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

Serge Hayward

No 241, Computing in Economics and Finance 2004 from Society for Computational Economics

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

Keywords: Artificial Neural Network; Genetic Algorithm; Heterogeneous Agents; Time Horizons; Memory Length; Economic Profitability; Statistical Accuracy; Financial Markets; Stock Trading Strategies (search for similar items in EconPapers)
JEL-codes: C45 E37 G12 (search for similar items in EconPapers)
Date: 2004-08-11
New Economics Papers: this item is included in nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:241

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