Application of multi-branch neural networks to stock market prediction

Recently, artificial neural networks (ANNs) have been utilized for financial market applications. On the other hand, we have so far shown that multi-branch neural networks (MBNNs) could have higher representation and generalization ability than conventional NNs. In this paper, we investigate the accuracy of prediction of TOPIX (Tokyo Stock Exchange Prices Indexes) using MBNNs. Using the TOPIX related values in time series and other information, MBNNs can learn the characteristics of time series and predict the TOPIX values of the next day. Several simulations were carried out in order to compare the proposed predictor using MBNNs with that using conventional NNs. The results show that the proposed method can have higher accuracy of the prediction.