School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.
Comput Intell Neurosci. 2022 Jul 21;2022:1921463. doi: 10.1155/2022/1921463. eCollection 2022.
China's Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect programs make it possible for investors to trade stocks within specified limits through the two stock exchanges. The A-H share exchange stock market is crucial to the opening of the Mainland market, but few studies have paid attention to the market risks of such stocks. Using deep learning and BP neural network algorithm, this study constructs a three-dimensional A-H share interconnection market risk prediction index system including stock price fundamental indicators, technical indicators, and macro indicators based on the CES300 Index. Taking the CES300 Index return as the output layer indicator, a BP neural network with a 21-10-1 structure is constructed, and the tan-sigmoid transfer function and the LM optimization algorithm training function are used for network training to predict the return of the A-H share interconnected stock market. The mean square error (MSE) converges to 10, and the goodness of fit reaches 0.9928 and validates the prediction accuracy of the BP neural network model. It provides an efficient and accurate risk prediction model for the A-H share interconnected market, which facilitates the interactive development of the Mainland and Hong Kong markets.
中国的沪港通和深港通计划使投资者能够通过两个证券交易所在规定的范围内进行股票交易。A-H 股互联互通股票市场对内地市场的开放至关重要，但很少有研究关注此类股票的市场风险。本研究使用深度学习和 BP 神经网络算法，基于 CES300 指数，构建了一个包括股票价格基本指标、技术指标和宏观指标的三维 A-H 股互联互通市场风险预测指标体系。以 CES300 指数收益率为输出层指标，构建了一个 21-10-1 结构的 BP 神经网络，采用 tan-sigmoid 传递函数和 LM 优化算法训练函数进行网络训练，预测 A-H 股互联互通股票市场的收益率。均方误差（MSE）收敛到 10，拟合优度达到 0.9928，验证了 BP 神经网络模型的预测精度。为 A-H 股互联互通市场提供了一个高效准确的风险预测模型，促进了内地和香港市场的互动发展。
