Stock forecasting using ensemble neural networks

Sammanfattning: This paper explores the viability of creating an artificial neural network for stock forecasting using an ensemble method, where each network is differentiated with a different set of input parameters. The inputs were chosen based on previous research and by using a stepwise addition parameter search method. The problem was approached as both a regression and a classification problem, where we evaluated the networks performance for the purpose of stock forecasting using relevant measurements. For the regression part, the result was negative: the neural network was not able to beat a naive prediction strategy. However, for classification, a modest but significant positive result was achieved.