ABSTRACT Despite the intuitive appeal, prior research finds mixed evidence on whether higher audit fees translate to superior audit quality. Under the assumption that product differentiation between auditors is based, in large part, on the level of financial statement assurance, we propose more refined measures of excess audit fees that separate auditor premiums from other fee premiums. Consistent with our conjecture, we identify significant variation in audit pricing across auditors (i.e., auditor premiums) that relates positively to audit quality. Conversely, we find no evidence that higher engagement‐specific fee premiums (i.e., fee model residuals) are positively related to proxies for audit quality. Additional tests indicate that our results do not simply reflect premiums attributable to auditor characteristics evaluated in prior research (e.g., Big 4 membership, office size, and industry expertise). In fact, our findings suggest that the positive association between auditor premiums and audit quality is better captured at the auditor level than it is at the auditor “tier,” office, auditor‐industry, or engagement levels. In sum, our results suggest that auditors charging higher fees, on average, deliver superior levels of financial statement assurance, but engagement‐specific fee premiums do not reflect quality‐enhancing audit effort. These contrasting results provide a possible explanation for the mixed findings in prior research.

ABSTRACT We employ a novel machine learning technique to classify analysts’ forecast revisions into five types based on how the revision weighs publicly available signals. We label these forecast types as quant, sundry, contrarian, herder, and independent forecasts. Our tests reveal that a greater diversity of forecast types within the consensus is associated with increased consensus dispersion and improved consensus accuracy. Additionally, consensus diversity is associated with an improved information environment for firms, as reflected in reduced earnings announcement information asymmetry and volatility, higher earnings response coefficients, and faster price formation. Our study sheds light on how analysts revise their forecasts and documents capital market benefits associated with different analyst forecasting approaches.

ABSTRACT Less-informed investors face greater costs of processing earnings news into actionable information. Our findings suggest financial analysis on social media reduces less-informed investors’ disclosure processing costs. We document an attenuated spike in earnings announcement (EA) information asymmetry for quarters containing more financial analysis on social media in the weeks prior to the EA. Cross-sectional evidence suggests this finding is stronger when coverage from traditional intermediaries is lower, for financial analyses written by more credible authors, and for financial analyses that are more likely relevant to evaluating the EA. Further evidence suggests retail trades, but not institutional trades, at EAs are significantly more profitable in quarters with greater financial analysis on social media, consistent with financial analysis on social media benefitting traders who are otherwise less-informed. Overall, our evidence suggests that financial analysis on social media plays an important role in aiding less-informed investors by helping them better process EA news. JEL Classifications: G14; M41.

ABSTRACT Consulting service revenues recently surpassed audit revenues as the primary income source for the largest accounting firms. Since SOX limits the provision of consulting services to audit clients, this shift in revenues implies that firms and many clients likely choose between audit and consulting relationships. We explore the implications of this by developing and validating a measure of client-level consulting needs that can likely be fulfilled by accounting firms, which we refer to as “consulting opportunities.” As predicted, we find that consulting opportunities relate positively to auditor switches. We also find that consulting opportunities relate negatively to subsequent Big 4 auditor selection—the firms focusing most on consulting—but we fail to find evidence that consulting opportunities relate to deteriorations in audit quality. Together, our results suggest that legislation limiting firms' ability to deliver consulting services to audit clients may have reduced audit market concentration without discernably impacting quality. Data Availability: All data used are publicly available from sources cited in the text.

ABSTRACT In the absence of random treatment assignment, the selection of appropriate control variables is essential to designing well-specified empirical tests of causal effects. However, the importance of control variables seems under-appreciated in accounting research relative to other methodological issues. Despite the frequent reliance on control variables, the accounting literature has limited guidance on how to select them. We evaluate the evolution in the use of control variables in accounting research and discuss some of the issues that researchers should consider when choosing control variables. Using simulations, we illustrate that more control is not always better and that some control variables can introduce bias into an otherwise well-specified model. We also demonstrate other issues with control variables, including the effects of measurement error and complications associated with fixed effects. Finally, we provide practical suggestions for future accounting research. Data Availability: All data used are publicly available from sources cited in the text. JEL Classifications: M40; M41; C18; C52.