Detecting Earnings Management

ESSENTIAL TOOLS AND STRATEGIES FOR DETECTING MANIPULATION. As recent corporate scandals prove, corrupt companies can maintain a façade of financial success through manipulation and fraud almost to the day they file for bankruptcy. Fortunately, tools exists to detect aggressive earnings management. This timely book reviews the current environment, explains the tools that can be used to detect a manipulative financial environment, and introduces techniques for recasting financial information to get a truer economic picture. Brief cases reflecting a variety of companies provide a feel for evaluating public data and how earning management potential can be analyzed. In addition, an appendix features a complete earnings management detection checklist that can be used to conduct a thorough analysis of any corporation. Detecting Earning Management will help readers: Identify the incentive of management to manipulate earning to promote their own short-term interests. Evaluate the effectiveness of corporate governance to limit short-term manipulation and promote long-term success. Consider whether recent regulations, such as Sarbanes-Oxley, will limit future abuse. Review the major fraud techniques used in the recent and not-so-recent scandals. Identify the potential areas of manipulation and other sources of distortion and develop appropriate detection strategies. Understand the challenging areas that can distort financial reality such as acquisitions, derivatives, and special purpose entities.

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.

Earnings management research is of interest not only to academics, but also to practitioners and regulators. A major strand of the relevant literature examines the divergent reporting incentives that managers face when reporting for tax and for financial accounting purposes. In case of conforming earnings management, firms that prefer tax aggressiveness also lower their financial accounting income, whereas firms that are aggressive in financial reporting also inflate taxable income. However, there is significant evidence that firms also take advantage of the opportunity provided by the dual reporting system (i.e., preparation of distinct reports) and simultaneously manipulate both accounting and taxable earnings (i.e., non-conforming earnings management). As the extent of earnings manipulation cannot be measured directly, a number of proxies have been developed in the literature relying on publicly available data. For the purposes of the present review, the most commonly used ones are classified into three groups: accrual models, effective tax rates and book-tax differences.

This book provides researchers and scholars with a comprehensive and up-to-date analysis of earnings management theory and literature. While it raises new questions for future research, the book can be also helpful to other parties who rely on financial reporting in making decisions like regulators, policy makers, shareholders, investors, and gatekeepers e.g., auditors and analysts. The book summarizes the existing literature and provides insight into new areas of research such as the differences between earnings management, fraud, earnings quality, impression management, and expectation management; the trade-off between earnings management activities; the special measures of earnings management; and the classification of earnings management motives based on a comprehensive theoretical framework.

"This paper evaluates alternative models for detecting earnings management. The paper restricts itself to models assuming that the construct being managed is total discretionary accruals, since such models are commonly used in the extant literature on earnings management. Existing models range from simple models in which discretionary accruals are measured as total accruals, to more sophisticated models that seek to separate total accruals into discretionary and non-discretionary components. There is, however, no direct evidence bearing on the relative performance of these alternative models at detecting earnings management. This paper evaluates the relative performance of these models by comparing the specification and power of commonly used statistical tests across the measures of discretionary accruals generated by the models."--Introduction.

This book is a study of earnings management, aimed at scholars and professionals in accounting, finance, economics, and law. The authors address research questions including: Why are earnings so important that firms feel compelled to manipulate them? What set of circumstances will induce earnings management? How will the interaction among management, boards of directors, investors, employees, suppliers, customers and regulators affect earnings management? How to design empirical research addressing earnings management? What are the limitations and strengths of current empirical models?