A Comparative Study on the Individual Stock Price Prediction with the Application of Neural Network Models

Abstract

According to well-stablished results in the literature, the Long Short Term Memory (LSTM) model is one of learning models most widely used in stock price prediction given its characteristic feature. In this paper, we employ a novel neural network, Gated Recurrent Unit (GRU), in performing individual stock price prediction task in Chinese A-share market. As shown by the experiment results, GRU has comparable performance with LSTM and both them outperform the conventional Recurrent Neural Network (RNN) model. Further, regression analysis indicates that there may exist quadratic relationship between prediction accuracy and training data size. Thereby attempts have been made on adding nonlinear time-weight functions to substantially improve the prediction accuracy with the LSTM model.