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Summary jpx wp_en_no9

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JPX Working Paper Vol.9 【Summary】
Impacts of Speedup of Market System on Price Formations using Artificial Market Simulations
http://www.jpx.co.jp/corporate/news-releases/0010/20150331-02.html

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Summary jpx wp_en_no9

  1. 1. 1111 Impacts of Speedup of Market System on Price Formations using Artificial Market Simulations SPARX Asset Management Co., Ltd. Japan Securities Clearing Corporation Osaka Exchange, Inc. The University of Tokyo CREST, JST Takanobu Mizuta Yoshito Noritake Satoshi Hayakawa Kiyoshi Izumi JPX Working Paper 【Summary】 Vol. 9, 31th March 2015
  2. 2. 2 This material was compiled based on the results of research and studies by directors, officers, and/or employees of Japan Exchange Group, Inc., its subsidiaries, and affiliates (hereafter collectively “the JPX group”) with the intention of seeking comments from a wide range of persons from academia, research institutions, and market users. The views and opinions in this material are the writer's own and do not constitute the official view of the JPX group. This material was prepared solely for the purpose of providing information, and was not intended to solicit investment or recommend specific issues or securities companies. The JPX group shall not be responsible or liable for any damages or losses arising from use of this material. This English translation is intended for reference purposes only. In cases where any differences occur between the English version and its Japanese original, the Japanese version shall prevail. This translation is subject to change without notice. The JPX group shall accept no responsibility or liability for damages or losses caused by any error, inaccuracy, misunderstanding, or changes with regard to this translation.
  3. 3. 3333 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Empirical Study to Compare (5) Summary & Future Works
  4. 4. 4444 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Empirical Study to Compare (5) Summary & Future Works
  5. 5. 5555 Speedup of Exchange System How much speedup is best?How much speedup is best? Increasing liquidity by increasing providing liquidity traders Increasing cost for systems of Markets and investors Because of competition between Markets and big investors demands 5 conflicting GOOD BAD
  6. 6. 6666 Does Market speed purely effect market efficiency? Artificial Market Simulation (Multi-Agent Simulation) 6 What are Mechanisms? How much enough speedup is Market system? -> So many factors cause price formation : An empirical study cannot isolate the pure contribution -> So many factors cause price formation : An empirical study cannot isolate the pure contribution -> Analysis Micro Process: Impossible by empirical study-> Analysis Micro Process: Impossible by empirical study -> No Market experienced more Speedup: Impossible by empirical study -> No Market experienced more Speedup: Impossible by empirical study Need Discussions
  7. 7. 7777 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Empirical Study to Compare (5) Summary & Future Works
  8. 8. 8888 Model of Latency Agents (Investors) Agents (Investors) MarketMarket OrderOrder New Traded Price New Traded Price Matching orders & Changing traded prices Matching orders & Changing traded prices Only here, it needs finite time (latency). LatencyLatency Needed time for matching orders and/or data transfer Needed time for matching orders and/or data transfer Most important factor of Market speed
  9. 9. Order & Price change Order & Price change True Price Observed Price Latency constant = δl Order interval exponential random numbers Avg. = δo Difference Most cases, agents know True Price True and Observed prices are difference 9 δl / δo > 1δl / δo > 1 δl / δo ≪ 1δl / δo ≪ 1
  10. 10. 10 * Continuous Double Auction: to implement realistic latency * Simple Agent model: to avoid arbitrary result Same Model as JPX Working Paper vol.2; Mizuta et. al. 2013 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 1000 agents Replicate traditional Stylized Facts and Replicate Micro Structures Latency has Micro Structure Time Scale, MilliSecondsLatency has Micro Structure Time Scale, MilliSeconds
  11. 11. 11111111 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Empirical Study to Compare (5) Summary & Future Works
  12. 12. 121212 δl/δo>1: increasing Volatility, decreasing Kurtosis (flatter fat tail) ⇒ be inefficient? δl/δo>1: increasing Volatility, decreasing Kurtosis (flatter fat tail) ⇒ be inefficient? 0 5 10 15 20 0.00% 0.01% 0.02% 0.03% 0.04% 0.05% 0.06% 0.001 0.002 0.005 0.01 0.02 0.05 0.1 0.2 0.5 1 2 5 10 Kurtosis Volatility δl / δo Volatility & Kurtosis(δr/δo=1) Volatility Kurtosis δl/δo: latency / order interval δr/δo: return calculation period / order interval δl/δo: latency / order interval δr/δo: return calculation period / order interval
  13. 13. 131313 0 0.5 1 1.5 2 2.5 0.00% 0.02% 0.04% 0.06% 0.08% 0.10% 0.12% 0.001 0.002 0.005 0.01 0.02 0.05 0.1 0.2 0.5 1 2 5 10 Kurtosis Volatility δl / δo Volatility & Kurtosis(δr/δo=10) Volatility Kurtosis δl/δo>1:Volatility is flat, Increasing Kurtosis (fatter fat tail) ⇒ be inefficient? δl/δo>1:Volatility is flat, Increasing Kurtosis (fatter fat tail) ⇒ be inefficient? We should use the way independent of return calculation period
  14. 14. 1414 We can measure Market Inefficiency Directly, not estimation in simulation studies. Market Inefficiency If Market was perfect efficient, Market prices were exactly same as the fundamental price. This Market Inefficiency is defined actual difference between market and fundamental prices. -> We can not use this definition for an empirical study. Experimental study for human sometimes uses this definition. Independent of return calculation period Market Inefficiency = Average of Market Price − Fundamental Price Fundamental Price
  15. 15. 151515 δl / δo > 1 : be 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 Inefficiency Right side δl / δo = 0.5, Market becomes InefficientRight side δl / δo = 0.5, Market becomes Inefficient
  16. 16. 161616 δl / δo > 1 : Wider Bid Ask Spreadδl / 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 Spread
  17. 17. 171717 δl / δo > 1 : Increasing Execution Rateδl / 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 Rate
  18. 18. 181818 Increasing Execution Rate especially near the fundamental price Increasing Execution Rate especially near the fundamental price 29.0% 29.5% 30.0% 30.5% 31.0% 31.5% 32.0% 32.5% 33.0% 9,940 9,950 9,960 9,970 9,980 9,990 10,000 10,010 10,020 10,030 10,040 10,050 10,060 ExecutionRate True Price Execution Rate for True Prices Fundamental Price = 10,000 δl / δo = 0.001 δl / δo = 10
  19. 19. 191919 Observed Price < True Price: More Buy Market Orders: Positive estimated returns Observed Price > True Price: More Sell Market Orders: Negative estimated returns Observed Price < True Price: More Buy Market Orders: Positive estimated returns Observed Price > True Price: More Sell Market Orders: Negative estimated returns δl / δo Execution Rate Avg. Estimated Return of agents Sum Buy Market Sell Limit Orders Sell Market Buy Limit Orders 10 Observed P. < True P. 32.5% 28.9% 3.5% 0.28% Observed P. > True P. 32.5% 3.6% 28.9% -0.27% 0.001 --- 31.2% 15.6% 15.6% 0.00%
  20. 20. Unnecessary market following tradesUnnecessary market following trades But, agents cannot change Estimate price, quicklyBut, agents cannot change Estimate price, quickly Observed Price > True PriceObserved Price < True Price Too High Estimated P. ⇒ Market Buy order ↑ If agents knew True P. they did not order. Too High Estimated P. ⇒ Market Buy order ↑ If agents knew True P. they did not order. Observed P. True P. Upward trend Estimated P. (near Fundamental Price) Too Low Estimated P. ⇒ Market Sell order ↑ If agents knew True P. they did not order. Too Low Estimated P. ⇒ Market Sell order ↑ If agents knew True P. they did not order. Stop market trendStop market trend
  21. 21. 21212121 Stop market trendStop market trend Market becomes Inefficient Expanding Bid Ask SpreadExpanding Bid Ask Spread 21 Mechanism of Large Latency(δl/δo>1) making Market Inefficient Increasing Execution RateIncreasing Execution Rate Decreasing Limit orders near Market Price, relativelyDecreasing Limit orders near Market Price, relatively But, agents cannot change Estimate price, quickly But, agents cannot change Estimate price, quickly Unnecessary market following trades Unnecessary market following trades Especially near Fundamental Price Especially near Fundamental Price Large Latency
  22. 22. 22222222 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Empirical Study to Compare (5) Summary & Future Works
  23. 23. 232323 1:Uno, 2012 2~6:In this study 1:Uno, 2012 2~6:In this study No. Analysis Period arrowhead Order No. Avg. for day Avg. names Calculation Period (min) Avg. δo (ms) = Period (ms) / Order No. Latency δl (ms) δl / δo 1 December 2009 (one month) Before 2,833 270 5,718 3,000 0.525 2 2 August 2010 - 18 November 2011 After 14,621 355 1,457 4.5 0.003 3 21 November 2011 - 26 November 2014 28,974 385 797 4.5 0.006 4 27 October 2014 - 26 November 2014 66,044 385 350 4.5 0.013 5 31 October 2014 (one day) 87,109 385 265 4.5 0.017 6 4 November 2014 (one day) 114,027 385 203 4.5 0.022 Before arrowhead It is Possible that Market is Chronically Inefficient After arrowhead Market is NOT Chronically Inefficient by the Mechanism we showed
  24. 24. 242424 0.000 0.005 0.010 0.015 0.020 0.025 10/27 10/28 10/29 10/30 10/31 11/4 11/5 11/6 11/7 11/10 11/11 11/12 11/13 11/14 11/17 11/18 11/19 11/20 11/21 11/25 11/26 δl/δo Month / Day δl/δo for business day 27 October 2014~26 November 2014 Even though near 31 October 2014, Bank of Japan announced “Expansion of the Quantitative and Qualitative Monetary Easing”, Market is NOT Chronically Inefficient by the Mechanism we showed Even though near 31 October 2014, Bank of Japan announced “Expansion of the Quantitative and Qualitative Monetary Easing”, Market is NOT Chronically Inefficient by the Mechanism we showed
  25. 25. 2525 25 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 δl/δo δl/δo for 1 minute 31 October 2014~5 November 2014 31 October 4 November 5 November 31 October 2014 at 13:44 Japan time, Bank of Japan announced it. For a few minutes after the announcement, orders are crowded. We cannot deny market inefficiency for less than one minute. Market is NOT Inefficient even for one minuteMarket is NOT Inefficient even for one minute Future WorksFuture Works
  26. 26. 26262626 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Empirical Study to Compare (5) Summary & Future Works
  27. 27. 27272727 Summary * The ratio (δl/δo) is key parameter, Latency(δl) per Order Interval (δo) * Enough fast market system is required δl << δo. * Stop market trend -> Large Latency -> agents cannot change Estimate price, quickly -> Unnecessary market following trades -> Increasing Execution Rate -> Expanding Bid Ask Spread -> Market becomes Inefficient * Before arrowhead: It is Possible that Market is Chronically Inefficient * After arrowhead: Market is NOT Inefficient even for one minute
  28. 28. 28282828 Future Works * We should discuss the case of very crowded orders for less than one minute, for example, at announced great market impacting information. -> needed simulation and empirical studies <- Certainly, such very short time scale event does not effect to general investors much. <-> It may effect to High Frequency Trading very much. * We should discuss it in more kinds of agents. (For example: High Frequency Trading such as Market Maker strategy, Arbitrage Strategy, and so on.)
  29. 29. 2929292929 Appendix
  30. 30. 3030 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 marketmarket limit marketmarket Exist matching order Order executed immediately Exist matching order Order executed immediately No matching order Order not executed immediately No matching order Order not executed immediately buybuysellsell Agents decide an order price, if exist matching order, market order else limit order Agents decide an order price, if exist matching order, market order else limit order All agents decide an order price

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