Prior research emphasizes the centrality of audit offices in understanding auditing practices, and documents significant interoffice variation in audit outcomes based on industry expertise and office size. Our study examines how two city‐specific labor characteristics also affect audit offices and local audit markets: the city's average educational attainment, and the number of accountants in a city, which proxy for a city's human capital. Our argument draws on the urban economics literature and predicts that the level of human capital in a city is positively associated with an audit office's ability to conduct high‐quality audits. As expected, there is a positive association between audit quality (quality of audited earnings and accuracy of going‐concern reports) and average education level in the city in which the lead engagement office is located. This association is generally significant for both Big 4 and non‐Big 4 offices, but is relatively stronger for non‐Big 4 firms that are more tied to local labor markets. A company is also more likely to choose a non‐Big 4 auditor in cities with higher educational levels and relatively more accountants, and there is evidence of higher non‐Big 4 audit fees as a city's education level increases. Collectively, these results suggest that local labor characteristics affect audit offices, audit quality, and the ability of non‐Big 4 auditors to compete with Big 4 auditors in the audits of public companies.

We find that non‐Big 4 audit offices with greater awareness of SEC enforcement are more likely to issue first‐time going‐concern reports to distressed clients; where SEC “awareness” is measured using (i) audit office proximity to SEC regional offices, and (ii) proximity to specific SEC enforcement actions against auditors. We also show that these non‐Big 4 audit offices issue more going‐concern opinions to clients who do not subsequently fail, indicating a conservative bias that reduces the informativeness of audit reports. This conservative reporting bias is also associated with higher audit fees and higher auditor switching rates. These findings are important because non‐Big 4 firms now audit 39 percent of SEC registrants and issue 88 percent of going‐concern audit reports. For Big 4 offices, we find some evidence that awareness of SEC enforcement may improve reporting accuracy by reducing Type II errors (failing to issue a going‐concern report to a company that fails), although the number of cases is small.

We develop a three-factor no-arbitrage model for valuing mortgage-backed securities in which we solve for the implied prepayment function from the cross-section of market prices. This model closely fits the cross-section of mortgage-backed security prices without needing to specify an econometric prepayment model. We find that implied prepayments are generally higher than actual prepayments, providing direct evidence of significant macroeconomic-driven prepayment risk premiums in mortgage-backed security prices. We also find evidence that mortgage-backed security prices were significantly affected by Fannie Mae credit risk and the Federal Reserve’s quantitative easing programs. Received May 10, 2016; editorial decision September 22, 2017 by Editor Stijn Van Nieuwerburgh.

We reconsider whether hedge funds’ time-varying risk factor exposures are predictive of superior performance. We construct an overall measure (BA) of fund managers and present evidence that top beta active managers deliver superior long-term out-of-sample performance compared to top alpha active managers. BA captures the time-varying nature of beta exposures and can be interpreted as a common factor of both systematic risk (SR) and (1 - R 2 ) measures. BA also compares favorably to extant measures of market timing, capturing the explanatory power of such measures of hedge fund performance.

In response to the increasing use of computer programs to process firm disclosures, this registered report develops a new measure of “scriptability” that reflects computerized, rather than human, information processing costs. We validate our measure using SEC filing‐derived data from prior research and identify firm and disclosure characteristics related to it. In our planned hypothesis tests, we find some evidence that the speed of the market response to filings increases with scriptability, but find little evidence that scriptability affects the incidence and speed of news dissemination by Dow Jones. In additional analyses, we find that scriptability exhibits both positive and negative associations with changes in information asymmetry between market participants, depending on the filing, trading window, and measure examined. We also find little evidence that XBRL interacts with scriptability in a meaningful way. Overall, our study broadens our understanding of information processing costs and provides opportunities for new avenues of research.

Past fund performance does a poor job of predicting future outcomes. The reason is noise. Using a random effects framework, we reduce the noise by pooling information from the cross-sectional alpha distribution to make density forecasts for each individual fund’s alpha. In simulations, we show that our method generates parameter estimates that outperform alternative methods, both at the population and at the individual fund level. An out-of-sample forecasting exercise also shows that our method generates improved alpha forecasts. Received November 23, 2016; editorial decision November 1, 2017 by Editor Andrew Karolyi.

We consider the estimation methods for the rank of a beta matrix corresponding to a multifactor model and study which method would be appropriate for data with a large number of assets. Our simulation results indicate that a restricted version of Cragg and Donald’s (1997) Bayesian information criterion estimator is quite reliable for such data. We use this estimator to analyze some selected asset pricing models with U.S. stock returns. Our results indicate that the beta matrix from many models fails to have full column rank, suggesting that risk premiums in these models are underidentified.

Gupta and Gerchak (2002) argue that different acquirers can arrive at different equity valuations for the same target depending on their strategic intent. A reason for acquirers' equity valuations to vary, holding target fundamentals constant, may be that individual acquirers place different weights on underlying fundamentals. I examine this possibility using Burgstahler and Dichev's (1997) theoretical framework. They argue that the relative importance of earnings and book value depends on expected adaptation, which is the likelihood that the existing earnings generating process will be altered. Using restructuring costs to proxy for expected adaptation at the individual acquirer level, I find that the association between the target's earnings (book value) and acquirers' bid prices is decreasing (increasing) in expected adaptation, consistent with theoretical predictions. These findings are less pronounced during merger waves and intense bid competition for the target.

In collaboration with the Authority for the Financial Markets in the Netherlands, we manipulate the content of official letters that instruct financial intermediaries to submit a mandatory self‐assessment. As part of the Registered Report Process, we submitted our hypotheses, experimental procedure, and planned statistical analyses before data collection. We predicted that a request indicating a supportive regulatory attitude has a positive effect on reporting quality on average. We also predicted this effect to be stronger for small firms and for firms with a long‐term orientation, and to become negative for firms with a short‐term orientation. Planned analyses show that a supportive letter reduced reporting quality unless firms had a long‐term orientation, supporting the moderating influence of time horizon, but providing no support for the expected average effect or for moderation by firm size.

One key risk to the banking system is how funding costs will change as monetary policy is normalized and interest rates rise after almost a decade of near-zero rates. Our contribution is to develop a model that jointly estimates banks’ balance sheets and retail interest rates to arrive at a consistent estimate of the change in bank funding costs as market rates change. Our estimates imply a 100 basis-point shock to the Federal Funds rate would increase overall deposit funding costs by about $40 billion, which is roughly equal to 25% of aggregate annual net income for commercial banks and savings institutions. We also find that deposit rate responses are largely symmetric, in contrast to some previous research showing deposit rates are less responsive to upward movements in reference rates. We introduce unique and confidential data on bank deposit betas to anchor our results.