Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns

Proposes a new methodology to aid in designing a portfolio of investment over multiple stock markets. It is our hypothesis that financial stock market trends may be predicted better over a set of markets instead of any one single market. A selection criterion is proposed in this paper to make this choice effectively. This criterion is based upon the observed backpropagation and recurrent neural networks' prediction accuracy, and the overall change recorded in the previous year. The results obtained when using data for four consecutive years over five international stock markets supports our claim. Backpropagation networks use gradient descent to learn spatial relationships. On the other hand, recurrent networks are capable of capturing spatiotemporal information from training data. This paper analyzes application of recurrent networks to the stock market return prediction problem in contrast with backpropagation networks. On the basis of the results observed during these experiments it follows that the effect of learning temporal information was not substantial on the prediction accuracy for the stock market returns.