Covid波动机制下的证券诉讼事件研究

S. Feinstein, O. M. Villanueva
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引用次数: 0

摘要

事件研究在证券欺诈集体诉讼中起着核心作用。事件研究用于评估市场效率、价格影响、损失原因和损害,所有这些都是证券欺诈索赔的基本要素。然而,整个市场波动的大幅增加,比如2020年的Covid-19大流行,可能会使标准事件研究方法变得不可靠。在这些时期,t检验显著性的通常分布假设产生了太多的假阳性。在本文中,我们研究了问题的严重程度,并提出了一种解决方案,即利用Covid期间t统计量的经验分布来调整临界检验统计量值。我们通过在PreCovid和Covid期间样本中使用耐用消费品指数S5CODU的19个组成部分的应用程序来演示这种方法,并评估收益公告对股价的影响。我们表明,该方法恢复了正确的测试大小,消除了多余的虚假显著性,同时保留了大量的测试功率,以正确识别Covid期间的重大事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Securities Litigation Event Studies in the Covid Volatility Regime
Event studies play a central role in class action securities fraud litigation. Event studies are used to assess market efficiency, price impact, loss causation, and damages, all of which are essential elements of a securities fraud claim. However, a substantial increase in market-wide volatility, such as what happened in 2020 with the Covid-19 pandemic, can render the standard event study methodology unreliable. In these periods, the usual distributional assumptions for t-tests of significance produce too many false positives. In this paper, we examine the extent of the problem and propose a solution that employs the empirical distribution of the t-statistic during the Covid period to adjust the critical test statistic value. We demonstrate this methodology with an application using the 19 constituents of the consumer durables index S5CODU in PreCovid and Covid period samples, and with an evaluation of earnings announcement effects on stock prices. We show the methodology restores correct test size, eliminating excess spurious significance, while preserving substantial test power to correctly identify significant events in the Covid period.
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