Stock closing price forecasting using ensembles of constructive neural networks

Abstract

Efficient automatic systems which continuously learn over long periods of time, and manage to evolve its knowledge, by discarding obsolete parts of it and acquiring new ones to reflect recent data, are difficult to be constructed. This paper addresses neural network (NN) learning in non-stationary environments related to financial markets, aiming at forecasting stock closing price. To face up this dynamic scenario, an efficient NN model is required. Therefore, Constructive Neural Networks (CoNN) were employed due to its self-adaptation capability, in contrast to regular NN which demands parameter adjustment. This paper investigates a possible ensemble organization, composed by NN's trained with the Cascade Correlation CoNN algorithm. An ensemble is an effective approach to non-stationary learning because it provides pre-defined rules that enable new learners - with new knowledge - to take part of the ensemble along data stream processing. Results obtained with data stream related with four different stocks are then analysed and favorably compared with those obtained with the traditional MLP NNs, trained with Backpropagation.