We use slow and fast time-series momentum to characterize four stock market cycles—Bull, Correction, Bear, and Rebound. The steep market declines of Bears concentrate in high-risk states, yet predict negative expected returns, which is difficult to rationalize by most models of time-varying risk premia. Using a model to analyze slow and fast momentum strategies, we estimate both relatively high mean persistence and realization noise in U.S. stock market returns. Intermediate-speed momentum portfolios, formed by blending slow and fast momentum strategies, translate predictive information in market cycles into positive unconditional alpha, for which we propose a novel decomposition.

We measure the impact of reputation for proxy fighting on investor activism by estimating a dynamic model in which activists engage a sequence of target firms. Our estimation produces an evolving reputation measure for each activist and quantifies its impact on campaign frequency and outcomes. We find that high reputation activists initiate 3.5 times as many campaigns and extract 85% more settlements from targets, and that reputation-building incentives explain 20% of campaign initiations and 19% of proxy fights. Our estimates indicate these reputation effects combine to nearly double the value that activism adds for target shareholders.

This paper investigates whether investor-level taxes affect corporate payout policy decisions. We predict and find a surge of special dividends in the final months of 2010 and 2012, immediately before individual-level dividend tax rates were expected to increase. We also find evidence that immediately before the expected tax increases, firms altered the timing of their regular dividend payments by shifting what would normally be January regular dividend payments into the preceding December. To our knowledge this is the first evidence in the literature about changes in the timing of regular dividend payments in response to tax law changes. For both actions (specials and shifting), we find that it was more likely for a firm to respond to individual-level tax rates if insiders owned a relatively large amount of the firm. Overall, our paper provides evidence that managers consider individual-level taxes in making corporate payout decisions.

This paper provides evidence on a previously unidentified source of managerial incentives: concerns about post-retirement board service. Both the likelihood that a retired CEO serves on his own board two years after departure, as well as the likelihood of serving as an outside director on other boards, are positively and strongly related to his performance while CEO. Retention on the CEO's own board depends primarily on stock returns, while service on outside boards is better explained by accounting returns. The evidence also suggests that firms consider ability in choosing board members.

This study examines whether auditors can effectively use nonfinancial measures (NFMs) to assess the reasonableness of financial performance and, thereby, help detect financial statement fraud (hereafter, fraud). If auditors or other interested parties (e.g., directors, lenders, investors, or regulators) can identify NFMs (e.g., facilities growth) that are correlated with financial measures (e.g., revenue growth), inconsistent patterns between the NFMs and financial measures can be used to detect firms with high fraud risk. We find that the difference between financial and nonfinancial performance is significantly greater for firms that committed fraud than for their nonfraud competitors. We also find that this difference is a significant fraud indicator when included in a model containing variables that have previously been linked to the likelihood of fraud. Overall, our results provide empirical evidence suggesting that NFMs can be effectively used to assess fraud risk.

A search-theoretic model of the retail market for illegal drugs is developed. Trade occurs in bilateral, potentially long-lived matches between sellers and buyers. Buyers incur search costs when experimenting with a new seller. Moral hazard is present because buyers learn purity only after a trade is made. This model is consistent with some new stylized facts about the drugs market, and it is informative for policy design. The effectiveness of different enforcement strategies is evaluated, including some novel ones that leverage the moral hazard present in the market.

The digital evolution in auditing has triggered a rapid shift in auditors' required skill sets, with audit firms heavily investing in and extolling advanced data analytics and artificial intelligence (AI) capabilities. However, this strong emphasis on newly required digital skills can lead many experienced auditors, who perceive these competencies as their weaker areas, to feel disaffirmed in their abilities. We predict and find, across two experiments, that auditors who feel disaffirmed in their digital skills more defensively discount specialist advice that places higher versus lower reliance on AI, but that an intervention in which auditors affirm their traditional audit skills mitigates this defensive reaction. Absent self‐affirmation, higher specialist reliance on AI results in auditors denigrating the competence and quality of advice that specialists provide. These findings suggest that disaffirmation escalates AI aversion, offering important insights into how audit firms can foster less defensive decision‐making in the rapidly evolving audit environment.

The elimination of goodwill amortization in 2001 brought about significant change in how companies are required to account for goodwill. This change in accounting also brought with it new challenges for auditors, namely evaluating the reasonableness of management's assumptions related to goodwill valuation. In addition to introducing technical challenges, this task is particularly difficult given the misalignment in incentives it creates between managers who likely prefer to avoid recording an impairment and auditors who seek to minimize the bias in management's impairment testing. This study focuses on the consequences of the misaligned incentives that auditors face under the current goodwill assessment process. We find that the decision to record a goodwill impairment is associated with an increase in the probability of auditor dismissal. Consistent with the presence of significant friction with clients, our results also indicate that the likelihood of auditor dismissals is negatively related to the favorability of the impairment decision. Furthermore, we find that companies impairing goodwill prior to dismissing auditors subsequently employ auditors that are, on average, more favorable to clients in their impairment decisions.

We examine the link between board gender diversity and managerial ability to transform corporate resources into revenue. Drawing on a sample of U.S firms during the period 2001–2016, we find a positive and economically meaningful association between female directors on boards and managerial ability, particularly when female directors are in monitoring roles on the board. The documented effect is stronger when using a tenure weighted measure of female representation on boards; and more pronounced for firms that have three or more women on the board of directors, in line with the critical mass hypothesis. We uncover that critical mass of female directors in monitoring roles is particularly conducive to enhancing managerial ability. Our channel analysis tests further reveal a distinctive tendency of firms with more gender diverse boards to shape the human capital of the firm by promoting managers with more generalist managerial skills. We find consistent results when we employ propensity score matching estimates and difference-in-differences using sudden deaths of female directors as a potential shock to address endogeneity concerns. We discuss implications for theory and policy.

The unprecedented contagion of the SARS-CoV-2 virus, causative of COVID-19, has spawned watershed economic, social, ethical, and political upheaval—catalyzing severe polarization among the global populace. Ostensibly, to demonstrate the most appropriate path towards responding to the virus outbreak, public officials in the United States (“U.S.”), representing both Democratic and Republican parties, stand accused of unduly influencing COVID-19 records in their respective jurisdictions. This study investigates the role political partisanship may have played in decreasing the accuracy of publicly reported COVID-19 data in the U.S. Leveraging social identity theory, we contend that public officials may have manipulated the reporting records in accounting for COVID-19 infection cases and deaths to validate the effectiveness of political party objectives. We employ Benford's Law to assess misreporting and evaluate the integrity of county-level COVID-19 reporting data through the construction of four distinct political party classifications. Specifically, we cross the county voting majority for the 2016 presidential candidate for each U.S. state (Democratic and Republican) with the 2020 gubernatorial political party (Democratic and Republican) in which each county resides. For the sample period of January 21, 2020 through November 3, 2020 (Election Day), the study's results suggest that the reported COVID-19 infection cases and deaths in the U.S. violate Benford's Law in a manner consistent with underreporting. Our analysis reveals that Democratic counties demonstrate the smallest departures from Benford's Law while Republican counties demonstrate the greatest departures.