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A methodological note on detecting a location shift in the distribution of abnormal returns: A nonparametric approach

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  • RAMESH CHANDRA
  • KERMIT ROHRBACH

Abstract

. The distribution of market model residuals or other measures of prediction errors is skewed and leptokurtic. To detect a location shift in leptokurtic residuals, a nonparametric rank test may be more efficient than a parametric t†test. Event studies that use a nonparametric test generally use the sign test, the Wilcoxon test, or some variation of the Wilcoxon test. The sign and Wilcoxon test statistics are calculated from the event†period residuals, so skewness and potential cross†sectional dependencies are not accounted for, causing a concern about the reliability of these tests for event studies. In this paper we propose a Mann†Whitney (MW) rank statistic in which the rank of the event†period residual is calculated relative to the estimation†period residuals. The MW statistic is a nonparametric analogue of the standardized residual (the residual scaled by its estimated standard deviation) used in the parametric (Patell†type) t†test. The MW statistics enable us to account correctly for skewness as well as potential cross†sectional correlation, and they are more powerful than the sign, Wilcoxon, and standardized residual statistics in detecting a location shift in leptokurtic residuals. Our results are based on a Monte Carlo study of simulated residuals. We simulate cross†sectionally independent residuals (for noncontemporaneous events) and cross†sectionally correlated residuals (for contemporaneous events). Résumé. La distribution des résidus du modèle du marché ou d'autres mesures des erreurs prévisionnelles est asymétrique et leptocurtique. Un test de rang non paramétrique peut être plus efficace qu'un test t paramétrique dans la détection d'un déplacement dans les résidus leptocurtiques. Les études événementielles basées sur un test non paramétrique ont généralement recours au test des signes, au test de Wilcoxon ou à une variante du test de Wilcoxon. Les statistiques du test des signes et du test de Wilcoxon sont calculées à partir des résidus événement†période, de sorte que l'asymétrie et les dépendances transversales possibles ne sont pas prises en compte, ce qui occasionne un problème de fiabilité des tests en ce qui a trait à l'étude événementielle. Dans le présent document, les auteurs proposent une statistique de rang Mann†Whitney (MW) dans laquelle le rang des résidus événement†période est calculé par rapport aux résidus estimation†période. La statistique MW est l'équivalent non paramétrique du résidu normé (soit du résidu pondéré en fonction de son écart†type estimé) utilisé dans le test paramétrique du t (de type Patell). Les statistiques MW nous permettent de tenir compte de façon exacte de l'asymétrie ainsi que de la corrélation transversale possible, tout en étant plus puissantes que les statistiques des signes, de Wilcoxon et des résidus normés dans la détermination d'un déplacement dans les résidus leptocurtiques. Les résultats obtenus par les auteurs sont basés sur une étude de Monte Carlo des résidus̀ simulés. Ils simulent des résidus transversalement indépendants (pour des événements non simultanés) et des résidus transversalement corrélés (pour des événements simultanés).

Suggested Citation

  • Ramesh Chandra & Kermit Rohrbach, 1990. "A methodological note on detecting a location shift in the distribution of abnormal returns: A nonparametric approach," Contemporary Accounting Research, John Wiley & Sons, vol. 7(1), pages 123-141, September.
  • Handle: RePEc:wly:coacre:v:7:y:1990:i:1:p:123-141
    DOI: 10.1111/j.1911-3846.1990.tb00804.x
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    1. Patell, Jm, 1976. "Corporate Forecasts Of Earnings Per Share And Stock-Price Behavior - Empirical Tests," Journal of Accounting Research, Wiley Blackwell, vol. 14(2), pages 246-276.
    2. Ramesh Chandra & Bala V. Balachandran, 1990. "A synthesis of alternative testing procedures for event studies," Contemporary Accounting Research, John Wiley & Sons, vol. 6(2), pages 611-640, March.
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    10. Sefcik, Se & Thompson, R, 1986. "An Approach To Statistical-Inference In Cross-Sectional Models With Security Abnormal Returns As Dependent Variable," Journal of Accounting Research, Wiley Blackwell, vol. 24(2), pages 316-334.
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    1. Ramesh Chandra & Kermit J. Rohrbach & G. Lee Willinger, 1992. "Longitudinal rank tests for detecting location shift in the distribution of abnormal returns: An extension," Contemporary Accounting Research, John Wiley & Sons, vol. 9(1), pages 296-305, September.
    2. Jagjeev Dosanjh, 2017. "Exchange Initiatives and Market Efficiency: Evidence from the Australian Securities Exchange," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2017, January-A.
    3. Bing Xiang, 1993. "The Choice of Return†Generating Models and Cross†Sectional Dependence in Event Studies," Contemporary Accounting Research, John Wiley & Sons, vol. 9(2), pages 365-394, March.
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    5. Lytle, Laurian Casson & Joy, O. Maurice, 1996. "The Stock market impact of social pressure: The South African divestment case," The Quarterly Review of Economics and Finance, Elsevier, vol. 36(4), pages 507-527.

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