رابطه بین مدیریت درآمد و صورت های مالی کلاهبرداری

This paper provides new evidence on the characteristics of firms that commit financial statement fraud. We examine how previous earnings management impacts the likelihood that a firm will commit financial statement fraud and in doing so develop three new fraud predictors. Using a sample of 54 fraud and 54 non-fraud firms, we find that fraud firms are more likely to have managed earnings in prior years and that earnings management in prior years is associated with a higher likelihood that firms that meet or beat analyst forecasts or that inflate revenue are committing fraud. We further find that fraud firms are more likely to meet or beat analyst forecasts and inflate revenue than non-fraud firms are even when there is no evidence of prior earnings management. This paper contributes to the fraud detection literature and the earnings management literature, and can help practitioners and regulators develop better fraud detection models.

The Association of Certified Fraud Examiners (ACFE, 2008) estimates that occupational fraud, or fraud in the workplace, costs the U.S. economy $994 billion per year. Within occupational fraud, financial statement fraud1 has the highest per case cost and total cost to the defrauded organizations, with an estimated total cost of $572 billion per year in the U.S.2 In addition to the direct impact on the defrauded organizations, fraud adversely impacts employees, shareholders and creditors. Financial statement fraud (henceforth fraud) also has broader, indirect negative effects on market participants by undermining the reliability of corporate financial statements and confidence in financial markets, resulting in higher risk premiums and less efficient capital markets. Research about fraud antecedents and detection is important because it adds to the understanding about fraud, which has the potential to improve auditors' and regulators' ability to detect fraud either directly or by serving as a foundation to future fraud research that does. Improved fraud detection can help defrauded organizations, and their employees, shareholders, and creditors curb costs associated with fraud, and can also help improve market efficiency. This knowledge is also of interest to auditors when providing assurance regarding whether financial statements are free of material misstatements caused by fraud, especially during client selection and continuation judgments, and audit planning. This research contributes to the literature on fraud antecedents by examining the relation between earnings management and fraud. Firms can manipulate financial statements by managing earnings using discretionary accruals or by committing fraud. However, as accruals reverse over time (Healy, 1985), firms that manage earnings must later either deal with the consequences of the accrual reversals or commit fraud to offset the reversals (Dechow et al., 1996, Beneish, 1997, Beneish, 1999 and Lee et al., 1999). Using income-increasing discretionary accruals over multiple years can also cause managers to run out of ways to manage earnings. Therefore, firms that manipulate financial statements over multiple years, for example to meet or beat analyst forecasts or to inflate revenue, become increasingly likely to use fraud rather than earnings management to manipulate financial statements. Based on this link between earnings management and fraud, we address five research questions related to how previous earnings management impacts fraud in the current year. More specifically, we examine the relation between previous earnings management and (1) the likelihood that firms that meet or beat analyst forecasts are committing fraud and (2) the likelihood that firms with inflated revenue are committing fraud. Additionally, we examine (3) the relation between previous earnings management and the likelihood of fraud, assuming no evidence of inflated revenue and no evidence of financial statement manipulation to meet or beat analyst forecasts, (4) the relation between meeting or beating analyst forecasts and the likelihood of fraud when there is no evidence of previous earnings management, and (5) the relation between inflated revenue and the likelihood of fraud when there is no evidence of previous earnings management. Our results show that the likelihood of fraud is significantly higher for firms that have previously managed earnings even when there is no evidence of inflated revenue and when they do not meet or beat analyst forecasts. We further find that firms that meet or beat analyst forecasts or inflate reported revenue are more likely to be committing fraud, even when there is no evidence of previously managed earnings. The results also show that previous earnings management is associated with a higher likelihood that firms that meet or beat analyst forecasts are committing fraud and a higher likelihood that firms with inflated revenue are committing fraud. These findings contribute to the fraud detection literature and earnings management literature, and also contribute to practice by improving auditors' and regulators' ability to detect fraud. In addition to contributing to prior research by examining the link between earnings management and fraud, we develop three new measures, Aggregated Prior Discretionary Accruals, Meeting or Beating Analyst Forecasts, and Unexpected Revenue per Employee, that can be used to detect fraud. These new measures represent refinements of prior research and thus provide relatively minor contributions compared to the examination of the link between earnings management and fraud. More specifically, our prior earnings management measure, Aggregated Prior Discretionary Accruals, is based on a previously conjectured, but only partially tested, relation. In addition, we investigate whether pressure to meet or beat analyst forecasts provides an incentive to commit fraud. 3 Prior research has shown that pressure to meet or beat analyst forecasts provides an incentive to manage earnings, but not whether it provides an incentive to commit fraud or whether this relation can be used to detect fraud. We also develop a completely new measure, Unexpected Revenue per Employee that is designed to detect revenue fraud, i.e., inflated revenue. These three new measures are important as they can enhance practitioners' ability to detect fraud. This paper is organized as follows. We define earnings management, fraud, and financial statement manipulation, review related fraud research, and develop our hypotheses in Section 2. We describe our sample selection criteria and research design in Section 3. We present empirical results in Section 4. Concluding remarks appear in Section 5.

This research provides new evidence regarding the characteristics of firms that commit fraud. It contributes to the body of research that describes the antecedents of fraud, and therefore also facilitates fraud detection. More specifically, we examine the relation between previous earnings management and the propensity to commit fraud and in doing so develop three new measures: Aggregated Prior Discretionary Accruals, Meeting or Beating Analyst Forecasts, and Unexpected Revenue per Employee. The first new measure, Aggregated Prior Discretionary Accruals, sums discretionary accruals over the three years prior to the first fraud year to capture the pressure of earnings reversals and earnings management constraints. We find that firms that have previously managed earnings are more likely to commit fraud even when there is no evidence of earnings manipulation to meet or beat analyst forecasts or inflate revenue. We also perform more in depth analyses of the earnings management reversal and constraint hypothesis and find that measures of prior discretionary accruals summed over three years have more predictive ability than those summed over two years or one year. The second measure, Meeting or Beating Analyst Forecasts, measures whether firms meet or beat analyst forecasts or fail to do so. We find that firms that meet or beat analyst forecasts are more likely to be committing fraud even when there is no evidence of prior earnings management. In addition to showing that evidence of a firm meeting or beating analyst forecasts can be used to detect fraud, this study contributes to earnings management research investigating capital market expectations, which typically assumes that distributional inconsistencies in reported earnings around analyst forecasts indicate that some firms manage earnings to meet analyst forecasts. Our results are consistent with capital market expectations providing an incentive for firms to manipulate financial statements and thus corroborate the findings of earnings management research. We also develop a new productivity-based measure, Unexpected Revenue per Employee, designed to capture revenue fraud. The results indicate that this measure can facilitate fraud prediction. More specifically, we find some evidence that firms with inflated revenue are more likely to be committing fraud even when they have not managed earnings in prior years. It should, also, be noted this relation becomes stronger when outliers are deleted from the sample. It is possible that because this measure is designed specifically to capture revenue fraud, including firms that commit other types of fraud in the sample weakens the results. Future research might investigate additional measures designed to capture other types of fraud in conjunction with Unexpected Revenue per Employee. More importantly, we contribute to the understanding of fraud antecedents by examining the link between earnings management and fraud and how prior earnings management interacts with other fraud antecedents. In doing so we obtain results that are consistent with positive associations between capital market related fraud incentives and fraud and between inflated revenue and fraud that are increasing in prior years' earnings management. In other words, our results indicate that it is more likely that firms that have (1) incentives to commit fraud will commit fraud if they have managed earnings in prior years, and (2) inflated revenue have committed fraud if they have managed earnings in prior years. In addition to contributing to fraud literature and earnings management literature, the improved understanding about the link between earnings management and fraud and the variables developed can be used to build better fraud prediction models. Better fraud models can be useful to auditors during client selection and continuation judgments, and audit planning. Regulatory bodies such as the SEC can also leverage these results to improve their effectiveness and efficiency when monitoring and selecting firms to investigate for potential fraud. These results, however, have some limitations. Because the sample of fraud firms was identified using SEC AAER, results might not fully generalize to other types of fraud. That is, results might apply only to fraud firms investigated by the SEC. Other limitations provide opportunities for future research. We propose that total discretionary accruals increase the likelihood of fraud through two processes: previous earnings management puts pressure on management as the accruals reverse and constrains current earnings management flexibility. Our results document a positive relation between prior earnings management and fraud, but we do not provide any direct evidence of this being caused by earnings management reversals or earnings management constraints. Future research can explore these two dimensions further. Future research can also examine whether discretionary accruals growth, in addition to aggregate levels, in the years leading up to the first fraud year predicts fraud. It would also be interesting to examine whether prior earnings management strengthens the relations between other fraud antecedents and fraud. Future research can also examine fraud incentives related to capital market expectations other than Meeting or Beating Analyst Forecasts. For example, do firms commit fraud in order to avoid reporting small losses or small earnings growth declines? Further, it might be possible to improve Unexpected Revenue per Employee by adjusting the denominator to count only the number of employees that are actually involved in revenue generating activities. Future advances in financial reporting, such as XBRL, might provide additional data necessary to implement such adjustments.