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Evolution of trading strategies in a market with heterogeneously informed agents

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  • Florian Hauser

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  • Bob Kaempff

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Abstract

We present an agent-based simulation of an asset market with heterogeneously informed agents. Genetic programming is applied to optimize the agents’ trading strategies. After optimization, insiders are the only agents able to generate small systematic above-average returns. For all other agents, genetic programming finds a rich variety of trading strategies that are predominantly based on exclusive subsets of their information. This limits their price impact and prevents them from making systematic losses. The resulting low noise renders market prices as largely informationally efficient. Copyright Springer-Verlag 2013

Suggested Citation

  • Florian Hauser & Bob Kaempff, 2013. "Evolution of trading strategies in a market with heterogeneously informed agents," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 575-607, July.
  • Handle: RePEc:spr:joevec:v:23:y:2013:i:3:p:575-607 DOI: 10.1007/s00191-011-0232-6
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    References listed on IDEAS

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    1. Huber, Jurgen & Kirchler, Michael & Sutter, Matthias, 2008. "Is more information always better: Experimental financial markets with cumulative information," Journal of Economic Behavior & Organization, Elsevier, vol. 65(1), pages 86-104, January.
    2. Jegadeesh, Narasimhan & Kim, Woojin, 2006. "Value of analyst recommendations: International evidence," Journal of Financial Markets, Elsevier, vol. 9(3), pages 274-309, August.
    3. Grossman, Sanford J, 1976. "On the Efficiency of Competitive Stock Markets Where Trades Have Diverse Information," Journal of Finance, American Finance Association, vol. 31(2), pages 573-585, May.
    4. Glosten, Lawrence R, 1989. "Insider Trading, Liquidity, and the Role of the Monopolist Specialist," The Journal of Business, University of Chicago Press, vol. 62(2), pages 211-235, April.
    5. Pfeifer, Christian & Schredelseker, Klaus & Seeber, Gilg U.H., 2009. "On the negative value of information in informationally inefficient markets: Calculations for large number of traders," European Journal of Operational Research, Elsevier, vol. 195(1), pages 117-126, May.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    7. Jeff Madura & Thanh Ngo, 2008. "Impact of ETF inception on the valuation and trading of component stocks," Applied Financial Economics, Taylor & Francis Journals, pages 995-1007.
    8. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    9. Figlewski, Stephen, 1982. " Information Diversity and Market Behavior," Journal of Finance, American Finance Association, vol. 37(1), pages 87-102, March.
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    Citations

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    Cited by:

    1. Giulio Bottazzi & Pietro Dindo, 2013. "Evolution and market behavior in economics and finance: introduction to the special issue," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 507-512, July.
    2. Marinelli, Carlo & Weissensteiner, Alex, 2014. "On the relation between forecast precision and trading profitability of financial analysts," Journal of Financial Markets, Elsevier, vol. 20(C), pages 39-60.
    3. Witte, Björn-Christopher, 2012. "Fund managers - Why the best might be the worst: On the evolutionary vigor of risk-seeking behavior," Economics Discussion Papers 2012-20, Kiel Institute for the World Economy (IfW).
    4. Haijun Yang & Harry Wang & Gui Sun & Li Wang, 2015. "A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 901-924, November.
    5. Florian Hauser & Jürgen Huber & Bob Kaempff, 2015. "Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 205-229, August.

    More about this item

    Keywords

    Agent-based simulation; Heterogeneous agents; Trading strategies; Genetic programming; D82; D58; C61; G1;

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G1 - Financial Economics - - General Financial Markets

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