In this paper, we investigate whether securitization was associated with risky lending in the corporate loan market by examining the performance of individual loans held by collateralized loan obligations. We employ two different data sets that identify loan holdings for a large set of CLOs and find that adverse selection problems in corporate loan securitizations are less severe than commonly believed. Using a battery of performance tests, we find that loans securitized before 2005 performed no worse than comparable unsecuritized loans originated by the same bank. Even loans originated by the bank that acts as the CLO underwriter do not show under-performance relative to the rest of the CLO portfolio. While some evidence exists of under-performance for securitized loans originated between 2005 and 2007, it is not consistent across samples, performance measures, and horizons. Overall, we argue that the securitization of corporate loans is fundamentally different from securitization of other assets classes because securitized loans are fractions of syndicated loans. Therefore, mechanisms used to align incentives in a lending syndicate are likely to reduce adverse selection in the choice of CLO collateral.

Large blocks of stock play an important role in many studies of corporate governance and finance. Despite this important role, there is no standardized data set for these blocks, and the best available data source, Compact Disclosure, has many mistakes and biases. In this paper, we document these mistakes and show how to fix them. The mistakes and biases tend to increase with the level of reported blockholdings: in firms where Compact Disclosure reports that aggregate blockholdings are greater than 50%, these aggregate holdings are incorrect more than half the time and average holdings for these incorrect firms are overstated by almost 30 percentage points. For researchers using uncorrected blockholder data as a dependent variable, these errors will increase the standard error of coefficient estimates but do not appear to cause bias. However, we find that if blockholders are used as an independent variable, economically significant errors-in-variables biases can occur. We demonstrate these biases using a representative analysis of the relationship between firm value and outside blockholders. An online appendix to our paper provides a “clean” data set for our sample firms and time period. For researchers who need to work outside of this sample, we also test the efficacy of alternative (cheaper) fixes to this data problem, and find that truncating or winsorizing the sample can reduce about half of the bias in our representative application.