The Effect of Resilience in Optimal Execution with Artificial-Market Approach

  • Hiroyuki Matsui
  • Ryo Ohyama
Part of the Evolutionary Economics and Social Complexity Science book series (EESCS, volume 4)


This chapter reexamines the optimality of Obizhaeva and Wang’s strategy by replacing their assumption of resilience given in functional form with resilience caused by trader behavior. This attempt is based on the idea that trading causes every financial market phenomenon. In other words, stock price change occurs only as the result of trades. In that sense, resilience exogenously given in a functional form is not realistic since it does not consider trading behavior. This thesis focuses on the modeling of trader behavior. Three models are proposed and examined to determine their validity. We name these models the full, low, and zero intelligence models, respectively. The three models are named according to how strategic they are. After examining the validity of each model, we try to simulate the strategies of Bertsimas and Lo and Obizhaeva and Wang by replacing resilience given in a functional form with each validated model. Through simulation of the total execution costs of each optimal strategy, we can check which strategy is better. Obizhaeva and Wang’s strategy is said to always outperform that of Bertsimas and Lo under the assumption that resilience follows a certain functional form. This thesis casts doubt on this assumption owing to its arbitrary nature.


Spot Price Limit Price Execution Cost Market Impact Artificial Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Y. Akimoto, Development of Artificial Market U-Mart System and Application of Market Analysis. Doctoral thesis, Osaka Prefecture University, 2011. In JapaneseGoogle Scholar
  2. 2.
    R. Almgren, N. Chriss, Optimal execution of portfolio transactions. J. Risk 3, 5–40 (2001)Google Scholar
  3. 3.
    R. Almgren, J. Lorenz, Adaptive arrival price. Trading 2007 (1), 59–66 (2007)Google Scholar
  4. 4.
    D. Bertsimas, A.W. Lo, Optimal control of execution costs. J. Financ. Mark. 1 (1), 1–50 (1998)CrossRefGoogle Scholar
  5. 5.
    B. Biais, P. Hillion, C. Spatt, An empirical analysis of the limit order book and the order flow in the Paris bourse. J. Financ. 50 (5), 1655–1689 (1995)CrossRefGoogle Scholar
  6. 6.
    Z.L. Chen, Exploring Best Execution Strategy through Agent-based Simulation: Study on a Virtual Future Market System. Master thesis, Tokyo Institute of Technology, 2011. In JapaneseGoogle Scholar
  7. 7.
    L.K. Chan, J. Lakonishok, The behavior of stock prices around institutional trades. J. Financ. 50 (4), 1147–1174 (1995)CrossRefGoogle Scholar
  8. 8.
    J. Chen, H. Hong, M. Huang, J.D. Kubik, Does fund size erode mutual fund performance? The role of liquidity and organization. Am. Econ. Rev. 94 (5), 1276–1302 (2004)Google Scholar
  9. 9.
    M. Chlistalla, From minutes to seconds and beyond: measuring order-book resilience in fragmented electronic securities markets, in ECIS 2011 Proceedings, Helsinki, Finland, Paper 94 (2011)Google Scholar
  10. 10.
    H. Degryse, F. De Jong, M. Van Ravenswaaij, G. Wuyts, Aggressive orders and the resiliency of a limit order market. Rev. Financ. 9 (2), 201–242 (2005)CrossRefGoogle Scholar
  11. 11.
    D.K. Gode, S. Sunder, Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J. Polit. Econ. 101 (1), 119–137 (1993)CrossRefGoogle Scholar
  12. 12.
    P. Gomber, U. Schweickert, The Market Impact-Liquidity Measure in Electronic Securities Trading. Working Paper, 2002Google Scholar
  13. 13.
    L. Harris, Liquidity, Trading Rules, and Electronic Trading Systems. Monograph Series in Finance and Economics 1990, no. 4 (New York University Salomon Center, New York, 1991)Google Scholar
  14. 14.
    K. Izumi, The Complex Systems Approach to Artificial Market Analysis (Morikita Publishing, Tokyo, 2003). In JapaneseGoogle Scholar
  15. 15.
    D.B. Keim, A. Madhavan, Execution Costs and Investment Performance: An Empirical Analysis of Institutional Equity Trades. Rodney L White Center for Financial Research Working Paper, 1995Google Scholar
  16. 16.
    H. Kimura, A. Akiyama, Low intelligence model which can explain market liquidity. Trans. Oper. Res. Soc. Jpn. 52, 56–81 (2009). In JapaneseGoogle Scholar
  17. 17.
    H. Kita, N. Mori, H. Sato, Y. Koyama, Y. Akimoto, Learn Market Mechanisms by an Artificial Market: U-Mart Engineering Version (Kyoritsu Shuppan, Tokyo, 2009). In JapaneseGoogle Scholar
  18. 18.
    A.S. Kyle, Continuous auctions and insider trading. Econometrica 53 (6), 1315–1335 (1985)CrossRefGoogle Scholar
  19. 19.
    J. Large, Measuring the resiliency of an electronic limit order book. J. Financ. Mark. 10 (1), 1–25 (2007)CrossRefGoogle Scholar
  20. 20.
    H. Markowitz, Portfolioselection. J. Financ. 7 (1), 77–91 (1952)Google Scholar
  21. 21.
    S. Maslov, Simple model of a limit order-driven market. Phys. A 278 (3), 571–578 (2000)CrossRefGoogle Scholar
  22. 22.
    J. Muranaga, Consideration on the Liquidity of the Domestic Stock Market – Tick Data Analysis of the Tokyo Stock Exchange. IMES Discussion Paper, 2000-J-18, 2000. In JapaneseGoogle Scholar
  23. 23.
    A.A. Obizhaeva, J. Wang, Optimal trading strategy and supply/demand dynamics. J. Financ. Mark. 16 (1), 1–32 (2013)CrossRefGoogle Scholar
  24. 24.
    I. Ono, U-Mart Simulation Exercise. U-Mart Summer School 2013 distributed document, 2013. In JapaneseGoogle Scholar
  25. 25.
    I. Ono, Y. Nakashima, T. Yawata, N. Mori, Y. Akimoto, H. Sato, H. Matsui, H. Kita, Artificial market as a system design tool: proposals of the U-Mart system of Zaraba/Market making version. J. Jpn. Assoc. Evol. Econ. 11, 377–390 (2007). In JapaneseGoogle Scholar
  26. 26.
    A. Perold, The implementation shortfall: paper versus reality. J. Portf. Manag. 14, 4–9 (1988)CrossRefGoogle Scholar
  27. 27.
    A. Schied, T. Schoneborn, Risk aversion and the dynamics of optimal liquidation strategies in illiquid markets. Financ. Stoch. 13 (2), 181–204 (2009)CrossRefGoogle Scholar
  28. 28.
    V.L. Smith, An experimental study of competitive market behavior. J. Polit. Econ. 70 (2), 111–137 (1962)CrossRefGoogle Scholar
  29. 29.
    E. Smith, J.D. Farmer, L.S. Gillemot, S. Krishnamurthy, Statistical theory of the continuous double auction. Quant. Financ. 3 (6), 481–514 (2003)CrossRefGoogle Scholar
  30. 30.
    Tokyo Stock Exchange (eds.), Introduction to the Securities Market of Japan (Toyo Keizai, Tokyo, 2004). In JapaneseGoogle Scholar
  31. 31.
    R.M. Withanawasam, P.A. Whigham, T. Crack, I.M. Premachandra, An Empirical Investigation of the Maslov Limit Order Market Model. Discussion Paper, Department of Information Science, University of Otago, 2010Google Scholar
  32. 32.
    G. Wuyts, The impact of aggressive orders in an order-driven market: a simulation approach. Eur. J. Financ. 18 (10), 1015–1038 (2012)CrossRefGoogle Scholar
  33. 33.
    X.S. Yan, Liquidity, investment style, and the relation between fund size and fund performance. J. Financ. Quant. Anal. 43 (03), 741–767 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Japan 2016

Authors and Affiliations

  1. 1.Kyoto UniversityKyotoJapan

Personalised recommendations