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Evolution of trading strategies in a market with heterogeneously informed agents

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  • Florian Hauser
  • Bob Kaempff

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

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  • 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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    Cited by:

    1. 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.
    2. 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 Kiel).
    3. Hauser, Florian & Schredelseker, Klaus, 2018. "Who benefits from insider regulation?," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 203-210.
    4. 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.
    5. 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.
    6. Wang, Zongrun & Chen, Songsheng, 2019. "Market efficiency, strategies and incomes of heterogeneously informed investors in a social network environment," Journal of Economic Behavior & Organization, Elsevier, vol. 158(C), pages 15-32.
    7. 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.

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    More about this item

    Keywords

    Agent-based simulation; Heterogeneous agents; Trading strategies; Genetic programming; D82; D58; C61; G1;
    All these keywords.

    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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