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Detecting cumulative abnormal volume: a comparison of event study methods

机译:检测累积异常量:事件研究方法的比较

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摘要

A growing body of research in accounting and finance examines the reaction of trading volume to new information. The typical 'volume event study' employs a single-index market model borrowed mutatis mutandis from abnormal returns event studies. In this article, several alternative event study test statistics are compared using Brown and Warner (1985) style simulations, i.e. random samples of securities are drawn from the data set provided by the Center for Research in Security Prices (CRSP) and the empirical distributions of alternative test statistics are compared. In contrast to the extant literature, these simulations show that estimated generalized least squares with first- and second-order autoregressive structures do not offer material improvement over ordinary least squares (OLS) regression. A first-order moving average structure also does not offer material improvement. These simulations also show that test statistics that are robust with regard to cross-sectional heteroskedasticity are essential for testing the hypothesis that the cross-sectional mean cumulative abnormal log turnover is zero.
机译:越来越多的会计和金融研究机构研究交易量对新信息的反应。典型的“数量事件研究”采用的是从异常收益事件研究中比照借鉴的单指数市场模型。在本文中,使用Brown和Warner(1985)风格的模拟比较了几种替代的事件研究测试统计数据,即从证券价格研究中心(CRSP)提供的数据集和证券的经验分布中抽取了证券的随机样本。比较备用测试统计信息。与现有文献相反,这些模拟表明,具有一阶和二阶自回归结构的广义广义最小二乘估计不能提供比普通最小二乘(OLS)回归更大的材料。一阶移动平均结构也不能提供实质性的改进。这些模拟还表明,对于横截面异方差具有稳健性的检验统计量对于检验横截面平均累积异常对数周转为零的假设至关重要。

著录项

  • 来源
    《Applied Financial Economics Letters》 |2009年第9期|797-802|共6页
  • 作者

    Imre Karafiath;

  • 作者单位

    Finance, Insurance, Real Estate, Business Law, University of North Texas, P.O. Box 305339, Denton, TX 76203, USA;

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  • 原文格式 PDF
  • 正文语种 eng
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