Knowledge that Transforms

We develop the concept of auditor industry range as the extent to which an auditor has experiences in auditing clients from different industries, and we link this construct to auditor performance, drawing on prior research in psychology and cognitive science. We find that auditors with a wide range of industry experiences are more likely to require audit adjustments than auditors with a narrow range. We conduct an extensive set of analyses to mitigate the concern that our results are driven by endogenous matching between auditors and clients. The positive relation between auditor industry range and audit adjustments exists regardless of whether the industries covered by an auditor's portfolio exhibit strong or weak economic co-movement, and the relation is stronger for more complex clients, in more uncertain environments, and for auditors with more years of audit experience. Overall, our findings suggest that an auditor's diverse experiences in different industries can enhance audit quality.

This paper explores the effects of public information such as accounting earnings in a competitive lending setting with risk shifting. Debt financing creates incentives for borrowers to take on excessive risks, in particular in bad states of the world. If a privately informed inside creditor bids against outside creditors to extend a loan, public information levels the playing field, which affects the bidding and risk shifting. Nonetheless, a perfect public signal would yield the least efficient outcome: introducing some measurement noise alleviates risk shifting by subjecting outside creditors to the winner’s curse, allowing borrowers in bad states cheaper access to loans. However, for pessimistic priors about the borrower, greater public signal precision can alleviate risk shifting, at the margin. We discuss implications for financial reporting regulations along the business cycle and for creditor turnover.

Glaeser and Lang (2024; GL) reviews the accounting literature on innovation, which has increased substantially in recent years. GL makes an important contribution to accounting research by bringing into the literature the implications of Romer's Nobel Prize winning endogenous growth theory and by explaining how accounting research addresses questions related to innovation. We contribute to accounting research by building on GL's foundation to suggest three main paths forward for future innovation research. First, focus on innovation's three defining attributes: novelty, nonrivalry, and partial excludability. Second, determine the needs of various users of information about a firm's innovation activities and how to meet those needs; we focus on the needs of investors. Third, address questions our discussion highlights as potentially important for future research on financial reporting and innovation, including the crucial question of an innovation's identifiability.

We study the role of litigation risk in M&A valuations. Specifically, we hypothesize that litigation risk leads to strategic valuations in fairness opinions (FOs) obtained in M&A transactions. Employing a regulatory shock to merger litigation risk and focusing on the most common valuation techniques – peer firm comparables and DCF analysis – we find that target-sought FOs exhibit lower valuations when litigation risk is high. The effect is concentrated in deals with greater agency conflicts between target management and outside shareholders. Furthermore, downward-biased valuations reduce appraisal litigation but are also associated with lower premiums. In contrast to prior work suggesting that target-sought FOs are used to negotiate a higher takeover price, our findings imply that they are used, at least in part, to mitigate litigation risk and facilitate successful deal completion. Our findings are relevant to academics, practitioners, and regulators interested in M&A price formation, and highlight the role litigation plays therein.

We provide the first evidence of systematic bias among an emerging type of credit rating agency that relies on subscriptions from institutional clients as its primary source of revenue. Using data from Egan-Jones Ratings Company (EJR), a representative subscriber-paid rating agency, we show that EJR issues more optimistically biased credit ratings, less timely downgrades, and less accurate ratings for firms held by more EJR clients. Our evidence is consistent with EJR optimistically biasing its ratings to bolster subscriber revenue, which allows institutional clients to invest in riskier bonds with higher expected returns. Taken together, our findings suggest that the emergence of subscriber-paid rating agencies as an alternative to more traditional issuer-paid agencies is unlikely to resolve problems arising from conflicts of interest but rather alter the nature of these conflicts in the ratings process.

Economists have long observed that stock price bubbles are associated with corporate overinvestment. We study the ex-ante identification of bubbles (i.e. stock price booms followed by busts) by examining industry-level investments in net operating asset (NOA) accruals and stock returns for 49 countries around the world. Consistent with overinvestment in operating assets being key to bubble formation, we document five findings: (1) NOA accruals positively forecast the eventual crash of an industry price run-up; (2) NOA accruals negatively forecast stock returns following a run-up; (3) NOA accruals are positively associated with investor sentiment; (4) higher NOA accruals forecast more disappointing earnings relative to analysts’ expectations for run-up industries; and (5) NOA accruals are sharply stronger predictors of crashes, returns and analyst forecast errors following run-ups compared to other periods. Our results provide the first evidence that accounting information identifies stock price bubbles and suggest that financial statements are important for detecting and anticipating industry- and market-level inefficiencies.

We discuss Feng et al. (2024), which studies a dynamic model of delegated investment. The paper provides novel insights into the optimal contract between a principal and an agent who obtains private information about both the timing and profitability of investment opportunities. While the analytical analysis provides interesting findings, we have concerns about the validity of the paper’s empirical predictions. We extend the “conceptual” and “operational” levels of Libby boxes by adding an “analytical” level to offer a tool for assessing and developing the link between theoretical models and empirical tests.

We study credit access in informal economies where market institutions, such as financial reporting systems, auditing, and courts, are nonexistent or function poorly. Using the setting of a large bazaar in India, we find that community membership plays a vital role in access to credit. Wholesalers are more likely to provide credit and offer greater amounts of credit to within-community retailers, and are more lenient when these retailers are delinquent. Furthermore, wholesalers who lent preferentially to their community retailers pre-COVID are more likely to receive help from their community following the COVID-19–related income shock, particularly from same-community landlords and suppliers. Also, wholesalers with low endowments, those with greater within-community information flow about them, and those facing income shocks are more likely to provide preferential credit to their community retailers. Our findings are consistent with an indirect reciprocity mechanism explaining within-community credit flows.

We combine U.S. Census data with SEC enforcement actions to examine employees' outcomes, such as wages and turnover, before, during, and after periods of fraudulent financial reporting. We find that fraud firms’ employees lose about 50% of cumulative annual wages, compared to a matched sample, and the separation rate is much higher after fraud periods. Yet, employment growth at fraud firms is positive during fraud periods; these firms overbuild and hire new, lower-paid employees concurrent with the fraud, unlike firms in distress which tend to contract. When the fraud is revealed, firms shed workers, unwinding this abnormal growth and resulting in most of the negative wage consequences. Wage outcomes are particularly unfavorable in thin labor markets, and lower-wage employees, though unlikely to have perpetrated the fraud, experience more severe wage losses compared to higher-wage employees.

Distinguishing between recurring and nonrecurring components of earnings is a critical task in financial analysis and valuation. Academics and quantitative investors often rely on measures of recurring and nonrecurring components derived from standardized financial databases. We use unobserved components modeling and the Kalman smoother to obtain efficient ex-post estimates of the recurring and nonrecurring components of annual earnings. We then show that popular measures are significantly misspecified and that investors appear to anticipate a significant portion of the misspecification. Finally, we identify certain misclassified items that drive misspecification and provide algorithms to improve their ex-ante classification.