Training Artificial Neural Networks with Genetic Algorithms for Stock Forecasting

Accurate prediction of future stock market prices is of great importance to traders. The process can be automated using artificial neural networks. However, the conventional backward propagation of errors algorithm commonly used for training the networks suffers from the local minima problem. This study investigates whether investing more computational resources into training an artificial neural network using genetic algorithms over the conventional algorithm, to avoid the local minima problem, can result in higher prediction accuracy. The results indicate that there is no significant increase in accuracy to gain by investing resources into training with genetic algorithms, using our proposed model. Träning Av Artificiella Neuronnät I Syfte Att Förutsp̊a Aktiemarknaden