Knowledge that Transforms

contemporary advances from 2010 to date.

ABSTRACT In an effort to motivate firms to more rapidly detect potential misconduct, legislators, regulators, and enforcement agencies incentivize firms to have integrity or “whistleblowing” hotlines. These hotlines provide individuals an opportunity to report alleged misconduct and seek guidance about how to appropriately respond. Beyond some isolated examples, little is known about the responsiveness of hotlines to actual claims of alleged misconduct. I undertake a field study to investigate how hotlines function in practice by making four different inquiries involving alleged misconduct to nearly 250 firms. I find that one‐fifth of firms have impediments (e.g., phone line disconnected, email bounce back, direct to incorrect website) that hinder reporting and approximately 10% of firms do not respond in a timely manner. Overall, this investigation illuminates several differences between integrity hotlines “on paper” and how they actually perform in practice.

ABSTRACT We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic ) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10‐K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of Securities and Exchange Commission (SEC) enforcement actions (Accounting and Auditing Enforcement Releases [AAERs]) and irregularities identified from financial restatements and 10‐K filing amendments. Our out‐of‐sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting.

ABSTRACT We develop a state‐of‐the‐art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory‐motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: the Dechow et al. logistic regression model based on financial ratios, and the Cecchini et al. support‐vector‐machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.

ABSTRACT I examine how mandatory extraction payment disclosures (EPD)—a policy solution intended to discourage corporate payment avoidance in the oil, gas, and mining industries—affect fiscal revenue contributions and investments by multinational firms in foreign host countries. Using the staggered adoption of EPD across firms headquartered in Europe and Canada, I find that disclosing companies increase their payments to host governments, decrease investments, and obtain fewer extraction licenses relative to non‐disclosing competitors. These effects are stronger for firms that face a high risk of public shaming, operate in corrupt host countries, and have a high exposure to bribery‐prone payments, suggesting that EPD increases the reputational cost of corporate behavior that could be perceived as exploitative. The resulting reallocation of investments from disclosing to non‐disclosing firms reduces drilling productivity and resource production in host countries, consistent with uneven disclosure regulation distorting capital allocation.

ABSTRACT I examine how characteristics of investors’ information access tools change investors’ reactions to firm disclosures. I examine my research question in the context of information choice (i.e., allowing investors to choose the order of information and sections to read within a disclosure) and spatial layout (i.e., how information is displayed when viewing the disclosure). Results of an experiment are consistent with information choice improving investors’ judgments if the disclosure is viewed on a computer screen. Conversely, and consistent with cognitive overload, information choice harms investors’ judgments if the disclosure is viewed on a smaller screen, such as that of a mobile device. Follow‐up experiments show that changing the disclosure presentation to reduce the need to scroll is one way to improve investors’ judgments on a smaller (or mobile) screen. My findings caution firms and regulators about expanding information choice within disclosures without considering the screen size used to access the disclosure.

ABSTRACT Although subsidiary disclosures in firms’ filings with the Securities and Exchanges Commission (SEC; Exhibit 21) represent the most granular required public disclosure of a firm's geographic footprint, little is understood about the quality of the disclosure, and anecdotal evidence suggests firms may not fully comply with the disclosure requirements. We use data provided by multinational firms to the Internal Revenue Service regarding their foreign subsidiary locations to explore the accuracy of public subsidiary disclosures on Exhibit 21 of Form 10‐K per SEC rules. The overall incidence of nondisclosure is low, suggesting that most firms comply with Exhibit 21 disclosure rules, and that for most applications, Exhibit 21 disclosures provide a reasonable proxy for locations of significant subsidiaries. Nevertheless, there is some evidence of nondisclosure, particularly when subsidiaries are in tax havens, when the firm is more highly scrutinized in the media, or when the firm has other characteristics consistent with low‐quality disclosures such as SEC comment letters.

ABSTRACT We have little knowledge about the prevalence of irreproducibility in the accounting literature. To narrow this gap, we conducted a survey among the participants of the 2019 JAR Conference on their perceptions of the frequency, causes, and consequences of irreproducible research published in accounting journals. A majority of respondents believe that irreproducibility is common in the literature, constitutes a major problem, and receives too little attention. Most have encountered irreproducibility in the work of others (although not in their own work) but chose not to pursue their failed reproduction attempts to publication. Respondents believe irreproducibility results chiefly from career or publication incentives as well as from selective reporting of results. They also believe that practices like sharing code and data combined with stronger incentives to replicate the work of others would enhance reproducibility. The views of accounting researchers are remarkably similar to those expressed in a survey by the scientific journal Nature . We conclude by discussing the implications of our findings and provide several potential paths forward for the accounting research community.

ABSTRACT Theory posits that investors can rationally infer the implications of strategic nondisclosure for firm value, pressuring managers to disclose information voluntarily. This study documents that the lack of an earnings guidance predicts an abnormal return of −41 basis points around the subsequent quarterly earnings announcement, suggesting that investors do not fully incorporate the implications of nonguidance. Further analyses demonstrate that limitations in price efficiency, driven by investors' limited attention and short‐selling constraints, explain the mispricing of nonguidance and are associated with less guidance issuance. Our results collectively highlight limited price efficiency as another friction when studying managers' strategic disclosure decisions.

ABSTRACT We explore whether corporate tax enforcement can affect bank lending. Specifically, we hypothesize that tax enforcement efforts aimed at small and midsized enterprises (SME) can improve their information environments, which in turn could lead to increased bank commercial lending. Exploiting the regional structure employed by the IRS until 1999, we find that the corporate tax return audit probability for SMEs is associated with greater commercial lending growth for regionally focused banks. We find similar evidence when exploiting the IRS reorganization from a regional to federal system in 2000. Further results show that tax enforcement's impact on SME informational environments is at least partially responsible for this association: The impact of tax auditing on bank lending is stronger for banks facing greater informational disadvantages and in areas where SMEs face greater hold‐up problems. Finally, we find that the tax audit rate is positively associated with loan portfolio quality, suggesting that tax enforcement can lead to better loan decisions. Our findings are consistent with the tax authority's mandate having important externalities on bank lending and SME access to capital, suggesting that the benefits to tax enforcement go beyond improving tax collection.