Financial Statement Complexity and Bank Lending

Recent studies and anecdotal evidence suggest that investors struggle to process complex financial reports. Existing theory and evidence demonstrate that banks not only have unique advantages in acquiring information, relative to equity and public debt investors, but also can impose contractual terms to mitigate information frictions. We investigate whether financial statement complexity is associated with firms' reliance on bank financing and with the terms of bank loans (i.e., the amount and rate of the loan, along with covenants and collateral). We focus on two dimensions of complexity that capture the volume and presentation of financial information: 10-K length and readability. We find that complexity is positively associated both with firms' reliance on bank financing and with banks increasing their level of screening, rationing their credit supply, and imposing tighter covenants. Our results suggest that banks continue to play their role as informed capital providers in a changing economy, characterized by growing financial statement complexity and innovations in banks' business models

Big 4 Audit Fee Premiums for National and City-Specific Industry Leadership in the United Kingdom:Additional Evidence

This study investigates the relationship between Big 4 auditor industry specialisation and audit pricing in the U.K. in a period of many changes having taken place in the market for audit services. Using a large dataset between 2004 and 2011, our empirical results show a significantly higher fee premium for the Big 4 firms who are national industry leaders as compared to city-specific industry leaders, and that the fee premium for industry leadership is only earned by the city-specific industry leaders if and when they are also the national leaders. Neither the national nor city level industry leadership alone is priced anymore in the U.K. audit market. These findings hold for the pre- and the post-GFC period only and for a number of additional analyses. The evidence suggests that the Big 4 industry leadership in the U.K. has moved away from the previously documented premium for the Big 4 cityspecific industry leadership alone, and is now driven solely by the joint Big 4 industry expertise at the national and city-specific levels concurrently. The study’s results indicate that there is a progression from city-specific industry expertise to national-specific industry expertise, and they imply that there has been an improvement in the sharing and transferability of industry knowledge and expertise among the city offices of the Big 4 firms in the U.K. in the period of investigation

Using Peer Firms to Examine whether Auditor Industry Specialization Improves Audit Quality and to Enhance Expectation Models for Analytical Audit Procedures

This dissertation investigates how economically-comparable peer firms can be used to obtain inferences about a company’s accounting quality in two different research settings. The first Chapter examines whether auditor industry specialization, measured using auditor market share by industry, improves audit quality. After matching clients of specialist and non-specialist auditors according to industry, size and performance, there are no significant differences in audit quality between these two groups of auditors. In addition, this Chapter uses two analyses that do not rely primarily on matched samples. First, examining a sample of Arthur Andersen clients that switched auditors in 2002, there is no evidence of industry-specialization effects following the auditor change. Second, using a simulation approach, this study shows that client characteristics, and particularly client size, influence the observed association between auditor industry specialization and audit quality. Overall, these findings do not imply that industry knowledge is not important for auditors, but that the methodology used in extant studies examining this issue may not fully parse out the effects of auditor industry expertise from client characteristics. The second Chapter examines whether account-level expectation models for analytical audit procedures can be enhanced by using information from economically-comparable peer firms. This Chapter assesses the effectiveness of three main types of expectation models, with and without including information from peer firms: heuristic, time-series, and industry cross-sectional models. Information from peer firms improves the accuracy of all models and improves the detection power of time-series and industry cross-sectional models. Comparing between models, one-period heuristic models are generally unreliable, and industry cross-sectional models can be more effective than time-series models. These findings may help auditors of public companies and financial analysts in selecting expectation models and finding peer firms to assess the reasonability of a company’s financial information at the account-level.Ph

The art of conversation: the expanded audit report

The new generation of expanded audit reports includes disclosures about significant matters in a company's financial reporting and its audit. These disclosures are a landmark change in auditors' responsibility to provide information to the public. I examine expanded reports in various jurisdictions, why they became mandatory, what the evidence from their implementation is, and whether they have fulfilled the expectations of regulators and other stakeholders. Expanded reports are intended to increase the information content and usefulness of audit opinions, to increase external monitoring of auditors and management, and to foster a more open conversation between auditors and users of financial reporting. However, existing regulatory requirements, conflicting auditors' incentives to provide new information, and evidence from the expanded reports' implementation call into question whether these objectives have been met. It is my hope that expanded reports are only a first step towards enhanced auditor reporting

Regression and Machine Learning Methods to Predict Discrete Outcomes in Accounting Research

Predictive modeling focuses on iteratively trying various combinations and transformations of a set of variables to generate a decision rule that predicts outcomes for new observations. Although accounting researchers have demonstrated interest in predictive modeling, we identify a lack of applied guidance on this topic for accounting settings. This issue has become more salient with the increasing availability of machine learning models that use unfamiliar terminology, are estimated using algorithms, and produce different outputs than other models used for causal inference. To overcome this gap, we provide an overview of how to predict discrete outcomes with logistic regression and machine learning models use in recent studies. We also include guidance and a comprehensive example – predicting investigations by the U.S. Securities and Exchange Commission – that illustrates the elements of the prediction process, highlighting the importance of out-of-sample accuracy and unique aspects in the presentation of a prediction model’s results

Big Shoes to Fill: CEO Turnover and Pre-Appointment Firm Performance

Bayesian learning implies that corporate owners' performance expectations for their CEO are affected by their firm's performance prior to the CEO's appointment because firm asset quality is persistent. Accordingly, we find that the sensitivity of CEO turnover to performance increases in pre-appointment firm performance; that is, a CEO is more likely to be dismissed for underperformance when appointed at a better-performing firm. Consistent with Bayesian learning, we show that this effect increases with firm uncertainty and declines over CEO tenure. We find no evidence that the effect is due to owners' biased assessments of CEO ability or corporate governance quality. Collectively, our results suggest that CEOs, indeed, face a "big shoes to fill" effect that affects their performance-related turnover likelihood

Can Big 4 versus Non-Big 4 Differences in Audit-Quality Proxies Be Attributed to Client Characteristics?

This study examines whether differences in proxies for audit quality between Big 4 and non-Big 4 audit firms could be a reflection of their respective clients’ characteristics. In our analyses, we use three audit-quality proxies—discretionary accruals, the ex ante cost-of-equity capital, and analyst forecast accuracy—and employ propensity-score and attribute-based matching models in attempt to control for differences in client characteristics between the two auditor groups while estimating the audit-quality effects. Using these matching models, we find that the effects of Big 4 auditors are insignificantly different from those of non-Big 4 auditors with respect to the three audit-quality proxies. Our results suggest that differences in these proxies between Big 4 and non-Big 4 auditors largely reflect client characteristics and, more specifically, client size. We caution the reader that this study has not resolved the question, although we hope that it encourages other researchers to explore alternative methodologies that separate client characteristics from audit-quality effects

The Optional Qualitative Assessment in Impairment Tests

International and U.S. accounting standards mandate a one-step quantitative impairment test for goodwill and other indefinite-lived intangibles, but U.S. standards allow an optional qualitative assessment before the quantitative test. This option is intended to reduce the complexity and costs of the quantitative test. We demonstrate that U.S. firms using this option have comparatively higher valuations and face higher expected costs for conducting quantitative tests. Next, using a difference-in-differences research design, we show that firms using this option have a marginally higher incidence of impairments, suggesting that the qualitative assessment does not systematically allow companies to avoid write-downs. Moreover, we do not find clear evidence that using this option decreases the timeliness of impairments or increases monitoring costs for auditors, regulators, and investors. Our study provides evidence about the consequences of a revised impairment approach and speaks to the broader issue of allowing unconditional options and qualitative judgments in financial reporting