Recurrent convolutional neural kernel model for stock price movement prediction

Abstract

Stock price movement prediction plays important roles in decision making for investors. It was usually regarded as a binary classification task. In this paper, a recurrent convolutional neural kernel (RCNK) model was proposed, which learned complementary features from different sources of data, namely, historical price data and text data in the message board, to predict the stock price movement. It integrated the advantage of technical analysis and sentiment analysis. Different from previous studies, the text data was treated as sequential data and utilized the RCNK model to train sentiment embeddings with the temporal features. Besides, in the classification section of the model, the explicit kernel mapping layer was used to replace several full-connected layers. This operation reduced the parameters of the model and the risk of overfitting. In order to test the impact of treating the sentiment data as sequential data, the effectiveness of explicit kernel mapping layer and the usefulness integrating the technical analysis and sentiment analysis, the proposed model was compared with the other two deep learning models (recurrent convolutional neural network model and convolutional neural kernel model) and the models with only one source of data as input. The result showed that the proposed model outperformed the other models.