Training Artificial Neural Networks with Genetic Algorithms for Stock Forecasting: A comparative study between genetic algorithms and the backpropagation of errors algorithms for predicting stock prices
Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Accurate prediction of future stock market prices is of great importance to traders. The process can be automated using articial 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 ar-ticial 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 signicant increase in accuracy to gain by investing resources into training with genetic algorithms, using our proposed model.
Place, publisher, year, edition, pages
IdentifiersURN: urn:nbn:se:kth:diva-186447OAI: oai:DiVA.org:kth-186447DiVA: diva2:927323
Subject / course