Financial forecasting using genetic algorithms

A new genetic-algorithm-b ased system is presented and applied to the task of predicting the future performances of individual stocks. The system, in its most general form, can be applied to any inductive machine-learning problem: given a database of examples, the system will return a general description applicable to examples both within and outside the database. This differs from traditional genetic algorithms, which perform optimization. The genetic algorithm system is compared to an established neural network system in the domain of financial forecasting, using the results from over 1600 stocks and roughly 5000 experiments. Synergy between the two systems is also examined. This study presents a new system that utilizes genetic algorithms (GAs) to predict the future performances of individual stocks. More generally, the system extends GAs from their traditional domain of optimization to inductive machine learning or classification. The overall learning system incorporates a GA, a niching method (for finding multiple solutions), and several other components (discussed in the section entitled Genetic Algorithms for Inductive Learning). Time-series forecasting is a special type of classification on which this study