DETECTING EARNINGS MANAGEMENT
DETECTING EARNINGS MANAGEMENT
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DOI：
10.1002/9781119204763.ch4
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发表时间：
1994-02
期刊：
影响因子：
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通讯作者：
Patricia M. Dechow;Richard G. Sloan;A. P. Sweeney
中科院分区：
文献类型：
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作者：
Patricia M. Dechow;Richard G. Sloan;A. P. Sweeney
本文评估了用于检测盈余管理的替代模型。本文仅限于假设被管理的结构是可支配应计利润的模型，因为这种模型在现存的会计文献中常用。现有的模型范围从简单的模型，其中全权应计利润计量为总应计利润，以更复杂的模型，分为全权和非全权部分的总应计利润。在本文之前，没有系统的证据表明这些替代模型在检测盈余管理方面的相对表现。本文通过比较常用的检验统计量在每个模型产生的可操纵应计利润指标中的规格和功效，评估了竞争模型的相对表现。检验统计量的规格进行评估，通过检查的频率，他们产生I型错误的随机样本的公司年和样本的公司年极端的财务业绩。我们专注于极端的财务表现的样本，因为在以前的研究中调查的刺激往往与财务表现。第一个样本的公司是针对美国证券交易委员会涉嫌夸大年度收益和第二个样本是通过人为地引入盈余管理到一个随机样本的公司。
This paper evaluates alternative models for detecting earnings management. The paper restricts itself to models that assume the construct being managed is discretionary accruals, since such models are commonly used in the extant accounting literature. Existing models range from simple models in which discretionary accruals are measured as total accruals, to more sophisticated models that separate total accruals into a discretionary and a non-discretionary component. Prior to this paper, there had been no systematic evidence bearing on the relative performance of these alternative models at detecting earnings management. This paper evaluates the relative performance of the competing models by comparing the specification and power of commonly used test statistics across the measures of discretionary accruals generated by each model. The specification of the test statistics is evaluated by examining the frequency with which they generate type I errors for a random sample of firm-years and for samples of firm-years with extreme financial performance. We focus on samples with extreme financial performance because the stimuli investigated in previous research are frequently correlated with financial performance. The first sample of firms are targeted by the Securities and Exchange Commission for allegedly overstating annual earnings and the second sample is created by artificially introducing earnings management into a random sample of firms.