A Long-term Recurrent Convolutional Network for Stock Index Prediction

Abstract

Stock index prediction aims to predict the future price of stock indexes, which plays a key role in seeking the maximum profit from stock investment. However, It has been proven to be a very difficult task because of its innate complexity, dynamics, and uncertainty. With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as long short-term memory networks (LSTMs) to capture the complex patterns hidden in market trends. In this paper, we propose a Long-term Recurrent Convolutional Network (LRCN), which combines convolutional layers and long-range temporal recursion and is end-to-end trainable. In the LRCN model, the two-dimensional convolutional neural network (2D-CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) learns the long-term temporal dependencies to improve stock index prediction. To evaluate the effectiveness of LRCN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed LRCN can significantly outperform several existing highly competitive methods.