Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Abstract

The prediction of stock price movement direction is significant in financial studies. In recent years, a number of deep learning models have gradually been applied for stock predictions. This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. The framework combines a convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for prediction. We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical indicators, and the correlation between stock indices. And in the three-dimensional input tensor, the technical indicators are converted into deterministic trend signals and the stock indices are ranked by Pearson product-moment correlation coefficient (PPMCC). When training, a fully connected network is used to drive the CNN to learn a feature vector, which acts as the input of concatenated LSTM. After both the CNN and the LSTM are trained well, they are finally used for prediction in the testing set. The experimental results demonstrate that the framework outperforms state-of-the-art models in predicting stock price movement direction.