Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market?

Abstract

Unpredictable stock market factors make it difficult to predict stock index futures. Although efforts to develop an effective prediction method have a long history, recent developments in artificial intelligence and the use of artificial neural networks have increased our success in nonlinear approximation. When we study financial markets, we can now extract features from a big data environment without prior predictive information. We here propose to further improve this predictive performance using a combination of a deep-learning-based stock index futures prediction model, an autoencoder, and a restricted Boltzmann machine. We use high-frequency data to examine the predictive performance of deep learning, and we compare three traditional artificial neural networks: 1) the back propagation neural network; 2) the extreme learning machine; and 3) the radial basis function neural network. We use all of the 1-min high-frequency transaction data of the CSI 300 futures contract (IF1704) in our empirical analysis, and we test three groups of different volume samples to validate our observations. We find that the deep learning method of predicting stock index futures outperforms the back propagation, the extreme learning machine, and the radial basis function neural network in its fitting degree and directional predictive accuracy. We also find that increasing the amount of data increases predictive performance. This indicates that deep learning captures the nonlinear features of transaction data and can serve as a powerful stock index futures prediction tool for financial market investors.