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Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment

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Natural Computing in Computational Finance

Part of the book series: Studies in Computational Intelligence ((SCI,volume 380))

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Summary

This chapter presents a market microstructure model, which investigates the behavior dynamics in financial markets. We are especially interested in examining whether the markets’ behavior is non-stationary, because this implies that strategies from the past cannot be applied to future time periods, unless they have co-evolved with the markets. In order to test this, we employ Genetic Programming, which acts as an inference engine for trading rules, and Self-Organizing Maps, which is used for clustering the above rules into types of trading strategies. The results on four empirical financial markets show that their behavior constantly changes; thus, agents’ trading strategies need to continuously adapt to the changes taking place in the market, in order to remain effective.

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References

  1. Arthur, B.: On learning and adaptation in the economy, working paper 92-07-038. Santa Fe Institute (1992)

    Google Scholar 

  2. Arthur, W., Holland, J., LeBaron, B., Palmer, R., Tayler, P.: Asset pricing under endogenous expectations in an artificial stock market. In: Arthur, B., Durlauf, S., Lane, D.E. (eds.) The Economy as an Evolving Complex System II, pp. 15–44. Addison-Wesley, Reading (1997)

    Google Scholar 

  3. Banzhaf, W., Nordina, P., Keller, R., Francone, F.: Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, Heidelberg (1998)

    MATH  Google Scholar 

  4. Brock, W., Hommes, C.: A rational route to randomness. Econometrica 65, 1059–1095 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  5. Brock, W., Hommes, C.: Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22, 1235–1274 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  6. Brock, W., Hommes, C., Wagener, F.: Evolutionary dynamics in markets with many trader types. Journal of Mathematical Economics 41, 7–42 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  7. Chan, N., LeBaron, B., Lo, A., Poggio, T.: Agent-based models of financial markets: A comparison with experimental markets. MIT Artificial Markets Project Paper No.124 (1999)

    Google Scholar 

  8. Chen, S.H., Huang, Y.C., Wang, J.F.: Bounded rationality and the elasticity puzzle: An analysis of agent-based computational consumption capital asset pricing models. In: Zambelli, S. (ed.). Routledge, New York (2009)

    Google Scholar 

  9. Chen, S.H., Chang, C.L., Du, Y.R.: Agent-based economic models and econometrics. Journal of Knowledge Engineering Review (2010) (forthcoming)

    Google Scholar 

  10. Chen, S.H., Kampouridis, M., Tsang, E.: Microstructure dynamics and agent-based financial markets. In: Bosse, T., Geller, A., Jonker, C.M. (eds.) MABS 2010. LNCS(LNAI), vol. 6532, pp. 121–135. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Dickey, D., Fuller, W.: Distribution of the estimates for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427–431 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  12. Dicks, C., Van der Weide, R.: Herding asynchronous updating and heterogeneity in memory in a CBS. Journal of Economic Dynamics and Control 29(4), 741–763 (2005)

    Article  MathSciNet  Google Scholar 

  13. Diez-Roux, A.: A glossary for multilevel analysis. Journal of Epidemiology and Community Health 56, 588–594 (2002)

    Article  Google Scholar 

  14. Dittenbach, M., Rauber, A., Merkl, D.: Recent advances with the growing hierarchical self-organizing map. In: Allinson, N., Yin, H., Allinson, L., Slack, J. (eds.) Proceedings of the 3rd Workshop on Self-Organizing Maps. Advances in Self-Organizing Maps, pp. 140–145. Springer, Lincoln (2001)

    Google Scholar 

  15. Duffy, J., Engle-Warnick, J.: Using symbolic regression to infer strategies from experimental data, In: pp. 61–82. Springer, Heidelberg (2002); Evolutionary Computation in Economics and Finance

    Google Scholar 

  16. Gigerenzer, G., Todd, P.: Fast and Frugal Heuristics: The Adaptive Toolbox. In: Gigerenzer, G., Todd, P. (eds.), pp. 3–34. Oxford University Press, Oxford (1999); The ABC Research Group

    Google Scholar 

  17. Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  18. Izumi, K., Okatsu, T.: An artificial market analysis of exchange rate dynamics. In: Fogel, L.J., Angeline, P.J. (eds.) Evolutionary Programming V, pp. 27–36. MIT Press, Cambridge (1996)

    Google Scholar 

  19. Izumi, K., Ueda, K.: Analysis of dealers’ processing financial news based on an artificial market approach. Journal of Computational Intelligence in Finance 7, 23–33 (1999)

    Google Scholar 

  20. Kampouridis, M., Tsang, E.: EDDIE for investment opportunities forecasting: Extending the search space of the GP. In: Proceedings of the IEEE Conference on Evolutionary Computation, Barcelona, Spain, pp. 2019–2026 (2010)

    Google Scholar 

  21. Kampouridis, M., Chen, S.H., Tsang, E.: Testing the dinosaur hypothesis under empirical datasets. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 199–208. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  22. Kampouridis, M., Chen, S.H., Tsang, E.: Investigating the effect of different GP algorithms on the non-stationary behavior of financial markets. In: Computational Intelligence for Financial Engineering and Economics. IEEE Symposium Series on Computational Intelligence. IEEE Press, Los Alamitos (2011) (forthcoming)

    Google Scholar 

  23. Kirman, A.: Epidemics of Opinion and Speculative Bubbles in Financial Markets. In: Taylor, M. (ed.) Money and Financial Markets, pp. 354–368. Macmillan, London (1991)

    Google Scholar 

  24. Kirman, A.: Ants, rationality and recruitment. Quarterly Journal of Economics 108(1), 137–156 (1993)

    Article  Google Scholar 

  25. Kohonen, T.: Self-organized formation of topologically correct feature maps. Journal of Biological Cybernetics 43, 59–69 (1982)

    Article  MATH  Google Scholar 

  26. Koza, J.: Genetic Programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  27. Koza, J.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

  28. Koza J, Andre D, Bennett III F, Keane M (1999) GeneticProgramming 3: Darwinian Invention and Problem Solving. Morgan Kaufman

    Google Scholar 

  29. Koza, J., Keane, M., Streeter, M., Mydlowec, W., Yu, J., Lanza, G.: Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers, Dordrecht (2003)

    MATH  Google Scholar 

  30. Lo, A.: The adaptive market hypothesis: market efficiency from an evolutionary perspective. Journal of Portfolio Management 30, 15–29 (2004)

    Article  Google Scholar 

  31. Lo, A.: Reconciling efficient markets with behavioral finance: The adaptive markets hypothesis. Journal of Investment Consulting 2, 21–44 (2005)

    Google Scholar 

  32. Lux, T.: Herd behavior, bubbles and crashes. Economic Journal 105, 880–896 (1995)

    Article  Google Scholar 

  33. Lux, T.: Time variation of second moments from a noise trader/infection model. Journal of Economic Dynamics and Control 22, 1–38 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  34. Lux, T.: The socio-economic dynamics of speculative markets: Interacting agents, chaos and the fat tails of return distributions. Journal of Economic Behavior and Organization 33, 143–165 (1998)

    Article  Google Scholar 

  35. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)

    Google Scholar 

  36. O’Hara, M.: Market Microstructure Theory. Blackwell, Oxford (1995)

    Google Scholar 

  37. O’Hara, M., Easley, D.A.: Liquidity and valuation in an uncertain world. Journal of Financial Economics 97(1), 1–11 (2010a)

    Article  Google Scholar 

  38. O’Hara, M., Easley, D.A.: Microstructure and ambiguity. Journal of Finance 65(5), 1817–1846 (2010b)

    Article  Google Scholar 

  39. Palmer, R., Arthur, W., Holland, J., LeBaron, B., Tayler, P.: Artificial economic life: a simple model of a stock market. Physica D 75, 264–274 (1994)

    Article  MATH  Google Scholar 

  40. Phillips, P., Perron, P.: Testing for a unit root in time series regression. Biometric 75, 335–346 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  41. Poli, R., Langdon, W., McPhee, N.: A Field Guide to Genetic Programming (2008), http://Lulu.com

  42. Sansone, A., Garofalo, G.: Asset price dynamics in a financial market with heterogeneous trading strategies and time delays. Physica A 382, 247–257 (2007)

    Article  MathSciNet  Google Scholar 

  43. Simon, H.: Rational choice and the structure of environments. Psychological Review 63, 129–138 (1956)

    Article  Google Scholar 

  44. Xu, R., Wunsch, D.: Clustering. Wiley-IEEE Press (2008)

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, UK

    Michael Kampouridis & Edward Tsang

  2. AI-Econ Center, Department of Economics, National Cheng Chi University, Taipei, Taiwan

    Shu-Heng Chen

Authors
  1. Michael Kampouridis
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  2. Shu-Heng Chen
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  3. Edward Tsang
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Editors and Affiliations

  1. Quinn School of Business, University College Dublin, Belfield, 4, Dublin, Ireland

    Anthony Brabazon

  2. UCD Complex Adaptive Systems Laboratory, University College Dublin, Belfield, 4, Dublin, Ireland

    Michael O’Neill

  3. Büro 5.56 WirtschaftswissenschaftlichesZentrum (WWZ) Abteilung Quantitative Methoden, University of Basel, Peter Merian-Weg 6, 4002, Basel, Switzerland

    Dietmar Maringer

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Kampouridis, M., Chen, SH., Tsang, E. (2011). Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment. In: Brabazon, A., O’Neill, M., Maringer, D. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 380. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23336-4_10

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  • DOI: https://doi.org/10.1007/978-3-642-23336-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

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