在Covid-19期间识别股票回报异常,采用分组多重比较程序
1School of Finance and Accounting, Fuzhou University of International Studies and Trade, Fuzhou, 350202, China.
Economic analysis and policy
|June 22, 2023
概括
2020年,COVID-19大流行严重影响了中国的股票市场. 一种新的分组测试方法揭示了不同的行业表现,并更有效地突出了异常库存回报.
科学领域:
- 金融经济学 金融经济学
- 市场波动性分析市场波动性分析
- 事件研究方法论 事件研究方法论
背景情况:
- 随着COVID-19的流行,全球经济出现了前所未有的混乱.
- 了解股票市场对重大事件的反应对投资者和政策制定者来说至关重要.
- 在疫情期间对中国股市的先前分析中,缺乏对异常回报的先进统计检测方法.
研究的目的:
- 调查COVID-19大流行对2020年中国股市的影响.
- 用一种新的分组比较程序来识别异常的库存回报率.
- 将分组程序的有效性与用于检测事件诱导的市场异常的传统非分组方法进行比较.
主要方法:
- 利用中国市场内三个不同的行业的每日股票数据.
- 将识别异常股票回报作为多重假设测试问题的框架.
- 应用了分组比较程序,以加强对重大库存回报异常的检测.
主要成果:
- 经验结果表明,由于大流行,行业业绩受到了不同的影响.
- 与非分组方法相比,分组比较程序发现了更多突出的异常回报信号.
- 有证据表明,在重大事件期间的异常性能聚类在拟议的方法论下更好地检测到.
结论:
- COVID-19大流行对中国各个行业产生了不同的影响.
- 分组比较程序提供了一种优越的方法,用于在重大破坏性事件期间检测库存回报异常.
- 这项研究为分析股票市场对全球卫生危机的反应提供了新的视角.
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