We investigate CEO compensation for completing M&A deals. We find that CEOs who have more power to influence board decisions receive significantly larger bonuses. We also find a positive relation between bonus compensation and measures of effort, but not between bonus compensation and deal performance. CEOs with more power also tend to engage in larger deals relative to the size of their own firms, and the market responds more negatively to their acquisition announcements. Our evidence is consistent with the argument that managerial power is the primary driver of M&A bonuses.

This paper examines the implications of using the absolute value of discretionary accruals when testing for earnings management. First, we analytically develop the mean and variance of the distribution of absolute discretionary accruals, and show that the expected value is an increasing function of the variance in the underlying error term from the first‐stage discretionary accrual estimation model. Second, we highlight several firm characteristics that are related to the error variance in discretionary accrual estimation models. Using simulations, we show that correlation between the earnings management partitioning variable and these firm characteristics leads to an overrejection of the null hypothesis of no earnings management. Third, we provide research design suggestions to help researchers mitigate the potential bias arising from the use of unsigned measures of earnings management. Using these suggestions, we replicate a recent study, and demonstrate that the inferences change after controlling for operating volatility.

We investigate whether firms use stock repurchases to meet or beat analysts’ earnings per share (EPS) forecasts. We identify conditions under which repurchases increase EPS and document the frequency of accretive repurchases from 1988 to 2001. We find a disproportionately large number of accretive stock repurchases among firms that would have missed analysts’ forecasts without the repurchase. The repurchase-induced component of earnings surprises appears to be discounted by the market, and this discount is larger when the repurchase seems motivated by EPS management, although using the repurchase to avoid missing analyst forecasts appears to mitigate some of the negative stock price response.

This paper examines how overconfidence affects the properties of management forecasts. Using both the “over‐optimism” and “miscalibration” dimensions of overconfidence to generate our predictions, we examine three research questions. First, we examine whether overconfidence increases the likelihood of issuing a forecast. Second, we examine whether overconfidence increases the amount of optimism in management forecasts. Third, we examine whether overconfidence increases the precision of the forecast. Using both options‐ and press‐based measures to proxy for individual overconfidence, we find support for all three research questions.

This paper employs the firm life‐cycle concept to extend our understanding of the mispricing of accrual and cash flow information by the stock market. We find that accruals and free cash flows are strongly (negatively) correlated in the maturity and decline stages of a firm's life cycle but not in the growth stage, suggesting that they capture unique information in the growth stage of the firm's life cycle but more correlated information in the later stages. Consistent with this finding, we show that the cash flows anomaly subsumes the accruals anomaly in maturity and decline stages, but not in the growth stage. Our findings contribute to the debate regarding the overlap between the two anomalies.

This paper investigates whether the accrual pricing anomaly documented by Sloan (1996. The Accounting Review 71(3), 289–316) for annual data holds for quarterly data and whether this form of market mispricing is distinct from the post-earnings announcement drift anomaly. We find that the market appears to overestimate the persistence of the accrual component of quarterly earnings and, therefore, tends to overprice accruals. Moreover, the accrual mispricing appears to be distinct from post-earnings announcement drift. A hedge portfolio trading strategy that exploits both forms of market mispricing generates abnormal returns in excess of those based on either unexpected earnings or accruals information alone.

This paper examines the impact of measuring accruals as the change in successive balance sheet accounts, as opposed to measuring accruals directly from the statement of cash flows. Our primary finding is that studies using a balance sheet approach to test for earnings management are potentially contaminated by measurement error in accruals estimates. In particular, if the partitioning variable used to indicate the presence of earnings management is correlated with the occurrence of mergers and acquisitions or discontinued operations, tests are biased and researchers are likely to erroneously conclude that earnings management exists when there is none. Additional results show that the errors in balance sheet accruals estimation can confound returns regressions where discretionary and non‐discretionary accruals are used as explanatory variables. Moreover, we demonstrate that tests of market mispricing of accruals will be understated due to erroneous classification of “extreme” accruals firms.

We analyze the standard error bias associated with the use of generated regressors—independent variables generated from first‐step regressions—in accounting research settings. Under general conditions, generated regressors do not affect the consistency of coefficient estimates. However, commonly used generated regressors can cause standard errors to be understated. Problematic generated regressors include predicted values, coefficient estimates, and measures derived from these estimates. Widely used generated regressors in accounting include measures of earnings persistence, normal accruals, litigation risk, and conditional conservatism. Using simple regression models and simulation, we demonstrate how generated regressors can produce understated standard errors in accounting research settings. We also demonstrate how the magnitude of the standard error bias is inversely related to the precision of the generated regressor. Finally, we discuss bootstrapping as a correction for the bias and demonstrate the pairs cluster bootstrap as a tool to improve inferences in common accounting settings involving generated regressors.