BESC 2021
The 8th International Conference on Behavioural and Social Computing
http://besc-conf.org/2021/

Do new investment strategies take existing strategies’ returns- An investigation into agent-based models -Takanobu Mizuta

SPARX Asset Management Co., Ltd.
Mail: mizutata[at]gmail.com
HP: https://mizutatakanobu.com

Note that the opinions contained herein are solely those of the authors and do notnecessarily reflect those of SPARX Asset Management Co., Ltd.

You can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

1(1) Introduction(2) Model(3) Simulation Result(4) Summary

You can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

2

(1) Introduction(2) Model(3) Simulation Result(4) Summary

You can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

3

CTAs have not performed(CTA=Commodity Trading Advisor)

CTAs, who mainly trade commodity futures, showed good returns in the 2000s.
However, since the 2010's, they have not performed very well.
Anual Returns of Barclay CTA Index

https://portal.barclayhedge.com/cgi-bin/indices/displayCtaIndex.cgi?indexCat=Barclay-CTA-Indices&indexName=Barclay-CTA-Index

It is not solved that the reason why CTAs have not performed.
The reason of good performance before also not understood.
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The reasons why CTAs have not performed
Clenow [2012] reported the following points as reasons for this(1) the yield of government bonds decreased,(2) most CTAs utilized similar trend-following strategies and tended totrade at the same time, leading to the increased number of CTAs
further impacting market prices,(3) the variation of investors and investment strategies was diversified byalternative investments spreading,(4) short-term reversal traders (STRTs) who preyed on CTAs for profit
emerged.
But the four points contradict each other
Point (2) implies that CTAs did not perform well due to the number of CTAs increasing,but Point (3) implies that the number of CTAs has relatively decreased.

When one investment strategy fails to work, new strategies,
especially those that use new technologies or faster tradings, havetended to be blamed for a long time.
Point (4), should be discussed with particular suspicion

5Difficulty of Empirical Study✓ Since many factors affect price formation, an empirical study cannot
isolate the direct effect of new trading strategies on price formation.

Artificial Market Simulation using Agent-Based Model can do
In this study
In this study, the artificial market model was built by adding aCTAA and STRTA to the prior model of Mizuta et al[2016]
CTAA: an agent trend-following (momentum) like as CTA
STRTA: short-term reversal trader agent

I investigated whether emerging STRTAs led to a decrease inCTAA revenue to determine whether STRTs prey on CTAs for profit. 6

An artificial market model = an agent-based model for a financial market

Virtual and Artificial financial Market built on Computers
ModelsIncludeAgents (Artificial Investors)＋Price Mechanism (Artificial Exchange)
Order

Price
Mechanism(Exchange)

Agent(Investor)

Each Agent determines anorder by some rules,
Price Mechanism gather
agents orders anddetermines Market Price

Determination market priceComplete Computer Simulation needing NO Empirical Data✓ can discuss on the mechanism between the micro-macro feedback✓ can be conducted to investigate situations that have neveroccurred in actual financial markets✓ can be conducted to isolate the direct effect of STRTs
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(1) Introduction(2) Model(3) Simulation Result(4) Summary

You can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

8

My Model

Normal Agents (NAs)exist 1000 agents
To replicate the nature of priceformation in actual financialmarkets, I introduced the NA tomodel a general investor.

Stock
Exchange

one CTAone STRTagentagent(CTAA)(STRTA)each 10 time steps, theydecide to trade or not
In this study, the artificial market model was built by adding a
CTAA and STRTA to the prior model of Mizuta et al[2016]

CTAA: an agent trend-following (momentum) like as CTA
STRTA: short-term reversal trader agent

9Stock Exchange (Price Mechanism)

continuous double auction

Shares
Sell
1030

Shares
BuyPrice
103
Waiting Orders
102
101
50
100
When buy order come here
130
99
transaction immediately occurs
98
150
When sell order come heretransaction immediately occurs
9796
70
Multiple buyers and sellers compete to buy and sell stocks in the market, andtransactions can occur at any time whenever an offer to buy and an offer tosell match.
10Normal Agents (NAs)j: agent number (1,000 agents)
ordering in number ordert: tick time

Historical Return𝑡𝑟ℎ,𝑗= log 𝑃𝑡 /𝑃𝑡−𝜏𝑗Technical

Expected Return of each NAPf
1 tt  w1, j log t + w2, j rh, j + w3, j j r =P
i wi, j te, jnoise

Fundamental

Parameters for agentswi , j and  j

PfFundamental Price

Random ofUniform Distribution

Pt

Market Price at t

wi , jji=1,3: 0～1
i=2: 0～100～10000

 tj

10000 = constantExpected Price of each NA

Random ofNormal
Distribution
Average=0
σ=3%

P = P exp(r )te, jtte, j

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Fundamental and Technical Strategies
Fundamental Strategy
Fundamental Price ＞ Market Price -> Expect + return
Fundamental Price < Market Price -> Expect - return
Technical Strategy (Historical Return)
Historical Return ＞ 0 -> Expect + return
Historical Return < 0 -> Expect - return
TechnicalStrategy

FundamentalPriceMarketPrice

FundamentalStrategy12

CTAA and STRTA

5 timestep

CTAA

STRTA

CTAA

STRTA

10 time step
They decide whether trade or not at
CTAA: when t=10000,10010,10020,,,,, (each 10 time steps)
STRTA: when t=10005,10015,10025,,,, (each 10 time steps)
Only when they need a trade, they place order one share.
(When t<10000, they do not place an order to generate a sufficient number of waiting orders)

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CTAA
When CTAA has no share* if historical highest price for 100 time steps -> Buy -> holding +1 share* if historical lowest price for 100 time steps -> Sell -> holding -1 share
When CTAA holds +1 share* if historical lowest price for 10 time steps -> Sell -> holding NO share
When CTAA holds -1 share* if historical highest price for 10 time steps -> Buy -> holding NO sharehighest price-> Buy

100timesteps

10lowest price-> Sell

Marketprices
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STRTA

When STRTA has no share* if historical lowest price for 10 time steps -> Buy -> holding +1 share* if historical highest price for 10 time steps -> Sell -> holding -1 share
When STRTA holds +1 or -1 share (has some position)* if historical lowest or highest price for 5 time steps-> closing the position -> holding NO share (stop loss or realizing profit)
10

5 lowest price-> Sell(stop loss)
10

lowest price-> BuyMarketprices

5
highest price-> Sell(realizing profit)

lowest price-> Buy
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CTAA

10
highest price100-> Buytimesteps
lowest price-> SellMarketprices
STRTA

10

5 lowest price-> Sell(stop loss)
10

lowest price-> Buy5highest price-> Sell(realizing profit)

lowest priceMarket-> Buyprices
The behavior of STRTA is opposite for that of CTAA-> leads to reduce market impacts (trading costs) of CTAA-> leads more benefit to CTAA (we can predict before simulation)16

(1) Introduction(2) Model(3) Simulation Result(4) Summary

You can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

17

Time evolution of the prices

The two cases are not very different
The trades of them barely impact the prices

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Profits

CTAA
ProfitCTAA

Exist
No exist3STRTA
ProfitCTAA

Exist
No exist

STRTA
Exist No exist
105%90%
93%
79%
STRTA
Exist No exist
293% 205%265% 185%* The results for when “no exist” are calculated on the basis thatthe one would virtually trade and those orders would not change.

Both CTAA and STRTA earn more when the another one exists
19Volume and Volatility

CTAA
VolumeExist
CTAA
No exist
6Volatility(returns for 100 steps)

CTAA

Exist
No exist

5
STRTA
STRTA
STRTA
VolumeExist No exist
Exist No exist
892
865
Exist
1739
1633
CTAA
No exist
891
866
1685
1587
7STRTAExist No exist
0.047% 0.038%
0.041% 0.032%

Both CTAA and STRTA has more chances of trades
when the another one exists
They have a mutually beneficial relationship20

Shares

Mechanism of the mutually beneficial relationship

Place market
order
Increased frequency
of prices reaching
maximum or minimum
thenmore chances
to tradeearns
moreRemove

Shares

Sell

Price

Buy2103

5102

1101

2100

199

Remove98

197

396

2

Removinga waiting order

Larger variationof prices

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(1) Introduction(2) Model(3) Simulation Result(4) SummaryYou can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

22Summary✓ In this study, I built an artificial market model by adding a CTAA and STRTA to the priormodel of Mizuta et al[2016] and investigated whether emerging STRTAs led to adecrease in CTAA revenue to determine whether STRTs prey on CTAs for profit.
✓ My results showed that CTAAs and STRTAs have more chances of trade and earn morewhen each other exist. Therefore, they have a mutually beneficial relationship.
✓ The CTAA and STRTA place a market order, which leads to a waiting order being
removed from the order book. Decreasing waiting orders leads to other buy (sell)market orders executing with a waiting sell (buy) order of a higher (lower) price. This
leads to a larger variation of prices and an increase in frequency that prices reach themaximum or minimum.
✓ Furthermore, the CTAA and STRTA have more chances to trade and earn more wheneach other exist, and increasing their trades leads to make more market orders and thestrengthening of this cycle.
You can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

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LESSON✓ When one investment strategy fails to work, new strategies, especially those that use
new technologies or faster tradings, have tended to be blamed for a long time.
Therefore, such arguments that new strategies are used to impede existing strategies
should be discussed with particular suspicion.
✓ Since the second half of the 20th century, this tendency has become stronger, and themedia have often criticized new investment strategies using new technologies,
electronic trades, program trades, algorithm trades, high-frequency trades, and AItraders. However, these critics seem to be missing the point. These criticisms should beinvestigated using an artificial market model, which are considered future works.

You can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

24References⚫ [Clenow 2012] Following the Trend: Diversified Managed Futures Trading, Wiley, 2012
⚫ [Mizuta 2016] Effects of price regulations and dark pools on financial market stability: An
investigation by multiagent simulations, Intelligent Systems in Accounting, Finance andManagement, vol. 23, no. 1-2, pp. 97–120, 2016.
⚫ [Mizuta 2019] An agent-based model for designing a financial market that works well,arXiv https://arxiv.org/abs/1906.06000
Slide: https://mizutatakanobu.com/2020CIFErb.pdf
YouTube: https://youtu.be/rmlb72ykmlE⚫ My recent lecture slides https://mizutatakanobu.com/2021kyushu.pdf

You can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

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Appendix
It is possible that I answer questions using following slides,
my recent lecture slides https://mizutatakanobu.com/2021kyushu.pdf

You can download this presentation material fromhttps://mizutatakanobu.com/2021BESC.pdf

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Stylized Facts

The model of Chiarella (2002) is very simple but replicates long-term statistical characteristics observed in actual
financial markets: a fat tail and volatility clustering.
In contrast, Mizuta (2013) replicates high-frequency micro structures, such as execution rates, cancel rates, andone-tick volatility, that cannot be replicated with the model of Chiarella (2002).
The simplicity of the model is very important for this study, because unnecessary replication of macro phenomena
leads to models that are overfitted and too complex. Such models prevent understanding and discovery ofmechanisms affecting price formation because of the increase in related factors.

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