Multi-factor Based Stock Price Prediction Using Hybrid Neural Networks with Attention Mechanism

Abstract

The prediction of time series data, such as stock prices, is difficult since there exist many factors that affect the prediction model. Also, the influence of different factors on a stock price may be linear or nonlinear. The generation of good models for stock prices challenge the researchers in recent years. Long Short-Term Memory (LSTM) is a variation of Recurrent Neural Network (RNN), which can capture temporal sequence and have gained great success on time series prediction. Also, Convolutional Neural Network (CNN) is superior for extracting features from multi-dimensional sequences. In this paper, we propose a CNN-LSTM hybrid neural network with multiple factors to predict stock prices. Moreover, we add an attention mechanism to improve the scalability and the accuracy of the CNN-LSTM model. In the experiments, we compare our proposed model with different approaches in two real stock datasets. The results confirm the efficiency and scalability of our proposed method.