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在股票连接计划的背景下,预测中国的股票市场风险
Wei Chen1, Bing Chen1, Xin Cai1
1School of Management and Engineering, Capital University of Economics and Business, Beijing, China.
Soft computing
|June 26, 2023
概括
本研究介绍了使用风险价值 (VaR) 和机器学习的中国股票市场风险预警系统. 该系统通过整合宏观经济和基本指标来提高预测的准确性,而Stock Connect计划的影响则有所不同.
科学领域:
- 金融市场是金融市场.
- 风险管理 风险管理
- 计算金融是一种计算金融.
背景情况:
- 通过股票连接计划,加强中国大陆和香港股票市场之间的整合.
- 在动态的金融环境中需要复杂的风险评估工具.
研究的目的:
- 开发和评估一种针对中国股票市场风险的新型预警系统.
- 评估股票连接计划对市场风险预测的影响.
- 确定改善风险预测的关键指标.
主要方法:
- 使用风险价值 (VaR) 来识别多类股票市场风险.
- 构建一个包括基本,技术,海外返回和宏观经济指标在内的综合指标系统.
- 采用机器学习模型:长短期内存 (LSTM),门反复单元 (GRU),多层感知器 (MLP) 和极端梯度增强 (XGBoost).
主要成果:
- 上海综合指数 (SSEC),深成分指数 (SZCZ) 和杭 Seng指数 (HSI) 的宏观经济和基本指标显著改善了风险预测.
- SH-HK 股票连接计划提高了预测性能,而 SZ-HK 股票连接计划降低了它.
- 与香港相关的指标在SZ-HK股票连接之后变得越来越重要.
结论:
- 开发的早期预警系统为中国股票市场风险评估提供了强有力的方法.
- 股票连接计划对市场风险预测有不同的影响,突出了需要量身定制的分析.
- 综合各种指标和先进的机器学习对于有效的财务风险管理至关重要.
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