Securitization without Adverse Selection: The Case of CLOs

TLDR: In this paper, the authors investigate whether securitization was associated with risky lending in the corporate loan market by examining the performance of individual loans held by CLOs and find that adverse selection problems in corporate loan securitizations are less severe than commonly believed.

Abstract: 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 CLOs. We employ two different datasets 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 underperformance relative to the rest of the CLO portfolio. While there is some evidence of underperformance 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.

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Table 10 Does securitization by the originator predict worse future performance? (H2): Implied probability of default This table compares post-origination changes in implied probability of default (Π) for borrowers whose loans were securitized and borrowers whose loans were not securitized. Implied default probabilities are calculated using the reduced form Merton (1974)-style approach described in Bharath and Shumway (2008). The unit of observation for the analysis is a loan. The dependent variable is the change in the borrower‘s implied 1-year default probability over the window. The t-1 implied default probability (Πt-1) is measured as of month-end in the month before loan origination. The sample only includes securitized loans. The unit of observation for the analysis is CLO x loan. The focus is on the Same bank dummy which is equal to 1 when a loan appears in a CLO underwritten by its lead arranger and 0 otherwise. Same bank 2005-2007 is essentially an interaction term reflecting the marginal effect for loans originated between 2005 and 2007. The p-value corresponds to the statistical test of the null hypothesis that Same bank + Same bank 2005-2007=0. Specifications include fixed effects for the year of loan origination, the borrower‘s industry (2-digit SIC code) and rating at origination, and the identity of the loan lead arranger. We use the most granular rating information; e.g., we include separate dummies for BB+, BB, and BB-. Rating fixed effects are relative to the omitted category unrated. We omit year fixed effects because the number of same bank loans is small (150 observations) and the estimation requires enough loans for a within-portfolio comparison with fixed effects. The results are robust to alternatively excluding other types of fixed effects. Standard errors are clustered by loan. Variable definitions are the same as in Table 1. Significance at the 1%, 5%, and 10 % levels is indicated by ***, **, and *, respectively.

Fig. 2. Quarterly global CDO issuance, 2005-2008. This figure is compiled using issuance data published by the Securities Industry and Financial Markets Association (SIFMA). Structured finance corresponds to global CDO issuance backed by collateral that is itself structured (e.g., residential and commercial mortgages-backed securities, asset-backed securities, credit default swaps, or other CDOs). For example, mortgages are usually pooled into pass-through securities before they are purchased by CDOs.

Table 7 Loan selection under CLO demand pressure (quasi-experiment) This table seeks to address a potential selection bias. We replicated results in Tables 3-6 using three alternative proxies for CLO demand for loans. The basic idea is to look at the marginal quality of loans securitized in periods when the CLO demand for loans is generally high. In addition to controls used in the analysis in Tables 3 to 6 we include a fund-flow proxy and its interaction with the Securitized dummy. Coefficients on benchmark controls and fund-flow proxies are omitted for compactness of presentation. The table reports coefficients of interest for 44 different regressions (11 specifications x 4 measures of demand). CLO issuance (in billions) is equivalent to CLO fund flow. CLO pipeline (in billions) is the accumulation of CLOs in the pipeline, i.e., excess demand for loans by CLOs. CLO issuance scaled by total term loan issuance is also likely to reflect imbalances in demand. For robustness, we also report results for net cash flow into institutional accounts scaled by total term loan issuance, which reflects imbalances in institutional demand for loans more broadly. The analysis is done using the at-origination sample which includes loans originated between 1997 and May 2007. Fund flow data is available from 2001 through 2009. Significance at the 1%, 5%, and 10 % levels is indicated by ***, **, and *, respectively.

Table 2 Determinants of securitization: Loan and borrower characteristics at origination This table examines the impact of loan and borrower characteristics on the probability that a loan is sold to a CLO. The dependent variable is a dummy that equals 1 if a loan was sold to a CLO and 0 if not. The at-origination sample includes loans originated between 1997 and May 2007. The portfolio sample includes loans originated between January 2005 and July 2007. Borrower financials are taken from Compustat for the fiscal year ending prior to loan origination and industry adjustment is done at the 2-digit SIC level. The Compustat dummy equals 1 when a company has pre-origination data available in Compustat. Other variable definitions are the same as in Table 1. All specifications include fixed effects for the year of loan origination, the borrower‘s industry (2-digit SIC code) and rating at origination, and the identity of the loan lead arranger. We use the most granular rating information; e.g., we include separate dummies for BB+, BB, and BB-. Rating fixed effects are relative to the omitted category unrated. The coefficients shown are marginal effects. Standard errors are clustered by loan lead arranger in all specifications. Significance at the 1%, 5%, and 10 % levels is indicated by ***, **, and *, respectively.

Table 11 Robustness check: frequency of covenant violations This table compares the frequency of covenant violations across groups of loans that correspond to the two hypotheses tested in the paper. The unit of observation for the analysis is a loan. Number of financial covenants is the number of covenants on the loan at origination. Covenant violations were compiled from loan amendments remarks as reported in DealScan. The at-origination sample includes loans originated between 1997 and May 2007. The portfolio sample includes loans originated between January 2005 and July 2007. Significance at the 1%, 5%, and 10 % levels is indicated by ***, **, and *, respectively. Panel A: Does securitization predict worse future performance? (H1)

Table 4 – continued Panel B: Upgrades