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Heterogeneous Agents Past and Forward Time Horizons in Setting Up a Computational Model

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  • Serge Hayward

Abstract

Price forecasting and trading strategies modelling are examined with major international stock indexes under different time horizons. Results demonstrate that an accurate prediction is equally important as a stable saving rate for long-term survivability. The best economic performances are achieved for a one-year investment horizon with longer training not necessarily leading to improved accuracy. Thin markets" dominance by a particular traders" type (e.g. short memory agents) results in a higher likelihood to learn with computational intelligence tools profitable strategies, used by dominant traders. An improvement in profitability is achieved for models optimized with genetic algorithm and fine-tuning of training/validation/testing distribution

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File URL: http://repec.org/sce2004/up.5449.1077915422.pdf
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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 241.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:241

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Web page: http://comp-econ.org/
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Related research

Keywords: Artificial Neural Network; Genetic Algorithm; Heterogeneous Agents; Time Horizons; Memory Length; Economic Profitability; Statistical Accuracy; Financial Markets; Stock Trading Strategies;

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  1. Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
  2. Sweeney, Richard J., 1988. "Some New Filter Rule Tests: Methods and Results," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 23(03), pages 285-300, September.
  3. 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.
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