case. The chart in
Figure A2 shows the stock holding counts for the back-testing ofTrading Range Strategy.
Figure A2. Number of holdings—Top 500 stocks; Fixed
portfolio value calculation

The less it holds, the smaller the capital needed to operate.
For this research project, the 95th percentiles were used for“Portfolio Value” calculation. In this particular example, the95th percentile (red line level in Figure A2) is 420 counts.
“Portfolio Value” is therefore calculated as $420,000($1,000*420). $1,000 is the amount assigned to each trade.
The figure is smaller than $500,000 ($1,000*500) for a holding
portfolio.

References
Robert D. Edwards and John Magee (2007), Technical Analysis of Stock
Trends, 9th edition
John J. Murphy (1999), Technical Analysis of the Financial Markets
Christian L. Dunis, Jason Laws, and Patrick Naim (2003), Applied
Quantitative Methods for Trading and Investment

2. Average per-trade P&L (trading portfolio) =sum of all per-trade P&Ls (all stocks) / Number of Trades (allstocks)IFTA.ORG PAGE 89

IFTA JOURNAL

2021 EDITION

Simple and Effective Market Timing
With Tactical Asset Allocation
By Lewis A. Glenn, Ph.D.1

Abstract

A simple market timing algorithm is examined that switchesfrom an exchange traded fund representing
U.S. equities to one holding treasury long bonds every monthon the last day, the switch being made to whichever ETF has
the greatest ratio of current adjusted closing price to adjusted
closing price µ months earlier. The parameter µ is determined so
as to maximize total return and minimize the total number of
trades, however the results are relatively insensitive to µ over afairly wide range. The performance of this scheme is comparedto that of an Ivy 5 portfolio consisting initially of equal dollar
amounts of ETFs in U.S. equities, foreign large blend, 7–10 year
treasuries, real estate, and commodities. As with the paired
switching approach, each ETF is purchased only once a month,
on the last day, in this case only if its adjusted closing priceexceeds the 10-month simple moving average (SMA). Otherwise,that portion of the portfolio is invested in a cash surrogate.
Comparison is made over the 10-year period ending on
12/31/13. It is shown that the average annual return of thepaired switching algorithm exceeds 30% in this period, whichis three times greater than that of the Ivy 5. Moreover, only
45 trades were required for the paired switching approach,whereas the Ivy 5 required 70 in the same period. The maximum
draw down was 14.6% for the Ivy 5 and 18.8% for paired
switching.

Introduction

The so-called weak form of efficient-market theory (EMT)
holds that future stock prices cannot be predicted on the basisof past stock prices2. Many advocates of this theory believethat the best strategy for the long-term investor is to “buythe market,” by which they generally mean to invest in an
index fund that represents all, or a significant segment of, theequity market. The claim is that past performance has shownthat in the long run buying and holding this class of asset will
outperform any active management scheme.
One problem with this approach is the definition of the term“long run.” To paraphrase the famous British economist John
Maynard Keynes, “…in the long run we’re all dead.” In fact, many
investors would consider a 5–15 year investment as long-term,
whereas some would choose a period as long as 30 years andothers as short as six months. A brief look at the large cap
growth ETF (QQQ) that tracks the NASDAQ 100 (which consistsof the largest non-financial securities listed on the Nasdaq Stock
Market) exhibits what can happen over a long run. An investor
purchasing this fund at its inception in March 1999 would haveseen her investment more than double in the first year. Two anda half years later, by September 2002, this would have dropped
PAGE 90 IFTA.ORG

Lewis A. Glenn, Ph.D.
Chief Scientific Officer
Creative Solutions Partners Associates
702 Park Hill Rd.
Danville, CA 94526 US
lewglenn@gmail.com
Phone: 925-820-5158

by more than a factor of five and her holdings would be down
to less than half the initial investment. It would take five more
years for this investor to recoup the original investment, notincluding the interest she would have made had she remained in
cash. Her willpower would once again be tested as she watched
the collapse of the financial markets in 2008, by the end ofwhich the value of the initial investment would be once again
halved.
It is this scenario that tactical asset allocation seeks tomitigate. The main idea is simply to diversify portfolio assets
and employ a market timing solution. But what kind of solution?
The literature is replete with different approaches. Our goal here
is not to review the many ideas that have been suggested but
rather to focus on two very simple schemes.
An especially popular method was described by Faber3 andthen further elaborated by Faber and Richardson4 as the Ivy 5
portfolio. The basic idea is to create a portfolio of five sectors
consisting of the S&P 500 index, the MSCI EAFE index, U.S. 10year government bonds, a real estate index, and a commodity
index. The initial study involved non-tradable indices over a
35-year period. The trading rule was simply to buy each index
when the monthly price exceeded the 10-month simple moving
average (SMA) and to sell and move to cash otherwise. All entry
and exit prices were on the day of the signal at the market close
and the model was only updated once a month on the last day.
Price fluctuations during the rest of the month were ignored.
Moving average methods are very useful for discerning trends
and indeed this author has suggested several market timing
algorithms based on them.5,6,7 All moving average algorithms,
however, exhibit latency, namely, they introduce some lag in thecalculation so that the MA lags the original signal. This meansthat the user will implicitly be late in leaving a declining market
and, perhaps even worse, late in entering a rising one. The lagtime increases directly with the length of the moving average.
The paired switching method to be described here does notdepend on moving averages. In general, paired switching refers
to investing in one of a pair of negatively correlated assets andperiodic switching of the position on the basis of the relative
performance of the two. Maewal and Scalaton8 proposed anespecially simple version that looked at the performance overa prior 13-week period and purchased the asset that had thehigher return over that period. That position was then held for
13 weeks at which point the cycle was repeated. They looked atseveral different ETFs that served as surrogates for equities andbonds, including SPY (for the S&P 500) and TLT (for treasuries
with maturities of 20 or more years).
Cohn9 devised a similar scheme but employed it on a daily
basis. Instead of a look back period of 13 weeks (65 trading days),

IFTA JOURNAL

he found that 84 trading days produced the best results.
In what follows we first generalize the Maewal-Scalaton
procedure by switching between SPY and TLT in the same way aswith the Ivy 5, namely once a month on the last day, the switch
being made to whichever ETF has the greatest ratio of current
adjusted closing price to adjusted closing price µ months earlier.
The parameter µ is determined so as to maximize total returnand minimize the total number of trades. We then compare
the results to those obtained with the Ivy 5 portfolio over a
recent 10-year period and exhibit the results in a novel way
that allows an investor to immediately visualize end-of-period
performance based on date of first entry (purchase) with use of
the algorithm.
The Paired Switching Strategy
First, a few definitions are in order. Let the value of thepaired switching portfolio on day n be v(n), 1 ≤ n ≤ N and thenormalized value be V (n) = v(n) / C, where C is the cost basis. For
the 10-year period ending 12/31/13, N = 2517 trading days. Also,
let t(n) be the number of trades, where either a buy or a sell is
considered a trade.
For a buy and hold (BAH) strategy with an individual ETF, thenormalized value on day n is simply V ETF (n) = PETF (n) / PETF (1),where PETF (n) is the adjusted closing price on day n ≥ 1.
Then, the paired switching rule is simply:
Move to TLT on day n if and only if
V TLT (n) / V TLT (n − µˆ) > VSPY (n) / VSPY (n − µˆ)

Equation 1

where µˆ is the daily equivalent of µ. Otherwise, move to SPY.
On all other days the previous position is maintained. Notethat daily price data on both SPY and TLT are available since
7/30/2002.
Nominal trading costs are included on all transactions, butthese costs are generally negligible with electronic trading andthe high volumes extant with these ETFs. However, slippage
is not included, and this might be significant with increasing
trading frequency.
To find the best value of µ to use, we first look to maximize
total return, V ( N ) − 1, or equivalently the cumulative
annualized growth rate (CAGR) over the entire period, χ( N ),where χ is defined as:
χ(n) = {[exp((log(V ) / (n / 250))] −1}×100

Equation 2(expressed as a percentage) and we have taken a trading year
to consist of 250 trading days. Note that since N = 2517 here,
Equation 2 evaluated at n = N will slightly underestimate thetrue CAGR in the period of interest.
Figure 1 depicts χps ( N ) , as a function of the look back period,µ. Also shown is the number of trades made. It is observed thatmaximum CAGR approaches 15% at µ = 3 months and that theperformance is relatively insensitive to µ between three andfive months. The number of trades decreases inversely withincreasing µ in this period.

2021 EDITION

Figure 1. Cumulative annualized growth rate for thepair switching algorithm for the 10-year period ending
12/31/13 as a function of look back period. Also shownon the right ordinate axis is the total number of trades
made for each value of µ .

Also of interest is the maximum draw down, defined as

For the cases shown in Figure 1, and expressed as a percentage,δmax ( N ) was 18.8% uniformly over the range 2 ≤ µ ≤ 5 months;
this value first occurred on 4/30/09.
The Ivy 5 Strategy
In applying the Ivy 5 strategy we utilize the ETFs suggested
by Faber and Richardson (FR)4 with one minor exception.
There were no broad sector commodity ETFs available on
1/1/2004, the beginning of our 10-year study period. So, we havesubstituted the PIMCO Commodity Real Ret Strat Instl mutualfund (PCRIX) for the commodity ETF recommended by FR (DBC).
The modified Ivy 5 trading rule is then:Purchase equal amounts of the following ETFs/funds on
day one if and only if the adjusted closing price exceeds the
10-month simple moving average (SMA): SPY (S&P 500), EFA(MSCI EAFE index), IEF (7–10 year treasuries), IYR (real estate),
PCRIX (commodities). Otherwise the amounts meant forpurchase are instead left in cash. Then, at the last day of this andeach following month, repeat the procedure. Price fluctuations
during the rest of the month(s) are ignored. Cash positions earninterest using IRX (CBOE index that measures the discount rateof the most recently auctioned 13-week U.S. Treasury Bill) as
a surrogate. Again, nominal trading costs are included on alltransactions.
Comparison of Paired Switching With the Ivy 5
Before proceeding, it will be convenient to slightly modify thenomenclature. We will redefine the normalized portfolio value
on day n > k as nkV where k ≥ 1 represents the day of purchase.
Figure 2 then compares n1V for the paired switching algorithmwith that of the Ivy 5 portfolio. Also shown for reference are thebuy-and-hold (BAH) values of SPY, TLT, and BAH5 (the portfolio
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consisting of initially equal dollar amounts of SPY, EFA, IEF, IYR,
and PCRIX and in which no further trades are made).
Figure 2. Portfolio value on day n,1 < n ≤ N = 2517, over the
10-year period n, beginning on 1/2/2004 and ending on
12/31/13

It is observed that N1V, the value on 12/31/13, for paired
switching is roughly double that delivered by the Ivy 5 portfolio,
although, for the first half of the 10-year period, n1V for theformer lags that of the latter by as much as 20%. The maximum
draw down over the entire period for paired switching was
18.8% as against 14.6% for the Ivy 5. By contrast, N1δmax was
55.2% for SPY, 26.6% for TLT, and 48.4% for BAH5.
Figure 2 compared results for every date in the period givena single entry (purchase) date, namely 1/2/04, the first day ofthe period. More computationally intensive, but also of moreinterest, is what an investor might gain or lose at the end of
the period for an investment made on any arbitrary date in the
period. Figures 3–6 address this question. Figure 3 compares
the total return as a function of entry date, NkV −1, 1 ≤ k ≤ N ,for paired switching with the Ivy 5 portfolio and with the threereferences in Figure3.

PAGE 92 IFTA.ORG

Figure 3. Total return on day N, the end day of the
10-year period beginning on 1/2/2004 and ending on
12/31/13 for entry date corresponding to day k,
1 ≤ k ≤ N = 2517.

The open and closed circles represent end-of-month entry
dates, corresponding to the allowable entry dates with thepaired switching and Ivy 5 rules discussed above; purchases
made at other dates are assumed to be invested in cash, without
interest, until the next end-of-month, at which point theappropriate purchase is made. It can be seen that the paired
switching scheme produces higher total return at the end of
the windowed period than the Ivy 5 independent of the entry
date. And, although an investor purchasing SPY at the end of
February 2009 and holding through 12/31/13 would have done
almost as well as with paired switching, it is highly unlikely that
he would have been able to identify the bottom of the market
that produced this result. Figure 4 shows the CAGR in the samecontext, and with similar modified nomenclature, as in Figure
3. Over the entire set of entry dates the minimum, mean, andmaximum values for paired switching are 14.4%, 20.4%, and31.9%, respectively. Note that the CAGR values after 12/31/12
are suppressed for clarity because the results are highly
volatile and not meaningful for n / 250 ≪1 in Equation 2. The
comparable CAGR minimum, mean, and maximum values for
the Ivy 5 are 1.4%, 6.6%, and 9.8%, respectively. Figure 5 shows
the maximum draw down for each of the cases in Figures 2–4
and exhibits the advantage of both paired switching and Ivy 5
schemes over buy-and-hold approaches.

IFTA JOURNAL

Figure 4. CAGR on day N , the end day of the 10-year
period beginning on 1/2/2004 and ending on 12/31/13 forentry date corresponding to day k, 1 ≤ k ≤ N = 2517

Figure 5. Maximum draw down on day N, the end day of
the 10-year period beginning on 1/2/2004 and ending on
12/31/13 for entry date corresponding to day k, 1 ≤ k ≤ N= 2517

With neither timing scheme did the maximum draw down
exceed 20%, independent of entry date but the Ivy 5 had smaller
drawdowns for most entry dates. Finally, Figure 6 depicts thenumber of trades employed (each switch counts for two tradeswith paired switching) for the Ivy 5 and paired switching
schemes. It is clear that the latter has the advantage regardlessof entry date. As mentioned earlier, even though the explicit
cost of electronic trading is now generally negligible, the costof slippage (the difference between the adjusted closing priceand the price actually obtained in a transaction) is not easily
accounted for. Slippage is not a deterministic process and, whilebounds may be established using Monte Carlo methods, this isbeyond the scope of the present study.

2021 EDITION

Figure 6. Number of trades on day N , the end day of the
10-year period beginning on 1/2/2004 and ending on
12/31/13 for entry date corresponding to day k, 1 ≤ k ≤ N= 2517

Discussion
The results presented in the previous section would appear toshow that the paired switching scheme has the advantage overthe Ivy 5 unless draw down is the main concern, in which casethe significantly increased return of the former would need to bebalanced against its modest increase in risk. One question thatarises, however, is the significance of these results in predicting
future performance. For one thing, most of the ETFs involved inboth schemes have been in existence only a short time so only arather narrow 10-year period was considered. As mentioned earlier, this problem was dealt with by Faber3 for the Ivy 5 by testing
the algorithm with non-trade-able indices over a 35-year period.
In their analysis of paired switching, Maewal and Scalaton8
employed the statistical procedure devised by Henriksson andMerton10 to evaluate the significance of their results. They alsosuggested a similarprocedure to that employed by Faber for the
Ivy 5, in this case the use of surrogate mutual funds—VFINX for
the S&P 500 and VUSTX for the treasury long bond. The former
has been in existence since 1980 and latter since 1986. Figures
7–11 show the results of our applying the paired switching algorithm described in “The Paired Switching Strategy” with these
two mutual funds over a 25-year period beginning on 1/4/1988
and terminating on 12/31/13. In this case, the number of trading
days, N = 6966.

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Figure 7. Portfolio value on day 1 < n ≤ N, over the 25-year
period n, beginning on 1/4/1988 and ending on 12/31/13

Figure 7 shows that the portfolio value at the end of the periodwith paired switching is double that obtained by buying andholding the S&P 500 index surrogate and, as will be seen below,
this result was obtained with significantly less risk. In deriving
this result the trading rule Equation 1 was applied even though,strictly speaking, extant short-term trading restrictions would
have prohibited its use. Moreover, when trading mutual funds,
the daily price is obtained only after the market closes so that
Equation 1 would need to be modified such that an indicatedtrade at month’s end would need to be made at the following
day’s close, which would also avoid any slippage.
As before, Figure 7 compares results for every date in
the period given a single entry (purchase) date, in this case
1/4/1988, the first day of the period. Figures 8–11, by contrast,
exhibit the results at the end of the period for an investment
made on any arbitrary date in the period. Figure 8 shows
the total return as a function of entry date, NkV −1, 1 ≤ k ≤ N,
for paired switching and with buy-and-hold of VFINX andVUSTX. The value of almost 2400% return over the 25-year
period amounts to almost 95% average annual return, which
substantially exceeds the 30% average over the 10-year period
in Figure 3. However, note that the total return for paired
switching is only double that for BAH of VFINX whereas, inFigure 3, the total return for paired switching over the 10-year
period is three times that of SPY.

PAGE 94 IFTA.ORG

Figure 8. Total return on day N, the end day of the
25-year period beginning on 1/4/1988 and ending on
12/31/13 for entry date corresponding to day k, 1 ≤ k ≤ N= 6966

Figure 9. CAGR on day N, the end day of the 25-year
period beginning on 1/2/1988 and ending on 12/31/13 forentry date corresponding to day k, 1 ≤ k ≤ N = 6966

IFTA JOURNAL

Figure 10. Maximum draw down on day N, the end day of
the 25-year period beginning on 1/2/1988 and ending on
12/31/13 for entry date corresponding to day k, 1 ≤ k ≤ N= 69662021 EDITION

Finally, we should comment on the evaluation period, k. In
this study we chose to evaluate the paired switching portfolio
at the end of each month, in accordance with the rule used forthe Ivy 5. However, as mentioned earlier, Maewal and Scalaton8
only evaluated the possibility of switch every three months,
corresponding to the 3-month look back period. Moreover,Cohn9 employed an evaluation period of one day, together
with a look back period of 84 days (it can be shown that, witha one day evaluation period, the optimal performance has asteep maximum about the 84-day look back period). Table 1
summarizes the performance for these three cases over the 10year period ending on 12/31/13.
Table 1. Total return, CAGR, maximum drawdown,
and total number of trades for three different paired
switching parameter sets for the 10-year period
beginning on 1/2/2004 and ending on 12/31/13 (N = 2517)

Figure 11. Number of trades on day N, the end day of the
25-year period beginning on 1/2/1988 and ending on
12/31/13 for entry date corresponding to day k, 1 ≤ k ≤ N= 6966100 (N1V − 1) %(N1c ) %(N1δmax )%N

k = 1 mo, µ = 3mo

294.1

14.6

18.9

45

k = 3 mo, µ = 3mo

193.9

11.3

24.1

41

k = 1 day, µ = 84 days 327.8

15.5

17.1

189

It can be seen that with k = 3 months, the number of tradesis only slightly less than with k = 1 month, but the performance
suffers significantly. Conversely if paired switching is evaluatedon a daily basis, with a look back period of 84 days, a roughly 1%
increase in CAGR is obtained, together with a small decreasein maximum draw down. However, in this case, the number of
trades increases by a factor of four so that slippage becomes asignificant factor.

Conclusion

CAGR results are shown in Figure 9. Over the entire set ofentry dates in the 25-year period the minimum, mean, andmaximum values for paired switching are 10.2%, 15.2%, and31.7%, respectively. These values are comparable to the values
obtained with SPY and TLT in the 10-year period and shownin Figure 4. The maximum draw down numbers in the 25-year
period, shown in Figure 10, are also similar to those for the 10year period in Figure 5 and the number of trades shown in Figure
11, a little less than six per year on average, compares with the
4 1/2 average number obtained from Figure 6.

t

1

Paired switching refers to investing in one of a pair ofnegatively correlated assets and periodic switching of theposition on the basis of the relative performance of the two.
The specific pair examined in this study is the S&P 500 index
and the index for U.S. treasury bonds with 20 or more years
duration. It was shown that using mutual fund surrogates,
VFINX for the former and VUSTX for the latter, and switching
between the two at the end of each month based on whichever
had the higher return over the past three months, a total returnof almost 2400% could have been achieved over a period of 25
years ending on 12/31/13, double that obtained by buying andholding VFINX alone in this period. Moreover, this result would
have been obtained with significantly less risk; draw down was
only a third that with buy-and-hold. Unfortunately, short-term
trading restrictions on these mutual funds, still in place, would
have made it difficult, if not impossible, for the average investor
to use this method. With the advent of exchange traded funds,
however, the game has changed. ETFs are readily available thattrack the S&P 500 index (SPY) and the long bond (TLT). The low
electronic trading cost coupled with the huge daily volumes
provide the liquidity with which slippage can be minimized.
Using the same paired switching rule just described it was
shown that the total return over the 10-year period endingon 12/31/13 was almost 300%, three times higher than thatobtained by buying and holding SPY alone, and also three
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times higher than that obtained with the popular Ivy 5 method
described in “The Ivy 5 Strategy” above. And, as with the mutual
funds over the 25-year period, the draw down with paired
switching was only a third that with buy-and-hold of SPY. The
number of trades with paired switching over the 10-year period
was 45, compared with 70 for the Ivy 5, although the latter had
slightly lower maximum draw down (14.6% versus 18.8% for
paired switching).

Notes
Lewis A. Glenn is a Founding Partner and Chief Scientific Officer ofCreative Solutions Associates LLC, a private investment and wealthmanagement group.
2 Malkiel, B. G., “A Random Walk Down Wall Street” (2007), W. W.
Norton & Company
3 Faber, Mebane T., A Quantitative Approach to Tactical Asset Allocation(February 1, 2013). The Journal of Wealth Management, Spring 2007.
Available at SSRN: http://ssrn.com/abstract=962461
4 Faber, M. T. and Richardson, E. W., The Ivy Portfolio—How to Invest
Like the Top Endowments and Avoid Bear Markets, John Wily &Sons, 2009.
5 Glenn, L. A., Market Timing with Volatility, Active Trader, 10, No.
7, July 2009, pp. 28-31; see also Beat the Market—A Strategy forConservative Investors (December 12, 2008). Available at SSRN:
http://ssrn.com/abstract=1315533
6 Glenn, L. A., Market Timing using Exchange Traded Funds, The
Technical Analyst, July-Sept. 2010, pp.16-24. Available at SSRN:
http://ssrn.com/abstract=1591969; see also Playing Both Sides,
Active Trader, 11, No. 9, September 2010, pp. 44-49
7 Glenn, L. A., Adaptive Market Timing with ETFs (December 28, 2010).
Available at SSRN: http://ssrn.com/abstract=1732010
8 Maewal, Akhilesh and Scalaton, Joel B., Paired-Switching for Tactical
Portfolio Allocation (August 22, 2011). Available at SSRN: http://
ssrn.com/abstract=1917044
9 Cohn, Marc, Return Like a Stock, Risk Like a Bond: 15.5% CAGR
with 17% Drawdown, (February 23, 2014), Available at http://
seekingalpha.com/article/2041703-return-like-a-stock-risk-like-abond-15_5- percent-cagr-with-17-percent-drawdown?ifp=0.
10 Henriksson, R. D. and Merton, R. C., On Market Timing andInvestment Performance. II. Statistical Procedures for Evaluating
Forecasting Skills, J. Business, 54, No. 4, October 1981, pp. 513-533.
1

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How the Average Investor Can Use Technical Analysis for Stock
Profits: An In-Depth Work on Stock Market Technical Analysis,
Mob Psychology, and Fundamentals by James Dines
Reviewed by Regina Meani, CFTe

Amazon describes it as James Dines’ famous treatise ontechnical analysis for the stock market and advises that there
are no reviews. On the Dines’ Letter website, there is a quotefrom a Barron’s Magazine reviewer, but I fear this was back inthe 1970s and no longer readily available. I reached onto mybookshelves and pulled out my treasured tome, thinking it
was time for a 21st century review. First published in 1972 after
taking James Dines 11 years to write, he set about publishingit himself through the Dines Chart Corporation, and it was
reprinted in 1973 and 1974. Not only was he a pioneer in his
thinking on stock market analysis but also in publishing his
own work nearly 50 years ago. There is a massive 599 pages thathave been written to involve both the beginner and the moreadvanced. The London Financial Times commented that “Mr
Dines has done for Technical Analysis what Graham & Dodd has
done for Fundamental Analysis.”Part One deals with Mass Market Psychology, and one isimmediately confronted with the first topic: Sex and the secretdesire to lose. While some parts of this section may be questioned
by modern society’s correctness, it remains an interesting
interpretation of people’s attitudes to the share market. We
are also introduced to “Pigeon”, who features throughout andI must admit has helped me out in many of my presentations.
Pigeon adds humour and allows us to connect with the text as we
empathize with Pigeon’s ongoing predicaments.
Throughout, Dines tempers his work2with sage advice. By page 123, we havereached Part Two—Technical Analysis. To
like everyone is to be indifferent toeveryone. Same with charts, you mustchoose. It is the act of choosing which is thebusiness of this book.3
In a clear and precise manner, Dines
explains the different types of charts in
his section on “How to Chart”. He deals with Point and Figure,the arithmetic bar, and the semi-log bar and advises that weshould use all three. He states that “Each measures different
technical factors: price change and volume in time, percentage
of price change and volume in time and price change alone. Allreveal different facets of a stock’s personality-in-motion. While themethods of analysis are homologous. The conclusions reached areoften surprisingly different.”4 It is a shame that we do not have hisinterpretation of candlesticks, but I believe that it wasn’t until
the 1990s that they became popular in the western world. Dines
uses Point and Figure charts for most of his prolific examples,and he uses his own particular style. Most of us are familiar
with the “X” and “O” technique, but Dines just uses crosses
and refers to them as “Cross Charts”. The interpretation is thesame; they just look slightly different and are a little harder tomanually construct.
Dines manages to bring reality to his text by using excerpts
from the Dines Letter. In one interesting piece, he deals with thecriticisms of the Dow Jones Industrial Average as to its value asa market indicator, which remains a relevant topic to this day.
For his argument, he uses quotes from the New York Times andfrom Arthur Merrill’s Behavior of Prices on Wall Street. The joint
conclusion from the comparison to the broader indices wasthat “The price action in the two averages has been amazingly
similar. But this nonetheless does not prove the DJI is always
representative of the market.”5
There are detailed descriptions of chart patterns, moving
averages, and both financial and technical indicators and, mostimportantly, how to use these to make profits. I continue to use
many of his techniques, particularly those for moving averages,
finding that along with his behavioural studies, they have stood
the test of time and the changing market scene.
To continue a more detailed review, and do justice to Dines’
work, would perhaps take most of the Journal pages, andthis would be without attempting to critique his section on
Fundamental Analysis. Many have referred to Dines’ book as a“classic” and to the man as a “legend”, and both no doubt havemerit for maintaining positions of excellence for 50 years or more.
One of my more memorable moments was attending a talk byDines when he visited Australia. I approached him, clutching mycopy, and I believe that he was both pleased and surprised. I’m
not sure, but my guess was that he was surprised it had madeit to Australia due to its limited publication. I might also guessthat my signed copy would be worth more than the current priceof AUD$1,365 (US$994) and perhaps it could be said that it has
proved one of my better investments.

Notes

The Dines Letter—Mr. Dines’ Bio: https://www.dinesletter.com/bio.
html
2 Dines, J, How the Average Investor Can Use Technical Analysis for
Stock Profits, Dine Chart Corporation, USA, 1972, p. 1
3 ibid, p. 123
4 Ibid, p. 323
5 Ibid, p. 345
6 Ibid, p. 138
1

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Author Profiles
Ron Albert Marcelino Acoba, CFTe, CMT, MFTA
Ron Albert Marcelino Acoba, CFTe, CMT, MFTA,
is the chief investment strategist and cofounder of Trading Edge Consultancy, a thirdparty research provider for banks andbrokerage firms in the Philippines. Ron has morethan 15 years of experience in the investment
industry. He used to be an equity dealer and forex trader for
Credit Suisse Philippines, as well as an equity fund manager andchief equity dealer for BPI Asset Management and Trust Group,
where he personally managed US$500 million in discretionary
funds. Earlier in his career, he worked as a forex analyst andtrader for BabyPips.com, LLC. Ron received his undergraduate
degree in economics from the Ateneo de Manila University. He
also holds an MBA from the University of the Philippines.
Momen Atef El Shayal, CFTe, MFTA
Momen Atef El Shayal, CFTe, MFTA, is the headof the technical analysis department at SIGMA
Securities Brokerage. He has almost 10 years ofexperience in the capital markets. He earned a
B.Sc. in commerce in 2008 and acquired his CFTein 2013. Momen is a member of the Egyptian
Society of Technical Analysts (ESTA). He started his career in
Jazira Capital as a senior technical analyst in 2011 and waspromoted to chief technical analyst in 2013, serving both asset
management and brokerage divisions. In 2016, he joined the
SIGMA Securities Brokerage team, where he is responsible fortechnical analysis products for individual and institutional
clients covering the MENA and U.S. markets.
Mohamed Fawzy ElSayed Ali AbdAlla, CETA, CFTe,
MFTA
Mohamed Fawzy ElSayed Ali AbdAlla, CETA,
CFTe, MFTA, is a urology consultant in the
National Institute of Urology and Nephrology in
Egypt. He is also deeply interested and involvedin the analysis of financial markets. He has five
years of experience as an independent
investment strategist handling his family portfolio.
As a private investor, Mohamed grew to believe that
achieving continuous portfolio growth is obtained by creating
and improving a reliable money management trading system,
which can only be reached through integration of two main
success tools: technical analysis as the only signal provider
and, more importantly, statistics and programming languages
for better diversification, allocation, risk management, andperformance analysis.

Lewis A. Glenn, Ph.D.
Lewis A. Glenn, Ph.D., is a founding partner andchief scientific officer at Creative Solutions
Partners Associates LLC, a private investment
and wealth management group with offices in
the San Francisco Bay Area and Lausanne,
Switzerland. In a previous life Lewis led theComputational Physics Group in the Earth Sciences Division at
the Lawrence Livermore National Laboratory in Livermore,
California.
Yukitoshi Higashino, MFTA
Yukitoshi Higashino, MFTA, is chief technicalstrategist of the equity research team at DZHFinancial Research, an investment information
firm located in Tokyo as Shanghai DZH Limitedgroup. He leads the development of technicalstrategies for client securities firms dealing in
futures, ETFs, and CFDs. His main responsibilities range fromanalysis of major indices of the Asian and world markets andcan extend to even individual stocks. Prior to this, he was astock lending trader for foreign securities houses and a treasury
stock trader for Mizuho Trust Bank. He was also an equity
trader and a market analyst at a securities firm. Yukitoshi isdirector of the Nippon Technical Analysts Association (NTAA).
He is also a member of the Financial Planners Association ofJapan (CFP). He made presentations at the IFTA conferences in
Vancouver, Lugano, Berlin, and Cairo.
Mohamed M. Khedr, CFTe, MFTA
Mohamed M. Khedr, CFTe, MFTA, has beenworking as a senior technical analyst at Prime
Securities since 2014. He has over 12 years ofexperience in the Egyptian capital market.
Before joining Prime Securities, he was a senioraccount manager at NAEEM Holding Co. Prior tothat, he worked as a senior technical analyst at Tycoon
Securities. Mohamed started his career at Mirage Brokerage Co.
in the customer service department. He then became a broker, aresearch technical analyst, and finally the head of the technicalanalysis department. He also created a new department (callcenter) to answer clients’ technical questions. Mohamed has abachelor’s degree in accounting from the Faculty of Commerce
at Cairo University. He has a brokerage license from ECMA
(Egyptian Capital Market Association) and is a CPM holder(Certified Portfolio Manager) from EIMA (Egyptian Investment
Management Association).
Kevin Luo
Profile not available.

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Mohamed Ashraf Mahfouz, CETA, CFTe, MFTA
Mohamed Ashraf Mahfouz, CETA, CFTe, MFTA, ischief technical analyst for Commercial
International Brokerage Company (CIBC), one of
Egypt’s top-ranked brokerage houses. CIBC is
the securities brokerage division of CI-Capital,
the premier investment bank in Egypt, withmarket-leading leasing, microfinance, investment banking,securities brokerage, asset management, and researchfranchises. Prior to this he was the head of the technicalanalysis desk for Dynamic Securities Brokerage Company, a
CI-Capital member.
He served as a board member and vice president for the
Middle East and Africa region of IFTA from 2010 until 2016.
During this period, he implemented MOU agreements between
IFTA and the United Arab Emirates Securities and Commodities
Authority (ESCA) and between IFTA and the Capital Market
Authority in Sultanate Oman (CMA), and he was responsible
for translating the CFTe bank of questions into the Arabic
language. He also served as a board member in the Egyptian
Society of Technical Analysts (ESTA) from 2011 until 2015, andhe has been a key education committee member in ESTA since2007. Mohamed has taught technical analysis at many financial
institutions and universities for more than 14 years. He
graduated from the Faculty of Economics and Political Sciences,
Cairo University.

2021 EDITION

Przemysław Smoliński, MFTA
A 20-year enthusiast of technical analysis andtrading, both professionally and as a hobby,
Przemysław Smoliński, MFTA, got interested in
the stock market in the last year of primaryschool and still has been fascinated by drawing
lines, designing, and coding trading strategies.
From 2000–2006, he worked at mBank Securities, and since2006, he has been working as an analyst in the brokerage houseof the largest bank in Poland, PKO BP—first in the Research
Department and currently at the Technical Analysis andInvestment Advisory Department. Invariably, since 2012, he has
been considered one of the best technical analysts in thenewspaper’s “Parkiet” ranking, and his analytical products
three times got to the short list of finalists in the competition
organized by the British magazine The Technical Analyst.
Nowadays, Przemysław is mainly focused on combining classicaltechnical analysis and statistical tools with the possibilities
offered by automated trading.

Regina Meani, CFTe
Regina Meani, CFTe, covered world markets as atechnical analyst and associate director for
Deutsche Bank prior to freelancing. She is anauthor in the area of technical analysis and is asought after presenter both internationally andlocally, lecturing for various financial bodies anduniversities as well as the Australian Stock Exchange. Regina isa founding member and former president of the AustralianProfessional Technical Analysts (APTA) and a past journal
director for IFTA, carrying the CFTe designation and theAustralian AMT (Accredited Market Technician). She has regularcolumns in the financial press and appears in other media
forums. Her freelance work includes market analysis, webinars,
and larger seminars; advising and training investors and tradersin market psychology; CFD; and share trading and technicalanalysis. She is also a former director of the AustralianTechnical Analysts Association (ATAA) and has belonged to theSociety of Technical Analysts, UK (STA) for over 30 years.

IFTA.ORG PAGE 99

IFTA JOURNAL

2021 EDITIONIFTA Staff

IFTA Board of Directors

Executive Director
Charles W.L. Deale, FASAE, CAE

IFTA HEADQUARTERS
International Federation of Technical Analysts
1300 Piccard Drive, Suite LL 14
Rockville, MD 20850 USA
Phone: +1 (240) 404-6508
Fax: +1 (301) 990-9771
Email: admin@ifta.org | Web: www.ifta.org

Senior Vice President, Meetings
Grace L. Jan, CAE, CMP
Senior Member Services Manager
Linda Bernetich, CAE
Production Manager
Maryia Alenchyk

President
Wieland Arlt, CFTe (VTAD)Managing Editor
Lynne Agoston
Director of Accounting Services
Dawn Rosenfeld

Treasurer; Website Director
Karin Roller, CFTe (VTAD)
Secretary; Membership Director
Alek Jankowski, BE, M.Eng.Sc., Grad.Dip.Mgt. (ATAA)
Vice President; Vice President, Asia-Pacific
Hiwon Yoon, Ph.D., CMTA (NTAA)
Vice President, Americas; Conference Director
Jeanette Young, CFP®, CMT, M.S. CFTe (AAPTA)
Vice President, Middle East and Africa; Immediate Past
President
Mohamed El Saiid, MFTA, CFTe (ESTA)
Vice President, Europe
Clive Lambert, MSTA, CFTe (STA)
Education Director
Saleh Nasser, CMT, CFTe (ESTA)
Examination Director
Gregor Bauer, Ph.D., CFTe (VTAD)
Marketing and Webinar Director
Thomas Hicks, MSTA (STA)
Development Director
Ron William, MSTA, CFTe (SAMT)
Director
Giovanni Trombetta, Electronic Engineer, CFTA (SIAT)
Director
Francesco Caruso, MFTA (SIAT)
Director
Nik Ihsan Bin Raja Abdullah, MSTA, CFTe (MATA)
Director
Yukitoshi Higashino, MFTA (NTAA)

PAGE 100 IFTA.ORG

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Certified Financial Technician (CFTe) Program
IFTA Certified Financial Technician (CFTe) consists of the CFTe I and CFTe II
examinations. Successful completion of both examinations culminates in theaward of the CFTe, an internationally recognised professional qualification intechnical analysis.

Examinations
The CFTe I exam is multiple-choice, covering a wide range of technicalknowledge and understanding of the principals of technical analysis; it isoffered in English, French, German, Italian, Spanish, Arabic, and Chinese;it’s available, year-round, at testing centers throughout the world, from IFTA’s
computer-based testing provider, Pearson VUE.
The CFTe II exam incorporates a number of questions that require essaybased, analysis responses. The candidate needs to demonstrate a depth ofknowledge and experience in applying various methods of technical analysis.
The candidate is provided with current charts covering one specific market(often an equity) to be analysed, as though for a Fund Manager.
The CFTe II is also offered in English, French, German, Italian, Spanish, Arabic,
and Chinese, typically in April and October of each year.

Curriculum
The CFTe II program is designed for self-study, however, IFTA will
also be happy to assist in finding qualified trainers. Local societies
may offer preparatory courses to assist potential candidates.
Syllabuses, Study Guides and registration are all available on the
IFTA website at http://www.ifta.org/certifications/registration/.

To Register
Please visit our website at http://www.ifta.org/certifications/registration/ for registration details.

Cost
IFTA Member Colleagues
CFTe I $550 US
CFTe II $850* US

Non-Members
CFTe I $850 US
CFTe II $1,150* US*Additional Fees (CFTe II only):$100 US applies for non-IFTA proctored exam locations

Master of Financial Technical Analysis (MFTA) Program
IFTA’s Master of Financial Technical Analysis (MFTA) represents the highest
professional achievement in the technical analysis community, worldwide.
Achieving this level of certification requires you to submit an original body ofresearch in the discipline of international technical analysis, which should beof practical application.

Examinations
In order to complete the MFTA and receive your Diploma, you must writea research paper of no less than three thousand, and no more than five
thousand, words. Charts, Figures and Tables may be presented in addition.
Your paper must meet the following criteria:• It must be original• It must develop a reasoned and logical argument and lead to a soundconclusion, supported by the tests, studies and analysis contained in thepaper• The subject matter should be of practical application• It should add to the body of knowledge in the discipline of international
technical analysis

Timelines & Schedules
There are two MFTA sessions per year, with thefollowing deadlines:
Session 1“Alternative Path” application deadline
Application, outline and fees deadline
Paper submission deadline
Session 2“Alternative Path” application deadline
Application, outline and fees deadline
Paper submission deadline

February 28
May 2
October 15
July 31
October 2
March 15 (of thefollowing year)

To Register
Please visit our website at http://www.ifta.org/certifications/master-of-financial-technical-analysis-mfta-program/for further details and to register.

Cost$950 US (IFTA Member Colleagues);$1,200 US (Non-Members)