Neuro-evolutionary approach to stock market prediction

A neuro-evolutionary method for a short-term stock index prediction is presented. The data is gathered from the German stock exchange (the target market) and two other markets (Tokyo stock exchange and New York stock exchange) together with EUR/USD and USD/JPY exchange rates. Neural networks supported by genetic algorithm (GA) are used as the prediction engine. The GA is used to find suboptimal set of input variables for a one day prediction. Due to high volatility of mutual relations between input variables, a particular choice of input variables found by the GA is valid only for a short period of time and a new set of inputs is generated every 5 days. The method of selecting input variables works efficiently. Variables which are no longer useful are exchanged with the new ones. On the other hand some particularly useful variables are consequently utilized by the GA in subsequent independent steps. Simulation results of the proposed neuro-evolutionary system applied to prediction of the percentage change of closing value of DAX index are very promising and competitive to the ones obtained by the three other heuristical models implemented and tested for comparison.