Designing Financial Strategies based on Artificial Neural Networks Ensembles for Stock Markets

Before the advent of computers and Internet, the stock market investors perform their operations based mainly on intuition. With the growth of investments and online stock trading, a continued search for better tools to improve the prediction of stock market trends has become necessary in order to increase profits and reduce risks. In this work we propose and evaluate some algorithmic trading (algotrading) strategies, based on an Ensemble of artificial neural networks (ANN), to support the decision of stock market's investors. Thirty different ANN models, using different input sets of price, volume and technical indicators, were analyzed for different stock symbols (i.e., companies from different economy sectors) of the main Brazilian Stock Market-BM& FBovespa. Moreover, ensembles that combine the best ANN models were modeled and validated through different experiments. The results confirm the potential of the proposed strategies for algotrading.