Heterogeneous Agents Past and Forward Time Horizons in Setting Up a Computational Model
AbstractPrice 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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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 241.
Date of creation: 11 Aug 2004
Date of revision:
Artificial Neural Network; Genetic Algorithm; Heterogeneous Agents; Time Horizons; Memory Length; Economic Profitability; Statistical Accuracy; Financial Markets; Stock Trading Strategies;
Find related papers by 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
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- 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.
- Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
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