I use a difference-in-differences (DiD) research design, comparing securitizing BHCs with a matched sample of non-securitizing BHCs, pre- and post- the adoption of each of the five securitization accounting pronouncements discussed in section 2.2.3. A key feature of this design is that it exploits five accounting pronouncements that form four distinct transparency treatments, of which one decreases transparency and the others increase transparency.1 This feature increases confidence in transparency as the causal influence.
I use three alternative test windows for each pronouncement. The first test window spans two years where the post-period is the first calendar year for which the pronounce- ment is effective, and the pre-period is the calendar year prior to post-period. For example,
for FAS 125 the pre-period is the calendar year 1996 and the post-period is the calendar year 1997. FAS 125 was issued in June 1996 and was effective for securitizations occurring after December 31, 1996. This narrow test window mitigates the possibility that changes in lending behavior are driven by factors other than the effective change in transparency. In the second test window, I use the calendar year prior to the adoption year as the pre- period because BHCs may change their lending behavior in response to the adoption of a pronouncement when it is issued and before the effective date. Continuing the above example for FAS 125, the post-period remains the calendar year 1997 while the pre-period is now the calendar year 1995. The third test window spans four years with the first two calendar years for which the pronouncement is effective forming the post-period (e.g., 1997 & 1998 for FAS 125) and the two calendar years immediately preceding the post period forming the pre-period (e.g., 1995 & 1996 for FAS 125).
Section 2.2.3 presents the case for why FAS 125 decreases transparency, and why the other pronouncements increase transparency. This section provides several reasons to sup- port my claim that the adoptions of the first three of the pronouncements are plausibly exogenous to my research question. First, suboptimal risk-taking in primary mortgage markets was not a major concern in the pre-crisis period. In the pre-crisis period, many commentators (e.g., Laderman 2001, Gramlich 2004) focused on the benefits of securiti- zation in promoting competition in the sub-prime markets and in increasing the flow of funds to borrowers in this market. Second, FASB provides no indication in its discussions that it issued these pronouncements because previous standards failed to reveal bank risk- taking, which it does for FAS 166. This is in line with FASB’s intent to write standards that neutrally reflect underlying economics, without attempting to directly influence the economic activity. Last, even FIN 46 (R), which was issued in response to Enron’s high- profile accounting scandal, did not directly target bank lending decisions. However, there is likely endogeneity around the adoption of FAS 166 & 167. The role of sub-prime mortgage securitizations in the financial crisis plausibly motivated these pronouncements.
While the first three pronouncements were not issued as a direct response to bank risk-taking, it is possible that other economic events that preceded or occurred contem- poraneously with these pronouncements can affect bank risk-taking. For example, FAS 125 was issued during the early stages of the securitization market, when it was growing rapidly. FAS 140 was preceded by the 1998 Russian debt and hedge fund crisis. Many subprime mortgage lenders either failed or were acquired by larger banks, and subprime mortgage securitization volume declined following the 1998 Russian debt/hedge fund crisis (Chomsisengphet & Pennington-Cross 2006). As discussed in the previous paragraph, FIN 46(R) was issued as a response to the Enron scandal. It is possible that these events affect bank risk-taking, and do so more than transparency affects bank risk-taking, making the
effect of transparency a second order effect and difficult to disentangle from the effect of these economic events. However, to the extent that these events affect both securitizing and non-securitizing banks, myDiDdesign with non-securitizing banks as a control group should isolate securitization-related transparency as the treatment.
I use non-securitizing BHCs, which are not affected by securitization accounting pro- nouncements, as a control group to account for changes in bank risk-taking driven by other factors, such as changes in economic and market conditions, government policy, industry practice, and regulatory policy in the same period. To alleviate selection problems that arise if the factors that cause BHCs to securitize also drive their differential risk-taking rel- ative to non-securitizing BHCs, I form a propensity score-matched control group.2 I form
a distinct test sample of matched treatment-control pairs for each transparency event. I employ the following procedure to create a matched pair of treatment and control BHCs. I first estimate a logistic propensity score model of the probability of securitization, conditional on characteristics observable in the year immediately prior to the treatment, separately for private and publicly traded BHCs.3 I then match each public (private)
securitizing BHC to the public (private) non-securitizing BHC with the closest propensity score. I perform the matching with replacement and imposing a 10% caliper. While it is common in the accounting literature (e.g., Casu, Clare, Sarkisyan & Thomas 2013,
Lawrence, Minutti-Meza & Zhang 2011,Oz 2016) to use a caliper width<5% to reduce the likelihood of matched pairs with drastically different propensity scores, I choose a relatively wider caliper to reduce the number of observations lost in the matching process.4 King & Nielsen(2016) show that pruning more observations can result in greater biases and model dependence.
The primary dependent variable is bank risk-taking in mortgage lending as reflected by the risk profiles of mortgages originated or purchased by each BHC. All else equal, more aggressive risk-taking corresponds to looser lending standards and origination or purchase of riskier mortgages. Chapter4 discusses validation of the specific measures of risk-taking used in this thesis.
2To reduce the likelihood that my results are driven by this particular matching method, I test the robustness of my findings to using other matching techniques in section5.5.
3AppendixC discusses the propensity score matching in greater detail, including a description of the ex ante observable covariates, which I selected based on prior literature (e.g.,Minton, Sanders, Strahan et al. 2004, Uzun & Webb 2007, Affinito & Tagliaferri 2010, Cardone-Riportella, Samaniego-Medina & Trujillo-Ponce 2010) that examines determinants of securitization, andOz (2016) who performs a similar propensity score matching of securitizing and non-securitizing BHCs around FAS 166 & 167.
3.2.1
Securitization Indicator
I define a given BHC-year as securitizing if the BHC has a stock of outstanding securitized mortgages in the three-year period ending in the current year. I use a three-year period to identify securitizing banks because some BHCs securitize sporadically. I define mortgages as securitized if they are: (i) “sold with recourse” for the 1993-2000 period, and (ii) “sold and securitized” or “sold with recourse but not securitized” for the 2001-2015 period.5
While mortgages “sold with recourse but not securitized” are not directly securitized by the reporting BHC, they likely represent participations in securitizations by other entities such as Fannie Mae, Freddie Mac, and unaffiliated private securitization conduits. More- over, these types of financial asset transfers are within the scopes of all the accounting pronouncements explored in this study.