Knowledge that Transforms

ABSTRACT 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.

ABSTRACT 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.

ABSTRACT 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.

ABSTRACT 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.

ABSTRACT 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.

ABSTRACT We examine how tax cuts that benefit some firms are related to the economic performance of their direct competitors. Consistent with tax cuts decreasing the cost of initiating competitive strategies, we find that a decrease in the tax burden for only a specific group of firms in the U.S. economy (i.e., “rivals”) has a negative economic effect on the performance of its direct competitors not directly exposed to the same tax cut (i.e., “competitors”). This negative externality is stronger when the relatively higher taxed competitors (1) are financially constrained, (2) operate in more competitive markets, (3) have similar products to their lower taxed rivals, (4) face rivals that retain more of their cash tax savings due to lower dividends and share repurchases, and (5) face lower taxed, but financially constrained, rivals. We also find that shareholders and lenders price the negative externality manifested in these competitors’ economic performance.

ABSTRACT We develop a measure of how information events impact investors' expectations of risk. The measure is broadly applicable and simple to implement. We derive it from an option‐pricing model, where investors anticipate an announcement that simultaneously conveys information on the announcer's expected future cash flows and risk profile. We empirically implement the measure using firms' earnings announcements, showing that it closely aligns with our model's predictions and offers strong forecasting power for firms' risk profiles, costs of capital, and future investments. We further highlight pitfalls of using simple changes in option‐implied volatilities to study information gleaned from earnings announcements. Finally, we apply our measure to study disclosure regulation, the efficacy of text‐based proxies, and market‐wide events, which we use to illustrate our measure's uses, and illuminate its potential limitations.

ABSTRACT This paper investigates the effect of mandatory disclosure requirements for private firms on their decision to go public. Using detailed project‐level data for biopharmaceutical firms, we explore the effects of a legal reform that exogenously required firms to publicly disclose information regarding clinical trials. Exploiting cross‐sectional heterogeneity in firms' exposure to the regulation based on their internal development portfolios, we find that affected firms are significantly more likely to transition to public equity markets following the reform. Moreover, firms that go public because of the increased disclosure requirements subsequently reduce the size of their project portfolios while shifting to safer investments acquired externally. We provide additional evidence for the main hypothesis using a second setting: a 2006 German reform which enhanced the enforcement of mandatory disclosure requirements for private firms. The results suggest that private firms' general information environment and disclosure requirements influence the propensity of going public.

ABSTRACT Could real‐time big data help unravel material firm events? How would it compare with firm disclosure and traditional media in terms of timeliness and completeness? Could big data provide incremental value‐relevant information for investors? With these questions in mind, we use a novel data set of cell phone “pings” (i.e., geolocation signals from mobile devices) to track production disruptions (outages)––material events for U.S. oil refineries. We first validate the construct by examining the effects of outages on local gas prices and firms’ accounting performance. Our main analyses show that (1) refining firms do not voluntarily disclose refinery outages identified by cell phone pings; (2) traditional media cover only a small portion of ping‐based outages; (3) the stock market finds ping‐based outages to be value relevant but incorporates the information with delay. Further analysis suggests that given the incomplete media coverage and lack of firm disclosure, investors appear to learn the financial impact of such outages through subsequent earnings announcements. Our evidence has implications for regulators such as the U.S. Energy Information Administration and the Securities and Exchange Commission as they continue to evaluate both the compliance and usefulness of disclosures for material firm events such as production disruptions.