Hybrid Information Mixing Module for Stock Movement Prediction

Abstract

With the continuing active research on deep learning, research on stock price prediction using deep learning has been actively conducted in the financial industry. This paper proposes a method for predicting stock price movement using stock and news data. The stock market is affected by many variables; thus, market volatility should be considered for predicting stock price movement. Because stock markets are efficient, all kinds of information are quickly reflected in stock prices. We create a new fusion mix by combining price and text data features and propose a hybrid information mixing module designed using two map blocks for effective interaction between the two features. We extract the multimodal interaction between the time-series features of the price data and the semantic features of the text data. In this paper, a multilayer perceptron-based model, the hybrid information mixing module, is applied to the stock price movement prediction to conduct a price fluctuation prediction experiment in a stock market with high volatility. In addition, the accuracy, Matthews correlation coefficient (MCC) and F1 score for the stock price movement prediction were used to verify the performance of the hybrid information mixing module.

Keywords

• Stock movement prediction
• time-series forecasting
• bidirectional encoder representations from transformer (BERT)
• gated recurrent units (GRU)
• multilayer perceptron (MLP)