Affecting Market Efficiency by Increasing Speed of Order Matching Systems on Financial Exchanges -- Investigation using Agent Based Model
- 1. 1111 Affecting Market Efficiency by Increasing Speed of Order Matching Systems on Financial Exchanges – Investigation using Agent Based Model SPARX Asset Management Co., Ltd. Daiwa SB Investments Ltd. Osaka Exchange, Inc. The University of Tokyo Takanobu Mizuta Yoshito Noritake Satoshi Hayakawa Kiyoshi Izumi IEEE SSCI CIFEr 2016 7th Dec. 2016 Note: the opinions contained herein are solely those of the authors and do not necessarily reflect those of the affiliations.
- 2. 2222 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Summary & Future Works
- 3. 3333 Speedup of Financial Exchange’s System The speed of order matching systems has been increasing due to • Competition between Financial Exchanges • Their investors’ demand 3 Tokyo Stock Exchange’s Arrowhead (Started from 2010) Latency (length of time required to transport data and match orders) Before Launch 3,000ms (3 seconds) After Launch 4.5ms (Example of Speedup of an order matching system)
- 4. 4444 Opposite Opinions in Increasing Speed What is the sufficient speed of Financial Exchange’s System ? Increasing market liquidity so that investors can trade without delay and large execution costs Increasing maintenance costs of systems imposed to a Financial Exchange 4 conflict Good Point Bad Point
- 5. 5555 • 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? Artificial Market Simulation (Multi-Agent Simulation) 5 • How much speed does a Market system need? − Need to analyze Micro Process, but too many factors may affect stock’s price: Empirical study cannot isolate the pure contribution of speed − No Market system implemented further high-speed: Impossible to verify by using empirical study (historical data) Need Discussions
- 6. 6666 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Summary & Future Works
- 7. 7 * Continuous Double Auction: as the real stock exchanges * Simple Agent model: to avoid an arbitrary result Agents (Investors) Model t jj t jhjt f j i ji t je urw P P w w r ,,2,1 , , log 1 Fundamental Technical noise Expected Return ,i jw Strategy Weight different for each agent heterogeneous 1,000 agents Replicate traditional Stylized Facts and Micro Structures Latency has Micro Structure Time Scale, Millie Seconds each agent places an order 10,000 times Order Process Same as Mizuta et. al. 2013
- 8. Matching System (Inside Financial Exchange) 8888 Model of Latency Agents (Investors) New Order Information of the order book (e.g. Updated Traded Price) Match orders & Change price We assume finite time intervals here = latency Latency Time-lag required for data transfer and/or matching orders (The most important factor of Market’s speed) How do we model it? An agent recognizes the price which is delayed for a latency
- 9. Order & Price change Order & Price change True Price (in Matching System) Observed Price (by Agent) Latency constant = δl Order interval = δt exponential random numbers Avg. = δo Difference In most cases, an agent knows True Latest Price True and Observed prices are different 9 δl/δo > 1 (slow) δl/δo ≪ 1 (fast) Key Variables
- 10. 1010 We can directly measure Market Inefficiency, by defining its Fundamental Price (=10,000) in Artificial Market Simulation. Market Inefficiency The Market Inefficiency is based on difference between market and 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 by empirical studies. Independent of time period used to calculate return Market Inefficiency = Time Avg. of Market Price − Fundamental Price Fundamental Price
- 11. 11111111 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Summary & Future Works
- 12. 121212 δl/δo > 1 ： Inefficient 0.27% 0.28% 0.29% 0.30% 0.31% 0.32% 0.001 0.002 0.005 0.01 0.02 0.05 0.1 0.2 0.5 1 2 5 10 MarketInefficiency δl / δo Market Inefficiencies Right side δl/δo ≥ 0.5, Market becomes Inefficient
- 13. 131313 δl/δo > 1 ： Wider Bid Ask Spread 0.135% 0.140% 0.145% 0.150% 0.001 0.002 0.005 0.01 0.02 0.05 0.1 0.2 0.5 1 2 5 10 BidAskSpread δl / δo Bid Ask Spreads (per Fundamental Price)
- 14. 141414 δl/δo > 1 ： Increasing Execution Rate 32.0% 32.2% 32.4% 32.6% 32.8% 0.001 0.002 0.005 0.01 0.02 0.05 0.1 0.2 0.5 1 2 5 10 ExecutionRate δl / δo Execution Rates (= Orders executed immediately / All orders)
- 15. 151515 Increasing Execution Rate especially near the Fundamental Price 𝑃 𝑡 ~𝑃𝑓 , where Technical term 𝑟ℎ 𝑡 dominates an expected return of an agent 29.0% 29.5% 30.0% 30.5% 31.0% 31.5% 32.0% 32.5% 33.0%ExecutionRate True Price (Fundamental Price = 10,000) Execution Rates for each True Prices around the Fundamental Price δl / δo = 0.001 δl / δo = 10
- 16. 161616 In a slow (δl/δo) market… True Price > Observed : Positive expected returns, Upward trend expectation True Price < Observed : Negative expected returns, Downward trend expectation δl/δo Relationship between Observed and True prices Execution Rate Avg. Expected Return of agentsTotal Buy Market Sell Limit Orders Sell Market Buy Limit Orders 10 (slow) True P. > Observed P. 32.5% 28.9% 3.5% 0.28% True P. < Observed P. 32.5% 3.6% 28.9% -0.27% 0.001 (fast) --- 31.2% 15.6% 15.6% 0.00% Execution rates and Average Expected Returns of all agents* compared by δl/δo=10, 0.001 and Relationship between Observed and True prices (slow market only) *Population of data: all executions and orders
- 17. Agents then place unnecessary market orders. But, agents cannot quickly modify their expected prices. Observed Price > True PriceObserved Price < True Price Too High Expected Price ⇒ Market Buy order Observed P. True P. Upward trend Expected Price （In a slow market） Too Low Expected Price ⇒ Market Sell order A trend has actually been finished around Fundamental Price. Agents wouldn’t placed such orders if they knew the true price. 17 Downward trend
- 18. 18181818 Stop Upward/Downward trend Market becomes Inefficient Expanding Bid Ask Spread 18 Mechanism of Large Latency（δl/δo>1）making Market Inefficient Increasing Execution Rate Decreasing remaining orders near Market Price, relatively But, agents cannot quickly change their expectation Unnecessary trend following orders Especially near Fundamental Price Large Latency
- 19. 19191919 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Summary & Future Works
- 20. 20202020 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 Inefficient Future Works * We should discuss it with more kinds of agents and situations.（example: High Frequency Trading Agents, Crowded Orders immediately after great market impacting information)
- 22. 2222 order book sell price buy 84 101 176 100 99 2 98 77 Definition of Market/Limit order In this study A little difference from actual market limit market limit market Exist matching order Order executed immediately No matching order Order not executed immediately buysell Agents decide an order price, if exist matching order, market order else limit order All agents decide an order price
- 23. 232323 δl / δo > 2 ： be Inefficient significantly -0.020% -0.010% 0.000% 0.010% 0.020% 0.030% 0.040% 0.002 0.005 0.01 0.02 0.05 0.1 0.2 0.5 1 2 5 10 DifferenceinMakertIneffiency δl / δo Difference in Makert Ineffiency (from δl / δo = 0.001)