Knowledge that Transforms

This study examines whether taxpayers intentionally avoid Internal Revenue Service (IRS) third‐party reports. In 2017 an IRS amendment created a quasi‐exogenous shock that reduced third‐party tax reporting of pari‐mutuel gambling winnings from certain types of wagers. I consider the effect that this rule change had on taxpayer behavior. Using a difference‐in‐differences research design comparing thoroughbred racing in the United States to Canada, I find a 27% increase in gambler's investment into wager‐types that became less likely to trigger third‐party reports. Further, I provide evidence that this effect was because of third‐party reporting, not withholding, and was stronger in more informed gambling populations. These findings suggest that taxpayers knowingly avoid third‐party reports, enabling underreporting of income to the IRS. This has important policy implications because underreported individual income is the largest driver of the $496 billion annual gap between legal tax liability and actual tax collections in the United States.

A firm that must decide whether to retain or terminate a manager can rely on several sources of information to assess managerial ability. When it relies on a performance signal and monitoring, we show that a more informative signal can surprisingly increase the value of monitoring. Then, signal precision and monitoring are complements. This happens if a more precise information system makes some signals more negative indicators of managerial ability that still do not trigger termination. When the turnover cost is high enough and the manager is more entrenched after a positive performance, an increase in signal precision increases expected monitoring. In firms with a high turnover cost, a less informative signal is compounded by worse monitoring after a disappointing performance. This “bad corporate governance trap” makes it hard for these firms to eventually improve performance.

Beginning in 2018, U.S. public firms were required to report the ratio of the chief executive officer's (CEO) compensation to their median employee's compensation in the annual proxy statement. Exploiting the staggered reporting of pay ratios, we find little evidence that total CEO compensation changes in response to pay ratio disclosure reform. However, we do find that boards significantly adjust the mix of compensation awarded by reducing the sensitivity of CEO pay to equity price changes, particularly when the CEO is likely to garner media scrutiny, and by reducing reliance on stock‐based and other compensation components that are most susceptible to media coverage surrounding the pay ratio disclosure. Firms ultimately disclosing higher pay ratios garner more media coverage around the filing of their proxy statement, and more negative‐toned coverage in the subsequent month. Finally, we find evidence that greater pay disparity is associated with greater selling activity by retail investors and more negative say‐on‐pay votes following pay ratio reform, consistent with a broad set of investors responding to public scrutiny resulting from pay ratio disclosures.

We examine the economic consequences of a rule designed to improve consumers' understanding of mortgage information. The 2015 TILA‐RESPA Integrated Disclosures rule (TRID) simplifies the mortgage disclosures provided to consumers. As a consequence, TRID‐affected mortgages become a less attractive investment opportunity to banks. Our main results document that mortgage applications affected by TRID are less likely to be approved following the rule's effective date. We find evidence consistent with both a decrease in consumers' information processing costs and an increase in banks' secondary market frictions, providing insight into the potential channels through which this reduction in mortgage credit operates. We also find that banks partially compensate for reduced mortgage lending by increasing small business lending, and that fintechs absorb mortgage demand in areas with reduced mortgage lending by banks. Our study documents real actions that firms take in response to disclosure transparency regulation and contributes to the literature on the economic consequences of such regulation.

We examine the deterrence effect of the Dodd–Frank whistleblower provision on accounting fraud. To facilitate causal inference, we use state False Claims Acts (FCAs), under which whistleblowing about accounting fraud at a firm invested in by a state's pension fund can result in monetary rewards from that state's government. We divide our sample into firms exposed and not exposed to whistleblowing risk from a state FCA during the 2008–2010 period that preceded the 2011 SEC implementation of the Dodd–Frank whistleblowing provision. We hypothesize that firms already exposed to a state FCA whistleblower law are less affected by the Dodd–Frank whistleblower provision. Using the companies exposed to a state FCA as control firms in our Dodd–Frank tests, the remaining firms constitute the treatment sample. We find that exposure to Dodd–Frank reduces the likelihood of accounting fraud of treatment firms by 12%–22% relative to control firms, but do not find that it affects audit fees.

We analyze the disposition of auditor‐proposed adjustments to financial statements. Our analyses address concerns, expressed by regulators and others, that auditors and their clients fixate on quantitative thresholds and overlook qualitative factors in assessing the materiality of discovered misstatements. Using a large sample of Public Company Accounting Oversight Board (PCAOB)‐inspected audits, we examine the frequency with which management records versus waives auditor‐proposed adjustments and whether waiving‐proposed adjustments ha consequences for reporting reliability and the audit process. We find waived adjustments are linked to lower financial reporting quality measured by material misstatements, to incentives to meet/beat earnings targets, and to the audit process, as measured by higher next‐period audit effort and fees and higher next‐period proposed adjustments. These effects on the audit process are consistent with auditors responding to the increased risk associated with waived adjustments. In an exploratory analysis, we find that controlling for the amount of proposed adjustments, auditor resignations are negatively associated with waived adjustments.

This study examines whether we can learn from the behavior of blockchain‐based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on‐chain service providers. The mapped data set permits us to empirically conduct several analyses. First, we analyze abnormal transfer volume in the vicinity of large‐scale highly visible terrorist attacks. We document evidence consistent with heightened activity in coin wallets belonging to unregulated exchanges and mixer services—central to laundering funds between terrorist groups and operatives on the ground. Next, we use forensic accounting techniques to follow the trails of funds associated with the Sri Lanka Easter bombing. Insights from this event corroborate our findings and aid in our construction of a blockchain‐based predictive model. Finally, using machine‐learning algorithms, we demonstrate that fund trails have predictive power in out‐of‐sample analysis. Our study is informative to researchers, regulators, and market players in providing methods for detecting the flow of terrorist funds on blockchain‐based systems using accounting knowledge and techniques.

Using an international sample of 2,113 initial coin offerings (ICOs), we explore the role of disclosure and information intermediaries in the unregulated crypto‐tokens market. First, we document substantial cross‐sectional variation in the voluntary disclosure practices of ventures seeking to raise capital through ICOs, such as the extent of information released in a prospectus‐type document called a white paper; releasing the technical source code; and communicating through social media platforms. Second, we find that, even with limited disclosure verifiability, ventures with higher levels of disclosure have a greater ability to raise capital. Finally, we find that this association is stronger in the presence of mechanisms that lend credibility to ventures’ voluntary disclosures, such as internal governance practices or external scrutiny from information intermediaries. Overall, our results suggest that voluntary disclosure and information intermediaries facilitate the functioning of ICOs as an alternative capital market.

We use machine learning methods and high‐dimensional detailed financial data to predict the direction of one‐year‐ahead earnings changes. Our models show significant out‐of‐sample predictive power: the area under the receiver operating characteristics curve ranges from 67.52% to 68.66%, significantly higher than the 50% of a random guess. The annual size‐adjusted returns to hedge portfolios formed based on the prediction of our models range from 5.02% to 9.74%. Our models outperform two conventional models that use logistic regressions and small sets of accounting variables, and professional analysts’ forecasts. Analyses suggest that the outperformance relative to the conventional models stems from both nonlinear predictor interactions missed by regressions and the use of more detailed financial data by machine learning.