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This paper investigates the effect of internal control deficiencies and their remediation on accrual quality. We first document that firms reporting internal control deficiencies have lower quality accruals as measured by accrual noise and absolute abnormal accruals relative to firms not reporting internal control problems. Second, we find that firms that report internal control deficiencies have significantly larger positive and larger negative abnormal accruals relative to control firms. This finding suggests internal control weaknesses are more likely to lead to unintentional errors that add noise to accruals than intentional misstatements that bias earnings upward. Third, we document that firms whose auditors confirm remediation of previously reported internal control deficiencies exhibit an increase in accrual quality relative to firms that do not remediate their control problems. Finally, we find firms that receive different internal control audit opinions in successive years exhibit changes in accrual quality consistent with changes in internal control quality. Collectively, our cross-sectional and intertemporal change tests provide strong evidence that the quality of internal control affects the quality of accruals.

The massive losses that banks incurred with the meltdown of the subprime mortgage market have raised concerns about their ability to continue lending to corporations. We investigate these concerns. We find that firms paid higher loan spreads during the subprime crisis. Importantly, the increase in loan spreads was higher for firms that borrowed from banks that incurred larger losses. These results hold after we control for firm-, bank-, and loan-specific factors, and account for endogeneity of bank losses. These findings, together with our evidence that borrowers took out smaller loans during the crisis when they borrowed from banks that incurred larger losses, lend support to the concerns about bank lending following their subprime losses.

We establish that the root cause of many goodwill write-offs is the buyers' overpriced shares at acquisition. Overpriced shares provide managers with strong incentives to exploit the overpricing by acquiring businesses, often paying more than the acquisition's synergies, setting the stage for subsequent goodwill write-offs. In particular, we document the following patterns: (1) Share overpricing is strongly and positively associated with the intensity of corporate acquisitions and the growth of accounting goodwill. (2) Share overpricing predicts goodwill write-offs and their magnitude. (3) Acquisitions by overpriced companies—a strategy often recommended by investment bankers and some academics—are often ill-advised (overpaid for and/or strategic misfit), exacerbating the post-acquisition negative returns of buyers beyond the reversal of the overpricing. Thus, managers' arguments notwithstanding, goodwill write-off is an important event highlighting a dysfunctional investment strategy.

We examine the impact of SFAS 133, Accounting for Derivative Instruments and Hedging Activities, on the reporting behavior of commercial banks and the informativeness of their financial statements. We argue that, because mandatory recognition of hedge ineffectiveness under SFAS 133 reduced banks' ability to smooth income through derivatives, banks that are more affected by SFAS 133 rely more on loan loss provisions to smooth income. We find evidence consistent with this argument. We also find that the increased reliance on loan loss provisions for smoothing income has impaired the informativeness of loan loss provisions for future loan defaults and bank stock returns.

We investigate the role of information technology (IT) in the collection of delinquent consumer debt. We argue that the widespread adoption of IT by the debt collection industry in the 1990s contributed to the observed expansion of unsecured risky lending such as credit cards. Our model stresses the importance of delinquency and private information about borrower solvency. The prevalence of delinquency implies that the costs of debt collection must be borne by lenders to sustain incentives to repay debt. IT mitigates informational asymmetries, allowing lenders to concentrate collection efforts on delinquent borrowers who are more likely to repay.

Prior research shows that during the pre-1990 bust financially weak banks managed income upward by delaying provisions for losses on heterogeneous loans. In contrast, we predict and find that during the 1990s boom profitable banks managed income downward by accelerating provisions for losses on homogeneous loans. Profitable banks obscured their income smoothing by accelerating charge-offs of homogeneous loans and by recording more gross charge-offs to offset recoveries of previously charged-off loans. Over the three years subsequent to the acceleration of charge-offs, they had higher and more persistent income before provisions for loan losses than other banks, consistent with income smoothing over a prolonged horizon.

Theories from the economics, management control, and organizational behavior literatures predict that when it is difficult to align incentives by contracting on output, aligning preferences via employee selection may provide a useful alternative. This study investigates this idea empirically using personnel and lending data from a financial services organization that implemented a highly decentralized business model. I exploit variation in this organization in whether or not employees are selected via channels that are likely to sort on the alignment of their preferences with organizational objectives. I find that employees selected through such channels are more likely to use decision-making authority in the granting and structuring of consumer loans than those who are not. Conditional on using decision-making authority, their decisions are also less risky ex post. These findings demonstrate employee selection as an important, but understudied, element of organizational control systems.

Prior research reveals that write-offs of long-lived assets are both large in magnitude and frequent in occurrence. Responding to calls for enhanced reporting of these items, the FASB issued SFAS No. 121, Accounting for the Impairment of Long-Lived Assets. However, its effect on the characteristics of reported write-offs remains unclear, as implementation requires inherently subjective estimates. Further, critics (including dissenting FASB board members and the SEC) question the standard's guidance. Motivated in part by this debate, this paper contrasts the characteristics of write-offs reported prior versus subsequent to the issuance of SFAS No. 121. Empirical results reveal that economic factors have a weaker association with write-offs reported after SFAS No. 121. This is consistent across macro, industry, and firm-specific variables. Results also indicate a higher association between write-offs and \"big bath\" reporting behavior after the standard's implementation, and that this \"big bath\" behavior more likely reflects opportunistic reporting by managers rather than the provision of their private information. These inferences are robust to a number of alternative specifications and variable definitions. Overall, the results suggest the reporting of write-offs under SFAS No. 121 has decreased in quality, consistent with criticisms of the standard.

Standard-setters and many investors argue that loan fair values provide more useful information about credit losses than historical cost information, while bankers and others generally disagree. We examine the ability of reported loan fair values to predict credit losses relative to the ability of net historical costs currently recognized under U.S. GAAP. Our analysis is important because credit losses in the banking sector can have severe and widespread economic effects, as the recent financial crisis demonstrates. Overall, we find that net historical loan costs are a better predictor of credit losses than reported loan fair values. Specifically, we find that historical cost information is more useful in predicting future net chargeoffs, non-performing loans, and bank failures over both short and long time horizons. Further tests indicate that the relative predictive ability of reported loan fair values improves in higher scrutiny environments, suggesting that a lack of scrutiny over reported loan fair values may contribute to our findings.

Real-world predictive data mining (classification or regression) problems are often cost sensitive, meaning that different types of prediction errors are not equally costly. While cost-sensitive learning methods for classification problems have been extensively studied recently, cost-sensitive regression has not been adequately addressed in the data mining literature yet. In this paper, we first advocate the use of average misprediction cost as a measure for assessing the performance of a cost-sensitive regression model. We then propose an efficient algorithm for tuning a regression model to further reduce its average misprediction cost. In contrast with previous statistical methods, which are tailored to particular cost functions, this algorithm can deal with any convex cost functions without modifying the underlying regression methods. We have evaluated the algorithm in bank loan charge-off forecasting, where underforecasting is considered much more costly than overforecasting. Our results show that the proposed algorithm significantly reduces the average misprediction costs of models learned with various base regression methods, such as linear regression, model tree, and neural network. The amount of cost reduction increases as the difference between the unit costs of the two types of errors (overprediction and underprediction) increases.