We offer several suggestions for researchers using corporate bond return data. First, despite clear instructions from older papers (e.g., Bessembinder et al., The Review of Financial Studies 22:4219–4258, 2009) about ways to compute credit excess returns, a lot of recent research simply subtracts a Treasury Bill return. We show that this imprecision is likely to contaminate inferences, as the rate component of returns is negatively correlated to the spread component. This is a problem for all research looking at corporate bond returns, especially time series analysis and safer corporate bonds (e.g., investment grade). We provide a simple approach using Wharton Research Data Services (WRDS) data to remove the interest rate component of corporate bond returns. Second, we note significant differences in the coverage of corporate bonds across the Trade Reporting and Compliance Engine (TRACE) platform and typical corporate bond indices. We provide some simple rules for researchers who are using TRACE to select a subset of bonds closest to those contained inside corporate bond indices used by institutional investors. Third, we note differential quality in the prices and hence returns between TRACE and typical corporate bond indices. Corporate bond returns provided by corporate bond indices (i) correctly estimate credit excess returns, (ii) are synchronous for the entire set of bonds, allowing for consistent cross-sectional comparability, and (iii) suffer less from stale pricing issues. Due to these coverage and data quality issues, researchers should try, where possible, to source return data from multiple sources to ensure the robustness of their results.

We examine whether fundamental measures of volatility are incremental to market-based measures of volatility in (i) predicting bankruptcies (out of sample), (ii) explaining cross-sectional variation in credit spreads, and (iii) explaining future credit excess returns. Our fundamental measures of volatility include (i) historical volatility in profitability, margins, turnover, operating income growth, and sales growth; (ii) dispersion in analyst forecasts of future earnings; and (iii) quantile regression forecasts of the interquartile range of the distribution of profitability. We find robust evidence that these fundamental measures of volatility improve out-of-sample forecasts of bankruptcy and help explain cross-sectional variation in credit spreads. This suggests that an analysis of credit risk can be enhanced with a detailed analysis of fundamental information. As a test case of the benefit of volatility forecasting, we document an improved ability to forecast future credit excess returns, particularly when using fundamental measures of volatility.

We examine the relation between the fees paid to auditors for audit and non‐audit services, and the choice of accrual measures for a large sample of firms. Using our pooled sample, we find that the ratio of non‐audit fees to total fees has a positive relation with the absolute value of accruals similar to Frankel, Johnson, and Nelson [2002]. However, using latent class mixture models to identify clusters of firms with a homogenous regression structure reveals that this positive association only occurs for about 8.5% of the sample. In contrast to the fee ratio results, we find consistent evidence of a negative relation between the level of fees (both audit and non‐audit) paid to auditors and accruals (i.e., higher fees are associated with smaller accruals). The latent class analysis also indicates that this negative relation is strongest for client firms with weak governance. Overall, our results are most consistent with auditor behavior being constrained by the reputation effects associated with allowing clients to engage in unusual accrual choices.