A prediction model for stock market based on the integration of independent component analysis and Multi-LSTM

In this paper, we investigate the statistical behaviors of the stock market complex network. A hybrid model is proposed to predict the variations of five stock prices in the securities plate sub-network. This model integrates independent component analysis (ICA) and multivariate long short-term memory (Multi-LSTM) neural network to analyze the trading noise and improve the prediction accuracy of stock prices in the sub-network. Firstly, we apply ICA to deconstruct the original dataset and remove the independent components that represent the trading noise. Secondly, the rest of the independent components are given to Multi-LSTM neural network. Finally, prediction results are reconstructed from the outputs of the Multi-LSTM neural network and the corresponding mixing matrix. The experiment results indicate that the hybrid model outperforms the benchmark approaches, especially in terms of the stock market complex network.