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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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  • Serge Hayward, 2004. "Heterogeneous Agents Past and Forward Time Horizons in Setting Up a Computational Model," Computing in Economics and Finance 2004 241, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:241
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    References listed on IDEAS

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    More about this item

    Keywords

    Artificial Neural Network; Genetic Algorithm; Heterogeneous Agents; Time Horizons; Memory Length; Economic Profitability; Statistical Accuracy; Financial Markets; Stock Trading Strategies;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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