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Measuring Real Activity Management†

Author

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  • Daniel Cohen
  • Shailendra Pandit
  • Charles E. Wasley
  • Tzachi Zach

Abstract

To test hypotheses about earnings management, many studies investigate managers' manipulation of real activities (real earnings management, REM). Tests using measures of abnormal REM hinge critically on the measurement of normal real activities. Yet, there is no systematic evidence on the statistical properties of commonly used REM measures. We provide such evidence by documenting the Type I error rates and power of the test of the REM measures commonly used in the literature. We find these measures are often misspecified with Type I error rates that deviate from the nominal significance level of the test, especially in samples of firms with extreme performance or firm characteristics. We also compare the specification and power of traditional REM measures with performance‐matched REM measures to see if the latter provide better specified and more powerful tests. While performance‐matched REM measures are not immune from misspecification in all settings, in general they are better specified under the null hypothesis (i.e., in terms of Type I errors) than are traditional REM measures. Comparisons of the power to detect abnormal REM reveal that neither approach, traditional or performance‐matched, is consistently more powerful than the other in terms of detecting abnormal REM ranging from 1 to 10 percent of (lagged) total assets. The absence of a dominant approach to measure abnormal REM leads us to recommend that future researchers report results using both traditional and performance‐matched measures, so that readers are able to clearly assess the reliability of the inferences drawn about the magnitude and significance of the abnormal REM documented in a given study. Évaluation de la gestion du résultat réel Afin de tester les hypothèses relatives à la gestion du résultat, maints chercheurs analysent la manipulation de l’activité réelle (gestion du résultat réel ou GRR) à laquelle se livrent les gestionnaires. Les tests faisant appel aux indicateurs d’anomalies de la GRR reposent surtout sur les indicateurs d’activité réelle normale. Pourtant, il n’existe pas de preuves établissant de manière systématique les propriétés statistiques des indicateurs de GRR couramment utilisés. Les auteurs produisent ce genre de preuves en documentant les coefficients d’erreur de type I et la puissance du test des indicateurs de GRR généralement utilisé par les chercheurs. Ils constatent que ces indicateurs sont souvent mal définis, les coefficients d’erreur de type I s’écartant du seuil de signification nominal du test, en particulier dans les échantillons de sociétés affichant une performance extrême ou des caractéristiques extrêmes. Les auteurs comparent également la définition et le pouvoir des indicateurs traditionnels de GRR aux indicateurs de GRR appariés selon la performance, afin de vérifier si les tests faisant appel à ces derniers sont mieux définis et plus puissants. Bien que les indicateurs de GRR appariés selon la performance ne soient pas à l’abri d’erreurs de définition dans tous les contextes, ces indicateurs assurent en général une meilleure définition quant à l’hypothèse nulle (soit en ce qui a trait aux erreurs de type I) que ne le font les indicateurs traditionnels de GRR. La comparaison du pouvoir respectif de la méthode traditionnelle et de la méthode d’appariement selon la performance de déceler les anomalies de GRR révèle que ni l’une ni l’autre n’est systématiquement supérieure à l’autre en ce qui a trait à la détection d’anomalies de GRR se situant entre 1 % et 10 % (de la valeur retardée) des actifs totaux. L’absence de supériorité d’une méthode d’évaluation des anomalies de GRR incite les auteurs à recommander que les chercheurs fassent désormais état des résultats que donnent à la fois les indicateurs traditionnels et les indicateurs appariés selon la performance, de sorte que les lecteurs soient à même d’évaluer précisément la fiabilité de leurs inférences à l’égard de l’ampleur et de l’importance d’une anomalie de GRR documentée dans une étude donnée.

Suggested Citation

  • Daniel Cohen & Shailendra Pandit & Charles E. Wasley & Tzachi Zach, 2020. "Measuring Real Activity Management†," Contemporary Accounting Research, John Wiley & Sons, vol. 37(2), pages 1172-1198, June.
  • Handle: RePEc:wly:coacre:v:37:y:2020:i:2:p:1172-1198
    DOI: 10.1111/1911-3846.12553
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