IEEE SSCI CIFEr 2016
7th Dec. 2016

Affecting Market Efficiency
by Increasing Speed of Order Matching
Systems on Financial Exchanges– Investigation using Agent Based Model
Takanobu Mizuta

SPARX Asset Management Co., Ltd.

Yoshito Noritake

Daiwa SB Investments Ltd.

Satoshi Hayakawa

Osaka Exchange, Inc.

Kiyoshi Izumi

The University of Tokyo
1

Note: the opinions contained herein are solely those of the authors and do not necessarily reflect those of the affiliations.

(1) Introduction(2) Artificial Market Model(3) Simulation Results(4) Summary & Future Works2

Speedup of Financial Exchange’s System

The speed of order matching systems has been
increasing due to• Competition between Financial Exchanges• Their investors’ demand(Example of Speedup of an order matching system)
Tokyo Stock Exchange’s
Arrowhead(Started from 2010)

Latency (length of timerequired to transport data andmatch orders)

Before Launch

3,000ms (3 seconds)

After Launch

4.5ms3

Opposite Opinions in Increasing Speed
Good Point

Increasing market liquidity so that investors cantrade without delay and large execution costs

conflict
Bad Point

Increasing maintenance costs of systems imposed
to a Financial Exchange

What is the sufficient speed of
Financial Exchange’s System ?
4

Need Discussions

Artificial Market Simulation(Multi-Agent Simulation)• How much speed does a Market system need?
− No Market system implemented further high-speed:
Impossible to verify by using empirical study (historical data)• If system’s speed effect market efficiency, what are
the mechanisms?
• Can investor buy or sell a stock around its fair
(fundamental) price regardless of its speed?
− Need to analyze Micro Process, but too many factors mayaffect stock’s price: Empirical study cannot isolate the pure
contribution of speed5

(1) Introduction(2) Artificial Market Model(3) Simulation Results(4) Summary & Future Works

6

Agents (Investors) Model

Same as Mizuta et. al. 2013

* Continuous Double Auction: as the real stock exchanges* Simple Agent model: to avoid an arbitrary result
heterogeneous 1,000 agentseach agent places an order 10,000 times

Expected Return1 tt  w1, j log t  w2, j rh , j  u j  j r P
i wi, j te, jPfFundamental

Technicalnoise

wi , jStrategy Weight
different for eachagent

Order Process

Replicate traditional Stylized Facts and Micro Structures
Latency has Micro Structure Time Scale, Millie Seconds

7Model of Latency
Latency

How do we model it?

Time-lag required for data transfer and/or matchingorders (The most important factor of Market’s speed)

Matching System(Inside Financial Exchange)
Match orders & Change price

New Order

Information of the order book(e.g. Updated Traded Price)

Agents(Investors)

We assume finite time intervals
here = latency
An agent recognizes the pricewhich is delayed for a latency
8δl/δo > 1 (slow)

True Price(in Matching System)

Observed Price(by Agent)

Difference
Key Variables

Order & Price change
True and Observed prices are different
δl/δo ≪ 1 (fast)

Latency
constant = δlOrder interval = δtexponential
random numbers

Avg. = δoOrder & Price change
In most cases, an agent knows True Latest Price

9Market Inefficiency

Market Inefficiency
Time Avg. of Market Price − Fundamental Price=Fundamental Price
The Market Inefficiency is based on difference between marketand fundamental prices.
If the price in the stock market highly deviates from its
fundamental, the market was not consider to be efficient.
-> But we don’t know the “true” fundamental price in real
financial markets, so we can’t assess correct inefficiency byempirical studies.
We can directly measure Market Inefficiency, by defining its
Fundamental Price (=10,000) in Artificial Market Simulation.
Independent of time period used to calculate return
10(1) Introduction(2) Artificial Market Model(3) Simulation Results(4) Summary & Future Works

11

Market Inefficiencies
0.32%

Market Inefficiency

0.31%
0.30%
0.29%
0.28%

10521

0.5

0.2

0.1

0.05

0.02

0.01

0.005

0.002

0.001

0.27%

δl / δoδl/δo > 1 ： Inefficient
Right side δl/δo ≥ 0.5, Market becomes Inefficient12Bid Ask Spreads (per Fundamental Price)
0.150%

0.140%

10521

0.5

0.2

0.1

0.05

0.02

0.01

0.005

0.002

0.135%
0.001

Bid Ask Spread

0.145%

δl / δoδl/δo > 1 ： Wider Bid Ask Spread
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Execution Rates (= Orders executed immediately / All orders)
32.8%

32.4%

32.2%

105210.5

0.2

0.1

0.05

0.02

0.01

0.005

0.002

32.0%
0.001

Execution Rate

32.6%

δl / δoδl/δo > 1 ： Increasing Execution Rate

1433.0%

Execution Rates for each True Prices
around the Fundamental Price

32.5%

Execution Rate

32.0%
31.5%
31.0%
30.5%
30.0%

δl / δo = 0.001

29.5%

δl / δo = 10

29.0%

True Price (Fundamental Price = 10,000)

Increasing Execution Rate especially near theFundamental Price 𝑃𝑡 ~𝑃𝑓 , where Technical term 𝑟ℎ𝑡
dominates an expected return of an agent15

 Execution rates and Average Expected Returns of all agents* comparedby δl/δo=10, 0.001 and Relationship between Observed and True prices(slow market only)
Execution Rate
Avg. Expected
Buy
Relationship between
Sell Market Return ofδl/δoMarket
Observed and True prices Total
Buy Limit
agentsSell Limit
OrdersOrdersTrue P. > Observed P.
32.5%
28.9%
3.5%
0.28%
10(slow)
True P. < Observed P.
32.5%
3.6%
28.9%
-0.27%
0.001(fast)

---

31.2%

15.6%

15.6%

0.00%

*Population of data: all executions and orders

In a slow (δl/δo) market…
True Price > Observed : Positive expected returns, Upward trend expectation
True Price < Observed : Negative expected returns, Downward trend expectation
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（In a slow market）
Observed Price > True Price
Observed Price < True Price
Expected Price
Downward trend
True P.

Observed P.
Upward trend

Too High Expected Price⇒ Market Buy orderToo Low Expected Price⇒ Market Sell order

A trend has actually been finished around Fundamental Price.
But, agents cannot quickly modify their expected prices.
Agents then place unnecessary market orders.
Agents wouldn’t placed such orders if they knew the true price.

17

Mechanism of Large Latency（δl/δo>1）making Market Inefficient

Stop Upward/Downward trend
But, agents cannot quickly
change their expectation

Large Latency

Unnecessary trend
following orders

Especially near
Fundamental Price

Increasing Execution Rate

Decreasing remaining orders near Market Price, relatively
Expanding Bid Ask Spread
Market becomes Inefficient18(1) Introduction(2) Artificial Market Model(3) Simulation Results(4) Summary & Future Works

19Summary* The ratio （δl/δo） is key parameter,Latency（δl） per Average of Order interval （δo）
* the sufficient speed of Exchange’s System is δl < δo （δt）.
* Trend stops in slow market (with Large Latency)-> agents cannot change Estimate price, quickly-> Unnecessary market following trades near fundamental-> Increasing Execution Rate -> Expanding Bid Ask Spread-> Market becomes InefficientFuture Works* We should discuss it with more kinds of agents andsituations.（example: High Frequency Trading Agents,
Crowded Orders immediately after great market impactinginformation)
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Appendix

21

Definition of Market/Limit order

In this study

A little difference from actual market

All agents decide an order priceorderbookselllimit
market

sell

price

84

101

176

100buybuy
Exist matching ordermarket Order executed immediately

99

2

98

77

limit

No matching order
Order not executed immediately

Agents decide an order price,
if exist matching order, market order else limit order22

Difference in Makert Ineffiency(from δl / δo = 0.001)

0.030%
0.020%
0.010%
0.000%-0.010%

10521

0.5

0.2

0.1

0.05

0.02

0.01

0.005

-0.020%0.002

Difference in Makert Ineffiency

0.040%

δl / δoδl / δo > 2 ： be Inefficient significantly
23