<abstract><p>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.</p></abstract>

In this paper, we investigated the effectiveness of price limit on stock market with the correlation study and complex network technology. We proposed a time-migrated DCCA cross-correlation coefficient which is beneficial to detect the asynchronous correlations of nonstationary time series. The stock market network is constructed with the threshold method based on time-migrated DCCA. The effectiveness of the price limit during the stock market crash period is studied based on the time-migrated DCCA stock market network. The results indicate that the time-migrated DCCA ensures more relevant results than the equal-time DCCA method. An interesting finding is that the price limit has different effects on the stock market network at different stages of dynamic evolution. Market stabilization will be lowered and the systemic risk will be increased if the price limit is enhanced. Such studies are relevant for a better understanding of the stock market and have a significant contribution to the stock market in reality.