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The Role of Heterogeneous Agents’ Past and Forward Time Horizons in Formulating Computational Models

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  • Serge Hayward

Abstract

The conditioning of strategies by market environment and the simultaneous emergence of market structure in the presence of evolving trading strategies are investigated with major international stock indexes. Models for price forecasting and trading strategies evolution are examined under different time horizons. The results demonstrate that trading strategies can become performative in thin markets, thereby shaping the price dynamics, which in turn feeds back into the strategy. The dominance in thin markets by some (short-memory) traders produces a better environment for learning profitable strategies with computational intelligence tools. The experiment conducted contradicts assertions that long-term fitness of traders is not a function of an accurate prediction, but only of an appropriate risk aversion through a stable saving rate. The stock traders’ economic performance is found to be best with a 1-year forward time horizon, and it deteriorates significantly for tests with horizons exceeding 2 years, identifying frequent structural breaks. To model the turmoil in an economic system with recurrent shocks, short-memory horizons are optimal, as older data is not informative about current or future states. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Serge Hayward, 2005. "The Role of Heterogeneous Agents’ Past and Forward Time Horizons in Formulating Computational Models," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 25-40, February.
    • Handle: RePEc:kap:compec:v:25:y:2005:i:1:p:25-40
    DOI: 10.1007/s10614-005-6246-0
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    References listed on IDEAS

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    1. Neely, Christopher J. & Weller, Paul A., 1999. "Technical trading rules in the European Monetary System," Journal of International Money and Finance, Elsevier, vol. 18(3), pages 429-458.
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    4. Ya-Chi Huang & Shu-Heng Chen, 2003. "Simulating the Evolution of Portfolio Behavior in a Multiple-Asset Agent-Based Artificial Stock Market," Computing in Economics and Finance 2003 62, Society for Computational Economics.
    5. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
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