冠状病毒大流行对股票指数的影响:多项式回归与时间延迟
1Department of Applied Mathematics, Hong Kong Polytechnic University, 11 Yucai Road, Hung Hom, Hong Kong, Kowloon, China.
Heliyon
|April 16, 2024
概括
这项研究使用时间延迟的多项式回归来模拟中国股市指数和COVID-19病例之间的非线性关系. 最佳17周的延迟显著提高了股票市场预测的准确性,突出了流行病趋势的影响.
科学领域:
- 计量经济学 计量经济学
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- 中国的股票市场表现出受到冠状病毒大流行影响的波动性.
- 关于模拟股票指数与流行病趋势之间的非线性关系的研究有限.
- 流行病数据对股票市场的时间延迟影响的影响尚未得到充分研究.
研究的目的:
- 模拟股票市场指数与中国冠状病毒流行病之间的非线性关系.
- 调查时间延迟的COVID-19数据对股票市场波动的预测能力.
- 开发一个高精度的股票市场指数预测模型,使用多项式回归与最佳时间延迟.
主要方法:
- 利用了2020年1月至2022年7月的高频数据.
- 应用多项式回归,结合时间延迟来建模股票指数和新的COVID-19病例.
- 确定了多项式回归模型的最佳时间延迟和度.
主要成果:
- 时间延迟多项式回归模型显著改善了非线性关系的表示.
- 确定了17周的最佳时间延迟,导致预测准确度增加了16倍.
- 该模型成功地预测了未来的股票市场趋势,可以提前17周.
结论:
- 时间延迟的多项式回归有效地捕捉了COVID-19趋势和股票市场表现之间的动态关系.
- 该模型的预测能力取决于COVID-19相关时间趋势的不稳定性.
- 研究人员和决策者应该考虑对冠状病毒病例的不稳定性进行时间延迟建模,以改善经济预测.
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