Building Financial Time Series Predictions with Evolutionary Artificial Neural Network

Price forecasting and trading strategies modeling 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.