Risk Analysis of A-H Share Connect Market Based on Deep Learning and BP Neural Network

Author:

Cui Rumeng1,Chen Wen1ORCID

Affiliation:

1. School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China

Abstract

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 10−6, and the goodness of fit R 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.

Funder

Chinese National Funding of Social Sciences

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Stock Price Prediction Based on Deep Learning;2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA);2023-11-28

2. Prophet-LSTM-BP Ensemble Carbon Trading Price Prediction Model;Computational Economics;2023-04-15

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