Knowledge that Transforms

Abstract While the production of credit ratings has long been limited mainly to rating agencies (CRAs), recent years have seen the growing popularity of consensus credit ratings crowdsourced from banks (i.e., bank ratings). We provide the first comprehensive examination of the properties and informativeness of bank ratings relative to CRA ratings. We find that bank ratings often deviate from CRA ratings, with over 60% of firm-months having different bank and CRA ratings. These deviations contain useful information. Bank ratings improve out-of-sample prediction of defaults and CRA rating revisions and explain the cross-section of credit spreads. However, bank ratings do not improve out-of-sample prediction of credit excess returns, indicating that current prices incorporate bank rating information. Overall our findings suggest that bank ratings are a useful supplement to traditional credit ratings.

Abstract We develop a method for extracting “other information” from the articulation between bottom-line accounting numbers and stock prices. We posit that “other information” captures future earnings growth originating from conservative accounting recognition principles as demonstrated by Penman and Zhang (2020) and Penman and Zhu (2022), as well as nonzero net present value investment opportunities. Our findings confirm that “other information” is strongly associated with various proxies for expected future earnings growth and firm risk attributes. Furthermore, we show how a structural expected return model incorporating our “other information” estimate can predict out-of-sample future stock returns and generate sizeable long-short return spreads.

Abstract We find irregular patterns in the distribution of firms’ reported quarterly gross margin percentages. Specifically, there is significant bunching around percentage integers that are highly round (e.g., multiples of 10, such as 30%, 40%, etc.) or are neatly divisible (e.g., 25%, 75%), compared to what is predicted by counterfactual distributions. Further investigation reveals that highly round gross margin firms are smaller, exert higher effort, achieve higher productivity, have more difficult goals, and pay their CEOs with a higher portion of fixed income. We also find that highly round gross margins are associated with superior performance. Additionally, we do not find consistent evidence that highly round gross margin reference points are linked to external rewards. Collectively, our evidence is consistent with reference-dependent preferences for highly round gross margins likely being driven by intrinsic (rather than extrinsic) motivations.

Abstract We provide a lower-bound estimate of the undetected share of corporate fraud. To identify the hidden part of the “iceberg,” we exploit Arthur Andersen’s demise, which triggered added scrutiny on Arthur Andersen’s former clients and thereby increased the detection likelihood of preexisting frauds. Our evidence suggests that in normal times only one-third of corporate frauds are detected. We estimate that on average 10% of large publicly traded firms are committing securities fraud every year, with a 95% confidence interval of 7%-14%. Combining fraud pervasiveness with existing estimates of the costs of detected and undetected fraud, we estimate that corporate fraud destroys 1.6% of equity value each year, equal to $830 billion in 2021.

Abstract 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.

Abstract We examine bankruptcy within business groups. Groups have incentives to support financially distressed subsidiaries, as the bankruptcy of a subsidiary may impose severe costs on the group as a whole. This is in part because, in several countries, bankruptcy courts often “pierce the corporate veil” and hold groups liable for their distressed subsidiaries’ obligations as if they were their own. Using a large cross-country sample of group-affiliated firms, we show that, by reallocating resources within the corporate structure, business groups actively manage intra-group credit risk to prevent costly within-group insolvencies. Moreover, we document that recent regulatory changes in the approval and disclosure of related party transactions are costly for business groups in that they constrain their ability to shield their subsidiaries from credit-risk shocks. Our study informs the current regulatory debate on related party transactions by highlighting an important cost of anti-self-dealing regulation.