Embed Size (px)
Citation preview
Asset-level Transparency and the (E)valuation of Asset-Backed Securities
Jed J. Neilson
Pennsylvania State University
Stephen G. Ryan
New York University
K. Philip Wang
University of Florida
Biqin Xie
Pennsylvania State University
September 10, 2018
Abstract
To address the concern that the opacity of structured finance products contributed to the financial
crisis, the Dodd-Frank Act mandates disclosure of information about the individual assets
underlying asset-backed securities (“ABS”). The SEC fulfilled this mandate for certain types of
ABS deals in Regulation AB II, whose asset-level disclosure requirements are effective as of
November 2016. We examine the impact of these asset-level disclosure requirements on the
(e)valuation of ABS by investors and credit rating agencies. We use a difference-in-differences
research design that compares affected and unaffected types of ABS around the imposition of the
requirements. We find that asset-level disclosures lead to initial ABS yield spreads and credit
ratings that better predict the subsequent performance of the underlying assets, as well as to
increased investor reliance on credit ratings. The increased informativeness of yield spreads is
consistent with asset-level transparency improving investors’ valuation of ABS. Because credit
rating agencies did not change their methodologies or reliance on asset-level information around
Regulation AB II, our findings for credit ratings suggest that public asset-level disclosures either
discipline the agencies’ evaluation of ABS or increase their use of ABS price-related
information. Our primary results obtain only in deals with above-median risk layering in the
underlying assets, for which pool-level summary statistics are less informative, and with above-
median complexity in the tranching of credit risk.
1
Asset-level Transparency and the (E)valuation of Asset-Backed Securities
“Sunlight is said to be the best of disinfectants.”
Louis Brandeis in Other People’s Money and How the Bankers Use It (1914)
1. Introduction
We examine the impact of asset-level transparency on the (e)valuation of asset-backed
securities (“ABS”) by investors and credit rating agencies. We focus on the ability of these two
types of market participants to predict, at the time of ABS issuance, the subsequent performance
of the underlying assets. This research question is important because the opacity of the assets
underlying ABS is widely cited as a primary cause of the 2007–2009 financial crisis (e.g.,
Acharya et al., 2009; Scott and Taylor, 2009; Gorton, 2010). The Financial Crisis Inquiry Report
(2011, p. xix) concludes that “a combination of excessive borrowing, risky investments, and lack
of transparency put the financial system on a collision course with crisis.” Ashcraft and
Schuermann (2008) explain how information about the underlying assets is lost in each step of
the securitization process: at asset origination, when originators sell assets to the issuers, when
issuers package the assets into complexly structured deals and sell the ABS to investors, and
possibly when ABS are resecuritized. Owing to their position at or near the end of this chain,
ABS investors have poor understandings of underlying asset quality (Coval et al., 2009).
As part of the post-financial crisis effort to reform the securitization process, the Dodd-
Frank Wall Street Reform and Consumer Protection Act (“Dodd-Frank Act”) directed the SEC to
“require issuers of asset-backed securities, at a minimum, to disclose asset-level or loan-level
data, if such data are necessary for investors to independently perform due diligence” (Section
942[b]). In response, the SEC developed asset-level disclosure requirements in Regulation AB II
(“Reg AB II”) that became effective on November 23, 2016 (SEC, 2014).
2
Reg AB II’s asset-level disclosure requirements represent an ideal setting to address our
research question for two primary reasons. First, these requirements constitute the first and still
most significant post-crisis expansion of public information about the assets underlying ABS.
For example, prior to these requirements issuers provided investors in ABS collateralized by auto
loans and leases (“auto ABS”), which typically are collateralized by pools of 20,000 to 100,000
loans or leases, with only pool-level summary statistics of individual underwriting criteria such
as borrower FICO scores and loan-to-value ratios. While useful, these pool-level disclosures
suppress important features of the underlying assets such as risk layering (e.g., some assets in the
pool have both low FICO scores and high loan-to-value ratios, while others have both high FICO
scores and low loan-to-value ratios) (Ryan, 2018). Under Reg AB II’s asset-level disclosure
requirements, issuers provide investors with underwriting criteria such as the FICO score and
loan-to-value ratio for each asset in the pool, revealing these features.1 Second, Reg AB II’s
asset-level disclosure requirements only substantially increase disclosures for certain types of
ABS,2 but the regulation’s other requirements (e.g., eliminating the use of credit ratings in shelf
eligibility criteria) apply to all ABS types. The limited effect of the asset-level disclosure
requirements enables us to employ a difference-in-differences research design with a treatment
sample that is affected by both the disclosure requirements and the other requirements, and a
control sample that is affected only by the other requirements.
We examine both investors and credit rating agencies because prior research suggests that
the two types of market participant should be differentially affected by Reg AB II’s asset-level
1 Appendix A provides examples of auto ABS issuers’ disclosures before and after the effective date of Reg AB II’s
asset-level disclosure requirements. 2 As discussed shortly and in more detail in Section 2, this limited effect of Reg AB II’s asset-level disclosure
requirements to date is attributable in part to the requirements applying only to certain types of ABS deals, in part to
the existence of active markets in only some of these types of deals, and in part to issuers providing asset-level
disclosures in practice for other types of ABS deals prior to these requirements.
3
disclosure requirements. Prior research finds that investors did not fully appreciate the risk of
ABS prior to the financial crisis and as a consequence bore substantial losses during the crisis
(e.g., Coval et al., 2009). Reg AB II’s asset-level disclosure requirements dramatically increase
the information available to investors in the subject types of ABS, and so in principle should
improve investors’ valuations of these ABS. This improvement may not materialize, however,
for various reasons. Based on investors’ information-processing constraints, the complexity of
securitizations, and the highly disaggregated nature of asset-level disclosures, Richardson et al.
(2011, p. 482) conclude that “it is not clear how investors or regulators can use this voluminous
information.” Similarly, in its comment letter on the SEC’s 2010 proposed rule that led to Reg
AB II, which is representative of the letters from other ABS issuers, Bank of America (2010, p.
11) states that “while some investors may suspect that the information would be helpful, the lack
of any historic reliance on some of this data suggests that it may be per se immaterial.” In
addition, Goldstein and Yang (2017, p. 101) illustrate that “disclosure can crowd out the
production of private information,” and Banerjee et al. (2018) show theoretically that greater
disclosure can reduce price efficiency by shifting investors’ attention from fundamental analysis
to the determination of other investors’ beliefs.
In contrast, although over-optimistic credit ratings of ABS have been identified as a key
cause of the financial crisis (Jones et al., 2008; Duyn and Chung, 2008; Ashcraft et al., 2010;
Jiang et al., 2018), and various provisions of the Dodd-Frank Act are intended to require or
induce credit rating agencies to refine their rating methodologies and resolve incentive problems
(e.g., Altman et al., 2010), Reg AB II’s asset-level disclosure requirements do not appear to have
had much effect on the information the agencies use to determine ratings. None of the big three
rating agencies substantially changed their rating methodologies around the effective date of the
4
asset-level disclosure requirements.3 Moreover, prior to Reg AB II the rating agencies had
access to nonpublic data more granular than the information available to investors.4 In its
comment letters on the proposals leading up to Reg AB II, Moody’s states that credit rating
agencies “typically ask for additional information to analyze and rate securities” (Moody’s,
2010, p. 2) and “likely would gain access to the [asset-level] information” (Moody’s, 2014, p.
1).5 The availability of asset-level disclosures may affect credit ratings through other channels,
however. To the extent that the disclosures help investors identify inaccurate ratings, the
resulting market discipline provides the agencies with reputational incentives to improve ratings
quality (White, 2010). For example, the SEC states that when asset-level information becomes
available, “investors will have the ability to better assess the rating performance” (SEC, 2014, p.
57203). American Securitization Forum also expresses the similar view in its comment letter on
Reg AB II that “investors believe that the transparency afforded by loan-level data will allow all
investors to evaluate, in any market and on an independent basis, whether … the ratings assigned
are appropriate” (American Securitization Forum, 2010, p. 33-34). To the (apparently limited)
extent that the agencies incorporate ABS price-related information (e.g., yield spreads for similar
ABS) into their ratings, the disclosures enable the agencies to incorporate more accurate price-
related information into their ratings.
3 Of the big three credit rating agencies, only Fitch issued updates to its auto ABS rating methodologies after the
effective date of the asset-level disclosure requirements, and even it claimed that “this updated criteria report is
substantially unchanged from the prior criteria” (Fitch, 2017). 4 In 2010, the SEC amended Regulation Fair Disclosure (“Reg FD”) to remove credit rating agencies’ exemption
from Reg FD’s prohibition on issuers selectively disclosing non-public information (SEC, 2010). However, credit
rating agencies and legal commentators state that this amendment did not inhibit credit rating agencies’ long-
standing practice of obtaining issuers’ non-public information, for two reasons. First, credit rating agencies are not
among the classes of covered persons in Reg FD. Second, Reg FD allows issuers to “share material nonpublic
information with outsiders, for legitimate business purposes, when the outsiders are subject to duties of
confidentiality” (SEC, 2000). Credit rating agencies commit to confidentiality in their engagement letters with
issuers. See Ohlson et al. (2010) and International Law Office (2010) for further discussion of this issue. 5 Our discussions with credit analysts at one of the big three credit rating agencies (i.e., S&P, Moody’s, and Fitch)
supports these two statements.
5
We first examine whether Reg AB II’s asset-level disclosure requirements improve
investors’ and credit rating agencies’ ability to predict, at the time of ABS issuance, the
subsequent performance of the underlying assets. We measure performance as the percentage of
the underlying assets that are at least 30, 60, and 90 days delinquent as of 120 days after the
issuance date. The treatment group consists of all SEC-registered (i.e., publicly offered) auto
ABS deals, because Reg AB II mandates a substantial increase in the asset-level disclosures for
these deals. The control group includes all other SEC-registered non-agency ABS deals, which
either are not subject to the asset-level disclosure requirements (e.g., ABS backed by credit card
receivables) or in practice provided asset-level disclosures prior to the requirements (i.e.,
commercial mortgage-backed securities [“CMBS”]).
We proxy for investors’ valuation of ABS using tranche yield spreads at issuance. We
find that these initial spreads predict subsequent delinquencies on the underlying assets
significantly better for the treatment group than for the control group after the effective date of
Reg AB II’s asset-level disclosure requirements than before that date, consistent with these
disclosures improving investors’ valuations of ABS. To give a sense for economic significance,
when initial yield spreads increase by one standard deviation (0.74 percent), subsequent 30-day
delinquencies as percentage of the underlying assets increase by 0.40 percent (48 percent of the
unconditional mean of 30-day delinquencies) for the treatment sample relative to the control
sample around the effective date of Reg AB II’s requirements.
We proxy for credit rating agencies’ evaluation of ABS using tranche credit ratings. We
find that these initial ratings predict subsequent delinquencies on the underlying assets
significantly better for the treatment group than for the control group after the effective date of
Reg AB II’s asset-level disclosure requirements than before that date. This finding is consistent
6
with public asset-level disclosures either increasing market discipline over credit ratings or
leading credit rating agencies to increase their incorporation of ABS price-related information.
To give a sense for economic significance, when initial credit ratings deteriorate by one standard
deviation (2.49 notches), subsequent 30-day delinquencies as percentage of the underlying assets
increase by 20 percent relative to its unconditional mean for the treatment sample relative to the
control sample around the effective date of Reg AB II’s requirements.
The results described above raise the question of whether the improved predictive ability
of yield spreads is attributable to higher quality credit ratings or to more effective due diligence
by investors independent of credit ratings. To address this question, we need a proxy for
investors’ independent due diligence. Based on the facts that initial credit ratings are included in
securitization prospectuses and investors typically use these ratings as key inputs in their
decisions to purchase ABS tranches, we argue that the portion of initial yield spreads that is not
explained by initial credit ratings captures investors’ independent due diligence. Employing a
decomposition approach similar to those used in prior research examining other contexts (e.g.,
Bhattacharya et al., 2012; Healy and Serafeim, 2016; Lee et al., 2018), we proxy for investors’
independent due diligence as the residual from the regression of initial yield spreads on initial
credit ratings. We find that this measure predicts subsequent asset performance significantly
better for the treatment sample than for the control sample under Reg AB II’s requirements
compared to before the requirements, consistent with asset-level disclosures improving
investors’ independent due diligence.
Given this more effective due diligence, we examine whether investors reduce their
reliance on credit ratings under Reg AB II’s asset-level disclosure requirements, a stated primary
objective of the SEC for the requirements (SEC, 2014, p. 57187). Prior research finds that
7
investors’ over-reliance on credit ratings contributed to ABS rating inflation before the crisis
(Bolton et al., 2012). The Financial Crisis Inquiry Commission (2011, p. 119) asserts that this
over-reliance occurred because investors “had neither access to the same data as the rating
agencies nor the capacity or analytical ability to assess the securities they were purchasing.”
We proxy for investors’ reliance on credit ratings as the association between initial
tranche yield spreads and initial tranche credit ratings. A weaker association under Reg AB II’s
asset-level disclosure requirements than under the prior requirements would suggest that asset-
level disclosures enable investors to reduce their reliance on ratings. We find, however, that this
association strengthens for the treatment sample relative to the control sample around the
effective date of Reg AB II’s asset-level disclosure requirements. Although contrary to the
SEC’s objective mentioned above, this result is consistent with our finding that credit rating
quality improves after this date. This result is also consistent with the finding in Banerjee et al.
(2018) that public disclosures encourage investors to acquire information from other participants.
We examine two mechanisms by which asset-level disclosures lead to more accurate
(e)valuation of ABS by investors and credit rating agencies. First, we expect our primary
findings to be stronger for ABS deals in which asset-level information provided under Reg AB II
adds more to the pool-level summary statistics for individual risk dimensions (e.g., FICO scores)
provided under the original Regulation AB. Asset-level information allows investors to jointly
assess all disclosed risk dimensions for each underlying asset. This information is thus directly
useful for assessing risk layering, i.e., for identifying individual assets and the proportion of
assets in the pool that are risky on multiple dimensions. In contrast, the pool-level summary
statistics for individual risk dimensions reveal little about risk layering; the same pool-level
statistics can be generated whether the individual risk dimensions are positively correlated (i.e.,
8
risk layering), uncorrelated, or negatively correlated across the individual assets in the pool. We
proxy for risk layering using the proportion of the underlying assets that exhibit multiple highrisk
dimensions in auto ABS deals in the post-Reg AB II period. As expected, we find that this proxy
predicts initial tranche yield spreads, initial tranche credit ratings, and subsequent loan
delinquencies in these deals, consistent with risk layering being priced by both investors and
rating agencies. We further find that our primary findings obtain only in deals with above-
median proportions of risk-layered assets.
Second, we expect the incremental value of asset-level disclosures to be greater for ABS
deals that involve more complex tranching of credit risk, and thus our primary findings to be
stronger for such deals. Prior studies find that higher deal complexity increases the difficulty for
market participants to assess the risk of ABS (e.g., He et al. 2012; Furfine, 2014; Efing and Hau,
2015; Ghent et al., 2017). Following these studies, we proxy for the complexity of credit risk
tranching using the number of tranches in each auto ABS deal. We find that our primary findings
obtain only in deals with above-median complex credit risk tranching.
A potential problem of inference confronting our study is that the treatment sample (auto
ABS) and the control sample may be differentially affected by omitted time-varying factors other
than Reg AB II’s asset-level disclosure requirements. To address this concern, we conduct a
falsification test using the pseudo effective date of June 1, 2015, approximately 18 months prior
to the effective date of Reg AB II’s asset-level disclosure requirements. None of our primary
findings obtain around this pseudo effective date, suggesting that time-related factors do not
explain these findings. We also show that our primary results are qualitatively similar when we
restrict the sample period to after the November 23, 2014 nominal effective date of Reg AB II or
the November 23, 2015 effective date of Reg AB II’s requirements other than for asset-level
9
disclosures, or when we measure the subsequent performance of the underlying assets over
alternative time horizons.
Our study contributes to the literature on the effects of transparency on financial markets.
The bulk of this literature examines corporate bond and equity markets (e.g., Bushman et al.,
2004; Yu, 2005; Francis et al., 2009; Lang et al., 2012; Firth et al., 2015). Two recent studies
examine the more opaque structured finance markets. Schmidt and Zhang (2018) find that
monthly public filings of pool-level information about the underlying assets required after
January 1, 2006 under the original Regulation AB increase the volume and liquidity of trading in
secondary ABS markets. Ertan et al. (2017) find that the European Central Bank’s loan-level
disclosure requirements for banks that pledge ABS as collateral to borrow under its repurchase
financing operations increase the quality of loans to small and medium-sized enterprises. Our
study complements these studies by examining how Reg AB II’s asset-level disclosure
requirements affect the ability of investors to value and credit rating agencies to evaluate ABS.
Our study also contributes to the debate about whether transparency enhances the
stability of banks and the financial system (Beatty and Liao, 2014; Bushman, 2014; Acharya and
Ryan, 2016). Most studies provide evidence of benefits of transparency, such as lower cost of
capital (Kleymenova, 2016), fewer failures (Granja, 2016), enhanced risk-taking discipline
(Bushman and Williams, 2012), lower exposure to illiquidity and tail risks (Bushman and
Williams, 2015), and increased loan supply (Balakrishnan and Ertan, 2017). Some papers
provide evidence of costs of transparency, however, including managerial myopia (Goldstein and
Sapra, 2013), limited risk-sharing (Goldstein and Leitner, 2018), and reduced liquidity provision
(Dang et al., 2017). We find that asset-level transparency improves the ability of investors and
credit rating agencies to predict subsequent underlying asset performance.
10
Our findings yield several policy implications. First, Reg AB II’s asset-level disclosure
requirements improve market participants’ (e)valuation of ABS, likely improving price
efficiency in ABS markets. Second, these improvements are attributable in part to more effective
due diligence by ABS investors and in part to higher quality credit ratings of ABS. The
improvement in credit ratings likely is attributable to these disclosures increasing either market
discipline over credit rating agencies or the agencies’ use of ABS price-related information,
unintended consequences of Reg AB II. Third, contrary to a stated primary objective of the SEC
for Reg AB II’s asset-level disclosure requirements, these disclosures do not reduce investors’
reliance on credit ratings.
2. Institutional Background
The SEC first issued comprehensive rules governing public offerings of ABS and related
disclosure requirements for ABS issuers in Regulation AB, which became effective on January
1, 2006. Reg AB II, which revised these requirements, was published in the Federal Register on
September 24, 2014 (SEC, 2014) and nominally became effective on November 24, 2014.
However, Reg AB II’s asset-level disclosure requirements became effective after a two-year
transition period on November 23, 2016, and all of its other requirements (e.g., shelf registration
eligibility requirements, requirements for other disclosures in the securitization prospectus, and
filing forms) became effective after a one-year transition period on November 23, 2015. ABS
issuers must file the required asset-level disclosures on SEC EDGAR in a standardized tagged
XML format using Form ABS-EE. They must also provide these disclosures in both
securitization prospectuses at ABS issuance and in subsequent periodic financial reports for the
securitization entities (SEC, 2014).
11
Reg AB II’s asset-level disclosure requirements only apply to the following types of
publicly offered (i.e., SEC-registered) ABS: Auto ABS, CMBS, residential mortgage backed
securities (“RMBS”), and resecuritizations of these ABS or debt securities (“CDO”). ABS
backed by other types of assets (e.g., credit card receivables, student loans, equipment loans and
leases, and floorplan financings) are not subject to these requirements. In contrast, Reg AB II’s
other requirements apply to all SEC-registered non-agency ABS (i.e., ABS issued by private
firms rather than by government-sponsored entities such as Fannie Mae).
The question arises why the SEC exempted certain types of ABS from Reg AB II’s asset-
level disclosure requirements. One possibility is the SEC expected the costs of these
requirements exceed their benefits for these ABS classes. If this possibility were true, any effects
of asset-level disclosures on the subject types of ABS that we document would overstate the
average effects of these disclosures for all types of ABS. However, SEC statements explaining
its decisions in developing Reg AB II suggest that the exemption of certain types of ABS from
the requirements is instead attributable to unresolved implementation issues. Specifically, in
2010 the SEC proposed to require asset-level disclosures for almost all ABS classes (SEC, 2014,
p. 57196).6 In Reg AB II, the SEC states that “[w]hile we are adopting [asset-level disclosure]
requirements for only certain asset classes, we continue to consider the appropriate disclosure
requirements for other asset classes and those [the 2010] proposals remain unchanged and
outstanding” (SEC, 2014, p. 57198). “For those asset classes where we are deferring action, we
will continue to consider the best approach for providing more information about underlying
assets to investors, including possibly requiring asset-level data in the future” (SEC, 2014, p.
57202).
6 The primary exception is ABS collateralized by credit and charge card receivables. Reflecting the huge number of
small accounts underlying these ABS, the SEC proposed requiring grouped-account disclosures for these deals
(SEC, 2014, p. 57196).
12
Although SEC-registered non-agency RMBS and CDO are subject to Reg AB II’s asset-
level disclosure requirements, new issuances of these types of ABS almost vanished as a
consequence of the financial crisis. Our comprehensive search on SEC EDGAR from the
effective date of the requirements on November 23, 2016 to the May 31, 2018 end of our sample
period yields no filings of Form ABS-EE for SEC-registered non-agency RMBS and CDO,
which implies that no public issuances of these types of ABS occurred during this period.7 We
thus exclude RMBS and CDO from our analysis. Only two types of ABS are both subject to the
asset-level disclosure requirements and have active markets during our sample period: auto ABS
and CMBS.
We expect that Reg AB II’s asset-level disclosure requirements substantially increase the
transparency of auto ABS. Appendix A provides representative examples of auto ABS issuers’
disclosures before and after these requirements become effective that support this expectation. In
contrast, we expect that the requirements do not appreciably increase the transparency of CMBS,
because CMBS securitization prospectuses and periodic financial reports for CMBS entities
typically disclosed detailed asset-level information prior to the effective date of the requirements.
The SEC states in Reg AB II that “[f]or CMBS, we note that issuers commonly provide investors
with asset-level disclosures at the time of securitization and on an ongoing basis pursuant to
industry developed standards” (SEC, 2014, p. 57197). As a result, in setting Reg AB II’s asset-
level disclosure requirements for CMBS, the SEC “made efforts to align our requirements, as
much as possible, with pre-established industry codes, titles and definitions to allow for the
comparability of future offerings with past offerings and to minimize the burden and cost of
reporting similar information in different formats” (SEC, 2014, p. 57222). Appendix B provides
7 Relatedly, the SEC states in Reg AB II that “over the past several years there have been no registered
resecuritizations of RMBS, CMBS or Auto ABS” (SEC, 2014, p. 57202).
13
representative examples of asset-level disclosures for CMBS before and after the effective date
of Reg AB II’s asset-level disclosure requirements. Based on a careful comparison of these
disclosures, we conclude that these requirements did not increase the transparency of the quality
of the assets underlying CMBS.
Reflecting this discussion, we include only SEC-registered auto ABS in the treatment
sample.8 We include both SEC-registered ABS not subject to the asset-level disclosure rule and
SEC-registered CMBS in the control sample. We include SEC-registered CMBS in the control
sample primarily because we expect Reg AB II’s asset-level disclosure requirements to have
little or no effect on CMBS, and also to increase the size of the control sample.
Although it seems intuitive that asset-level disclosures should help auto ABS investors
make better investment decisions, comment letters from large auto ABS issuers on the proposals
leading up to Reg AB II suggest otherwise. For example, a large group of non-bank auto ABS
issuers, including Ally Financial, Ford Motor Credit Company, and General Motors Financial
Company (2011, p. 10-11), state in a joint comment letter that they “vigorously disagree with the
Commission’s apparent assumption that asset-level disclosure should apply to Vehicle ABS”,
that “individual loan data would not be useful to investors”, and that “investors do not need data
points on individual receivables when there are many thousands of assets in the pool. A single
receivable is simply immaterial in a pool with the number of assets we typically securitize.”
Bank issuers make similar statements: Wells Fargo (2010, p.14) asserts that “the proposed asset-
level disclosures for auto loan securitizations would provide little or no incremental value to
investors”; J.P. Morgan (2010, p.12) asserts that “the proposed expansion of asset-level data
requirements … may, for most asset classes other than RMBS and CMBS, only provide
8 This is not especially restrictive, because in Reg AB II the SEC states that “Auto ABS represents a large portion of
the current SEC-registered ABS market” (SEC, 2014, P. 57201).
14
incremental [i.e., little] value to investors relative to the data that is currently disclosed”; and
Capital One (2010, p.2) states that “we do not believe that the proposed loan-level or grouped-
account disclosure rules for auto and credit card ABS would meaningfully enhance our
understanding of the underlying collateral.”
3. Research Design
We use the effective date of Reg AB II’s asset-level disclosure requirements on
November 23, 2016 as a positive shock to the transparency of publicly offered auto ABS that
does not affect the transparency of other types of publicly offered ABS.9 This setting enables us
to employ a differences-in-difference research design. Our treatment sample comprises SEC-
registered auto ABS deals. Our control sample consists of all other types of SEC-registered non-
agency ABS deals, including CMBS, credit card ABS, and equipment loan ABS.
3.1. Impact of Asset-Level Disclosures on Investors’ Valuation of ABS
We first examine the impact of asset-level disclosures on investors’ valuation of ABS in
the primary (i.e., at issuance) markets. We test whether these disclosures are associated with
increased ability of initial tranche yield spreads to predict the subsequent performance of the
underlying assets using the following model:
9 On December 24, 2016, approximately one month after the effective date of Reg AB II’s asset-level disclosure
requirements on November 23, 2016, the Dodd-Frank Act’s risk retention rules came into effect (Department of the
Treasury et al., 2014). Except for securitizations of qualified residential mortgages (which are largely securitized
through government-sponsored entities, and thus exempt from Reg AB II’s asset-level disclosure requirements),
these risk retention rules require a sponsor of an ABS deal or its majority-owned affiliate to retain an economic
interest equal to at least five percent of the aggregate credit risk of the underlying assets. The risk retention rules
apply to all types of non-agency ABS, however, and hence do not affect the validity of our use of the asset-level
disclosure requirements as a positive shock to the transparency of auto ABS.
15
Delinquency Ratej = α0 + α1 (Yieldi,j × Treatj × Postt) + α2 Yieldi,j
+ α3 (Yieldi,j × Treatj) + α4 (Yieldi,j × Postt) + α5 (Treatj × Postt) + ∑αk Deal-level Control Variablen
j (1)
+ ∑αh Tranche-level Control Variableni
+ Issuing Year-quarter fixed effects + Issuer fixed effects.
The dependent variable, Delinquency Ratej, stands in for one of three measures of delinquent
assets as a percentage of the underlying assets for deal j: the percentages of the underlying assets
that are delinquent for at least 30 days (Delinquency_30plus), 60 days (Delinquency_60plus), or
90 days (Delinquency_90plus). To ensure comparable loan seasoning effects across deals, we
measure these delinquency variables at 120 days after ABS issuance.10 The primary explanatory
variable of interest is the three-way interaction of the initial tranche yield spread over a reference
rate (described below) for tranche i in deal j, Yieldi,j, an indicator for SEC-registered auto ABS,
Treatj,11 and an indicator for ABS issuance dates after the November 23, 2016 effective date of
Reg AB II’s asset-level disclosure requirements, Postt.12 For tranches with floating coupon rates,
we measure Yield as the specified fixed markup over the specified reference rate. For tranches
with fixed coupon rates, we measure Yield as the initial tranche coupon rate less the yield on a
Treasury security with similar maturity as the disclosed Average Life of the tranche (He et al.
2016).13 A significantly positive coefficient α1 on Yield × Treat × Post is consistent with asset-
level disclosures increasing investors’ ability to value ABS, as reflected in the ability of initial
yield spreads to predict the subsequent performance of the underlying assets.
10 120 days after ABS issuance is long enough to allow the underlying assets to become delinquent but short enough
so that we do not lose excessive post-Reg ABS II observations owing to missing delinquency data. Our results are
robust to the use of alternative time horizons such as 90 days or 180 days after issuance. 11 We do not include Treat as an explanatory variable in equation (1) because no sample issuers issue both treatment
deals and control deals. We do not include Post as an explanatory variable because the equation includes issuing
year-quarter fixed effects. 12 All indicator variables in the paper take a value of one when the condition is present and zero otherwise. 13 Bloomberg classifies some tranches as having “variable” coupon rates. Inspection of selected deal prospectuses
indicates that these coupon rates vary with the unamortized principal balances of the underlying assets, not with
reference rates, and that these assets are fixed rate. Hence, we treat these tranches as having fixed coupon rates.
16
Following prior literature (e.g., He et al., 2012), equation (1) includes the Number of
Tranches in a deal to control for the complexity of credit-risk tranching. Equation (1) includes
five variables to control for tranche heterogeneity: Tranche Size is the natural log of the initial
principal amount of the tranche; Average Life is the disclosed expected number of years to repay
the tranche’s initial principal; Subordination is the principal amount of the (junior) tranches in
the deal with worse credit ratings than the tranche, divided by the total principal amount of the
deal; Floating is an indicator for tranches with floating coupon rates; and One Rating is an
indicator for tranches rated by only one credit rating agency.14, 15 Because the sample deals
involve different issuers and issued ABS at different points in time, equation (1) includes issuer
and issuing year-quarter fixed effects.
We estimate equation (1) using OLS. As in subsequent equations, the dependent variable
in equation (1) is measured at the deal level while the independent variables of interest are
measured the tranche level,16 and so we calculate standard errors clustering observations by deal.
3.2. Impact of Asset-Level Disclosures on Credit Rating Agencies’ Evaluation of ABS
We next examine the impact of asset-level disclosures on credit rating agencies’
evaluation of ABS. We test whether asset-level disclosures are associated with increased ability
of initial credit ratings to predict the subsequent performance of the underlying assets using the
following model:
14 Unlike He et al. (2012), we do not include an indicator for tranches rated by two credit rating agencies, because
only 2% of the sample tranches are rated by all three credit rating agencies. 15 In the tabulated estimations of equation (1) and subsequent equations, we include the control variables separately.
In untabulated estimations, we also include the control variables interactively with Treat, Post, and Treat × Post,
which yields the same infererences as the tabulated estimations. We do not include these interactive controls in the
tabulated estimations because this inclusion increases the variance inflation factors on some of the explanatory
variables beyond conventionally accepted levels. 16 We measure the independent variables of interest at the tranche level rather than at the deal level in part to
maximize power, given only 684 sample deals, and in part to mitigate multicollinearity, given our highly interactive
models.
17
Delinquency Ratej = β0 + β1 (Ratingi,j × Treatj × Postt) + β2 Ratingi,j
+ β3 (Ratingi,j × Treatj) + β4 (Ratingi,j × Postt)
+ β5 (Treatj × Postt) + ∑βk Deal-level Control Variablenj (2)
+ ∑βh Tranche-level Control Variableni
+ Issuing Year-quarter fixed effects + Issuer fixed effects.
Ratingi,j is the average of the initial credit ratings provided by S&P, Moody’s, or Fitch for
tranche i in deal j. Higher values of Rating indicate higher credit risk. The primary explanatory
variable of interest is the three-way interaction Rating × Treat × Post. A significantly positive
coefficient β1 on this variable is consistent with asset-level disclosures increasing credit rating
quality as reflected in the ability of credit ratings to predict the subsequent performance of the
underlying assets.
Equation (2) includes the same control variables and fixed effects as in equation (1). We
estimate equation (2) using OLS and calculate standard errors clustering observations by deal.
3.3. Impact of Asset-Level Disclosures on Investor Due Diligence
We next examine the impact of asset-level disclosures on investor due diligence
independent of credit ratings. Since investors observe and typically use credit ratings as
important inputs in their decisions to purchase ABS, we argue that the portion of tranche yield
spreads that cannot be explained by credit ratings is a reasonable proxy for investors’
independent due diligence. Employing a similar decomposition approach as used by prior studies
examining various other contexts (e.g., Frederickson and Zolotoy, 2016; Healy and Serafeim,
2016; Bhattacharya et al., 2012; Lee et al., 2018), we calculate the portion of yield spreads that
are not explained by credit ratings as the residual from an OLS regression of initial yield spreads
on initial credit ratings. We test whether asset-level disclosures increase the ability of residual
yield spreads to predict subsequent asset performance, controlling for tranche credit ratings,
using the following model:
18
Delinquency Ratej = γ0 + γ1 (Yield_Residuali,j × Treatj × Postt) + γ2 Yield_Residuali,j
+ γ3 (Yield_Residuali,j × Treatj) + γ4 (Yield_Residuali,j × Postt)
+ γ5 Ratingi,j + γ6 (Ratingi,j × Treatj) + γ7 (Ratingi,j × Postt)
+ γ8 (Ratingi,j × Treatj × Postt) + γ9 (Treatj × Postt) (3)
+ ∑γk Deal-level Control Variablenj
+ ∑γh Tranche-level Control Variablen
+ Issuing Year-quarter fixed effects + Issuer fixed effects.
Yield_Residual is the portion of initial tranche yield spreads that is not explained by initial credit
ratings. The main explanatory variable of interest is Yield_Residual × Treat × Post.17 Equation
(3) controls for Rating both separately and interacted with Treat, Post, and Treat × Post to
capture any improvements in the predictive power of yield spreads for subsequent asset
performance attributable to investors relying on higher-quality credit ratings under asset-level
disclosure. A significantly positive coefficient γ1 on Yield_Residual × Treat × Post indicates that
initial yield spreads better predict subsequent performance after the effective date of Reg AB II’s
asset-level disclosure requirements, consistent with asset-level disclosures enabling investors to
conduct more effective due diligence independent of credit ratings.
Equation (3) also includes all of the other control variables and fixed effects in equations
(1) and (2). We estimate equation (3) using OLS and calculate standard errors clustering
observations by deal.
3.4. Impact of Asset-Level Disclosures on Investors’ Reliance on Credit Ratings
Lastly, we examine whether asset-level disclosure requirements reduce investors’
reliance on credit ratings, a stated primary objective of the SEC for Reg AB II’s asset-level
disclosure requirements. We test whether the association between initial tranche yields and initial
tranche credit ratings weakens under asset-level disclosures using the following model:
17 We use Yield_Residual instead of Yield to mitigate multicollinearity, because the correlation between Yield ×
Treat × Post and Rating × Treat × Post is as high as 0.93.
19
Yieldj,j = α0 + α1 (Ratingi,j × Treatj × Postt) + α2 Ratingi,j
+ α4 (Ratingi,j × Treatj) + α5 (Ratingi,j × Postt)
+ α6 (Treatj × Postt) + ∑γk Deal-level Control Variablen (4)
+ ∑γh Tranche-level Control Variablen
+ Issuing Year-quarter fixed effects + Issuer fixed effects.
The main explanatory variable of interest is the three-way interaction term Rating × Treat ×
Post. A significantly negative coefficient α1 on this variable is consistent with asset-level
disclosures reducing investors’ reliance on credit ratings.
Equation (4) includes all of the control variables and fixed effects in prior equations. We
estimate equation (4) using OLS and calculate standard errors clustering observations by deal.
4. Sample Selection and Summary Statistics
We obtain our initial sample of 2,353 non-agency ABS deals (other than RMBS and
CDO deals18) with issuance dates from January 1, 2013 to May 31, 2018 in the U.S. from
Bloomberg. These deals include 15,170 initial sample tranches. For the deals, we extract from
Bloomberg deal-level information including issuer name, issuance date, collateral type (e.g., auto
loans, student loans, credit card receivables, commercial mortgages), and the rates of 30-, 60-,
and 90-day delinquencies in the underlying asset pool at 120 days after issuance.19 For deals with
missing delinquencies on Bloomberg, we hand collect available delinquency data from Moody’s
ABS & ABCP database. For the tranches, we extract from Bloomberg tranche-level information
including any initial credit ratings from the three largest credit rating agencies (S&P, Moody’s,
and Fitch), coupon type and rate, principal amount, and weighted-average life. We winsorize all
continuous variables at the 1st and 99th percentiles of their distributions.
18 We discuss why we exclude RMBS and CDO from the sample in Section 2. 19 Bloomberg reports delinquency rates monthly. We use the delinquency rates on the day closest to 120 days after
issuance.
20
Panel A of Table 1 summarizes the sample selection. From the initial sample, we remove
privately placed deals (which are not subject to Reg AB II’s asset-level disclosure requirements)
and federally insured student loan ABS deals (which involve minimal credit risk transfer to
investors). We also require deals to have non-missing data on all variables, which eliminates all
deals with issuance dates after January 31, 2018 owing to missing delinquency rates 120 days
after issuance.20 Lastly, we remove a small number of equity (i.e., residual) tranches, which
usually are retained but when sold typically are sold to hedge funds and similar risk-tolerant
speculative investors. These requirements yield a final sample of 684 deals and 3,984 tranches
issued by 60 distinct consolidated firms. 21 Of the 684 sample deals, 293 (43 percent) are
treatment, i.e., auto ABS, deals and 391 are control deals. Of the treatment deals, 236 (57) were
issued before (after) the effective date of Reg AB II’s asset-level disclosure requirements.
Panel B of Table 1 reports the composition of the sample tranches by collateral type and
issuing year. Of the 3,984 sample tranches, 1,368 (34 percent) are treatment tranches and 2,616
are control tranches. Of the treatment tranches, 1,102 (80 percent) were issued before the
November 23, 2016 effective date of Reg AB II’s asset-level disclosure requirements and 266
were issued afterwards. Of the 2,616 control tranches, 2,331 (89 percent) are CMBS, and 2,028
(78 percent) were issued before the effective date of the asset-level disclosure requirements and
588 were issued afterwards.
Panel C of Table 1 reports descriptive statistics for the model variables. As of 120 days
after ABS issuance, on average 0.84 percent of the underlying assets are 30 or more days past
due (Delinquency_30plus), 0.24 percent of the assets are 60 or more days past due
20 This requirement also eliminates all public offerings of ABS backed by floorplan financings, as these deals have
missing delinquency information. 21 A consolidated firm often owns multiple distinct issuers on Bloomberg. We treat such affiliated issuers as a single
firm.
21
(Delinquency_60plus), and 0.10 percent of the assets are 90 or more days past due
(Delinquency_90plus). The average tranche Yield spread is 1.06 percent. The mean tranche credit
Rating is 2.45, which corresponds to an S&P rating between AA+ and AA. The average Number
of Tranches in a deal is 14, the average Tranche Size is $98 million (i.e., e18.40), and the Average
Life is 5.88 years. The average tranche Subordination equals 29 percent of the principal amount
of a deal. Of the sample tranches, 6 percent have Floating coupon rates and 21 percent have only
One Rating from the three major credit rating agencies.
5. Empirical Results
5.1. Impact of Asset-Level Disclosures on Investors’ Valuation of ABS
Table 2 reports the OLS estimation of equation (1), which we use to test the effect of
asset-level disclosures on investors’ valuation of ABS. Column 1 reports the estimation of the
model with the rate of 30 or more days past due delinquencies on the underlying assets 120 days
after ABS issuance (Delinquency_30plus) as the dependent variable. The coefficient on the main
explanatory variable of interest, Yield × Treat × Post, is significantly positive (0.542, p = 0.048),
indicating that the ability of initial yield spreads to predict subsequent asset delinquency
increases after the effective date of Reg AB II’s asset-level disclosure requirements.22 This
finding is consistent with asset-level disclosures improving investors’ ability to value ABS. To
give a sense for economic significance, when initial yield spreads increase by one standard
deviation (0.74 percent), subsequent 30-day delinquencies as percentage of the underlying assets
increase by 0.40 percent (48 percent of the unconditional mean of the 30-day delinquencies) for
the treatment sample relative to the control sample under Reg AB II’s asset-level disclosure
22 Unless indicated otherwise, we refer to a coefficient or other statistic as significant if it is significant at the five
percent level or better in a two-tailed test.
22
requirements compared to the prior requirements.23 Columns 2 and 3 report the qualitative
similar results of estimating the models with the rates of 60 and 90 day or more days
delinquency, respectively, as the dependent variable.
The coefficients on the control variables generally are interpretable. The coefficient on
Yield is significantly positive in all three columns of the table, consistent with tranche yield
spreads being positively associated with delinquencies on the underlying assets. The coefficient
on Yield × Treat is significantly positive in column 1 but is significantly negative in column 3.
The significantly negative coefficient in column 3 likely reflects the practice of charging off auto
loans at 120 days past due, earlier than most other types of securitized assets; faster charge-offs
reduce the number of loans delinquent for longer periods of time (Ryan 2007, Chapter 5). The
coefficient on Yield × Post is significantly negative in column 1 and insignificant in columns 2
and 3. The coefficient on Tranche Size is significantly positive, and the coefficient on the
Average life of a tranche is significantly negative.
5.2. Impact of Asset-Level Disclosures on Credit Rating Agencies’ Evaluation of ABS
Table 3 reports the OLS estimation of equation (2), which we use to test the effect of
asset-level disclosure on credit rating agencies’ evaluation of ABS. Column 1 reports the model
with Delinquency_30plus as the dependent variable. The coefficient on the main explanatory
variable of interest, Rating × Treat × Post, is significantly positive (0.068, p = 0.038), consistent
with asset-level disclosures improving credit rating agencies’ ability to evaluate the credit risk of
ABS. Because Reg AB II apparently did not change the extent to which credit rating agencies
rely on asset-level information, this result suggests that the public release of this information
23 The calculation is 0.74% × 0.542 ÷ 0.84% = 0.40% ÷ 0.84% = 48%, where 0.542 is the coefficient on the main
explanatory variable of interest (Yield×Treat×Post) reported in column 1 of Table 2, and 0.84% is the unconditional
mean of subsequent 30-day delinquencies as percentage of the underlying assets.
23
either disciplines the agencies’ evaluations of ABS or increases their incorporation of ABS price-
related information into credit ratings. To give a sense for economic significance, when initial
credit ratings deteriorate by one standard deviation (2.49 ratings notches), subsequent 30-day
delinquencies as percentage of the underlying assets increase by 0.17 percent (20 percent of the
unconditional mean of 30-day delinquencies) for the treatment sample relative to the control
sample under Reg AB II’s asset-level disclosure requirements versus the prior requirements.24
Columns 2 and 3 of Table 3 report the qualitative similar results of estimating equation
(1) with the rates of 60 and 90 day or more days delinquency, respectively, as the dependent
variable. We do not discuss the coefficients on the control variables, which are similar to those
reported in Table 2.
5.3. Impact of Asset-Level Disclosures on Investor Due Diligence
Table 4 reports the OLS estimations of equation (3), which we use to test the effect of
asset-level disclosure on investor due diligence. Columns 1 through 3 report the estimations with
Delinquency_30plus, Delinquency_60plus, and Delinquency_90plus, respectively, as the
dependent variable. The coefficient on the main explanatory variable of interest, Yield_Residual
× Treat × Post, is insignificant in Column 1 but significantly positive in Columns 2 and 3. These
significant coefficients indicate that the portion of tranche yield spreads unexplained by credit
ratings has incremental explanatory power for the subsequent performance of underlying assets
in the treatment sample under Reg AB II’s asset-level disclosure requirements compared to the
prior requirements, controlling for the increased ability of tranche credit ratings to predict
delinquencies on the underlying assets under the disclosure requirements. These results are
24 The calculation is 2.49 × 0.068 ÷ 0.84% = 0.17 ÷ 0.84% = 20%, where 0.068 is the coefficient on the main
explanatory variable of interest (Rating×Treat×Post) reported in column 1 of Table 3, and 0.84% is the
unconditional mean of subsequent 30-day delinquencies as percentage of the underlying assets.
24
consistent with asset-level disclosures enabling investors to conduct more effective due diligence
of ABS deals independent of credit ratings.
5.4. Impact of Asset-Level Transparency on Investors’ Reliance on Credit Ratings
Table 5 reports the OLS estimation of equation (4) testing the effect of asset-level
disclosures on investors’ reliance on tranche credit ratings in their decisions to purchase tranches.
This analysis does not require non-missing data on delinquencies, which yields a sample size
about 15 percent larger than in prior regression analyses. The coefficient on the main variable of
interest, Rating × Treat × Post, is significantly positive (0.065, p = 0.001). Thus, contrary to a
stated objective of the SEC, we find that investors increase their reliance on credit ratings after
the effective date of Reg AB II’s asset-level disclosure requirements.
6. Risk Layering and the Complexity of Credit Risk Tranching as Mechanisms
In this section, we conduct cross-sectional analyses to evaluate two mechanisms by
which asset-level disclosures might improve the (e)valuation of ABS by investors and credit
rating agencies, as well as investors’ due diligence independent of credit ratings. First, we expect
that asset-level disclosures under Reg AB II’s requirements are more useful when the underlying
assets exhibit more risk layering, which was opaque to investors under the disclosure
requirements of the original Regulation AB. Second, we expect these disclosures are more useful
in ABS deals with more complex tranching of credit risk.
6.1. Risk Layering
Risk layering refers to the existence of multiple high-risk dimensions in an individual
asset or a high proportion of such assets in a pool (Office of the Comptroller of the Currency
[OCC], 2018, p. 21). For example, a risk-layered auto loan might exhibit a low FICO score
borrower, a high loan-to-value ratio, and low loan documentation. The presence of multiple high-
25
risk dimensions naturally tends to increase the default risk of an individual loan. Moreover, pools
of loans with higher proportions of risk-layered loans tend to exhibit higher default rates, holding
the average levels of individual risk dimensions constant, because risk-layered loans tend to be
close to or even to exceed prudent risk management limits and thus typically perform very
poorly when economic conditions change for the worse (OCC, 2018, p. 22). Opaque risk
layering of the subprime mortgages underlying many structured finance products during the
boom period preceding the financial crisis is believed to have contributed to the system-wide
buildup of default risk during this period (Bernanke, 2007; Financial Crisis Inquiry Report, 2011,
p. 118; FDIC, 2017, Ch. 1, p. 12). The OCC identifies risk layering as one of four key areas to
assess and monitor credit risk in retail lending (OCC, 2018, p. 18).
Market participants can best detect risk layering when securitization issuers provide
asset-level information about all relevant credit risk dimensions. Hence, we expect that Reg AB
II’s asset-level disclosures improve market participants’ (e)valuation of treatment ABS deals by
allowing them to estimate the extent of risk layering in the underlying asset pools.
We proxy for the extent of risk layering in an auto ABS deal as follows. We define an
auto loan as risk layered if it exhibits three or more of the following five high-risk credit risk
indicators: lowest quartile borrower FICO credit score, highest quartile loan-to-value ratio,
lowest quartile borrower income, actual payment more than 90 percent below scheduled
payment, and no documentation of borrower income or employment. The indicator variable
D_MultipleLayers takes a value of 1 for a risk-layered auto loan, and zero otherwise. Risk
Layering is the weighted average, based on loan amount, of D_MultipleLayers for the loans in
the deal.
26
We first show that, after the effective date of Reg AB II’s asset-level disclosure
requirements, investors (credit rating agencies) make ABS investment (rating) decisions
incorporating risk layering beyond pool-level average individual risk dimensions, such as
average FICO score. We regress initial tranche yield spreads (credit ratings) on Risk Layering,
controlling for the pool-level averages of each of the five risk metrics used in constructing Risk
Layering, the tranche characteristics included in prior equations, and an indicator distinguishing
auto loan deals from auto lease deals. Columns 1 and 2 of Panel A of Table 6 present OLS
estimations of the initial tranche yield spread model. The estimation reported in column 1
includes only the control variables as explanatory variables. As expected, pool-level average
FICO is significantly negatively associated with spreads, while pool-average Loan-to-Value and
the proportion of low-documentation loans in a deal (Low Documentation) are significantly
positively associated with spreads. The estimation reported in column 2 includes both Risk
Layering and the control variables. As expected, the coefficient on Risk Layering is significantly
positive (0.025, p < 0.001). Moreover, the absolute magnitudes of the coefficients on the pool-
level average FICO and Loan-to-Value in column 2 are significantly smaller than the
corresponding coefficients in column 1 (the untabulated p-values of the coefficient differences
are 0.004 and 0.010, respectively), consistent with Risk Layering subsuming sizable portions of
the ability of pool-level average credit risk metrics to explain yield spreads.
Columns 3 and 4 of Panel A of Table 6 present estimations of the initial tranche credit
rating model. The estimation reported in column 3 includes only the control variables as
explanatory variables. As expected, pool-level average FICO is significantly negatively
associated with ratings, while average Loan-to-Value and Low Documentation are significantly
positively associated with ratings. The estimation reported in column 4 includes both Risk
27
Layering and the control variables. As expected, the coefficient on Risk Layering is significantly
positive (0.100, p = 0.009). Moreover, the absolute magnitudes of the coefficients on pool-level
average FICO and Loan-to-Value are significantly smaller than the corresponding coefficients in
column 3 (the untabulated p-values of the coefficient differences are 0.014 and 0.039,
respectively), consistent with Risk Layering subsuming sizable portions of the ability of pool-
level average credit risk metrics to explain credit ratings.
We next demonstrate that asset-level disclosures improve market participants’ ability to
(e)valuate risk layering of the assets underlying ABS. We show that estimates of risk layering
based on these disclosures predict subsequent loan performance beyond pool-level average
individual risk dimensions. Panel B of Table 6 reports the results of this analysis. Columns 1 and
2 of the panel present estimations of the model with the rate of 30 or more days past due
delinquencies on the underlying assets 120 days after ABS issuance (Delinquency_30plus) as the
dependent variable. The estimation reported in column 1 includes only the control variables as
explanatory variables. Delinquency_30plus is significantly negatively associated with pool-level
average FICO and Actual-payment-to-scheduled-payment ratio. The estimation reported in
column 2 includes both Risk Layering and the control variables as explanatory variables. As
expected, the coefficient on Risk Layering is significantly positive (0.435, p < 0.001). Moreover,
the coefficients on pool-level average FICO and Actual-payment-to-scheduled-payment Ratio are
insignificant in column 2, and the absolute magnitudes of these coefficients are significantly
smaller than the corresponding coefficients in column 1 (the untabulated p-values of the
coefficient differences are <0.001 and 0.002, respectively), consistent with Risk Layering
subsuming most of the ability of the pool-level averages of these credit risk metrics to explain
subsequent loan performance. Columns 3 through 6 report the qualitatively similar results of
28
estimating the models with the rates of 60 and 90 or more days delinquencies on the underlying
assets 120 days after ABS issuance, respectively, as the dependent variable.
The results described above are consistent with asset-level disclosures improving market
participants’ evaluation of risk layering in treatment ABS deals. To further support this
conclusion, we conduct a cross-sectional analysis using a difference-in-differences research
design to show that our primary findings are stronger for the treatment deals with more risk
layering. For deals with no or few risk-layered loans, Reg AB II’s asset-level disclosures of
multiple credit risk dimensions likely do not provide much incremental information beyond what
investors can learn from the pool-level averages of individual credit risk metrics disclosed under
the original Regulation AB. In contrast, for deals with many risk-layered loans, asset-level
disclosures enable investors to determine the extent of risk layering in the underlying assets.
An obstacle we face in implementing this research design is that asset-level data are not
available prior to the effective date of Reg AB II’s asset-level disclosure requirements to
calculate the extent of risk layering in the underlying assets. This obstacle is manageable,
however, because after that effective date we observe stable levels of Risk Layering across
treatment deals with the same issuer. For example, all three of the auto loan deals issued by
Nissan North America Inc., exhibit relatively low levels of Risk Layering, whereas all six of the
auto deals issued by Carmax Inc., exhibit relatively high levels of Risk Layering. We assume that
the stability of Risk Layering extends to auto ABS deals issued prior to the effective date of Reg
AB’s asset-level disclosure requirements, and classify these deals as exhibiting above-median
(below-median) Risk Layering if the auto ABS deals issued by the same issuer after that date
exhibit above-median (below-median) Risk Layering.25
25 We split only the auto ABS (i.e., treatment) sample, not our overall sample, based on the magnitude of Risk
Layering. We cannot observe asset-level information and thus risk layering for some control deals. While we can
29
Panels A through C of Table 7 report the estimated coefficient on the explanatory
variable of interest in OLS estimations of equations (1) through (3), respectively, for the
combination of each treatment subsample with the control sample. Columns 1, 3, and 5 of each
panel present the results for the auto ABS deals with above-median Risk Layering for the 30, 60,
and 90, respectively, days past due delinquency measures as the dependent variable. Columns 2,
4, and 6 present these estimations for the auto ABS deals with below-median Risk Layering.
These panels also report the difference in the estimated coefficient on the explanatory variable of
interest for the above- versus below-median Risk Layering deals for each of the delinquency
measures.
Panel A reports the estimations of equation (1) used in the investor valuation analysis.
The coefficient on the explanatory variable of interest, Yield × Treat × Post, is significantly
positive in columns 1, 3, and 5 for the above-median Risk Layering auto ABS deals, but is
insignificant in columns 2, 4, and 6 for the below-median Risk Layering auto ABS deals.
Moreover, the differences in the coefficient on Yield × Treat × Post for the above- versus below-
median Risk Layering deals are significantly positive for all three delinquency measures (10
percent level for Delinquency_30plus). These results are consistent with asset-level disclosures
improving investors’ valuation of ABS for the deals with above-median Risk Layering.
Panel B reports the estimations of equation (2) used in the credit rating quality analysis.
As in Panel A, the coefficient on the explanatory variable of interest, Rating × Treat × Post, is
significantly positive in columns 1, 3, and 5 for the above-median Risk Layering auto ABS deals,
but is insignificant in columns 2, 4, and 6 for the below-median Risk Layering deals. Moreover,
the differences in the coefficient on Rating × Treat × Post for the above- versus below-median
observe asset-level information for CMBS deals, for these deals we expect risk layering to have the same effects in
the period before and after the effective date of Reg AB II’s asset-level disclosure requirements.
30
Risk Layering deals are significantly positive for all three delinquency measures (10 percent
level for Delinquency_30plus and Delinquency_60plus). These results are consistent with asset-
level disclosures improving the quality of credit ratings for the deals with above-median Risk
Layering.
Lastly, Panel C reports the OLS estimations of equation (3) used in the investor due
diligence analysis. The coefficient on the explanatory variable of interest, Yield_Residual ×
Treat × Post, is insignificant in column 1 but significantly positive in columns 3 and 5 for the
above-median Risk Layering auto ABS deals. In contrast, the coefficient on Yield_Residual ×
Treat × Post is insignificant in columns 2, 4, and 6 for the below-median Risk Layering auto
ABS deals. Moreover, the differences in the coefficient on Yield_Residual × Treat × Post for the
above- versus below-median Risk Layering deals are significantly positive for
Delinquency_60plus (10 percent level) and Delinquency_90plus. These results are largely
consistent with asset-level disclosures improving investors’ due diligence independent of credit
ratings for the deals with above-median Risk Layering.
6.2. Complexity of Credit Risk Tranching
Prior research shows that more complex tranching of the credit risk of the underlying
assets in an ABS deal increases the difficulty for market participants to assess the credit risk of
the ABS tranches issued (He et al., 2012; Furfine, 2014; Efing and Hau, 2015; Ghent et al.,
2017). Hence, we expect that asset-level disclosures are more useful for assessing the credit risk
of ABS issued in deals with more complex tranching of the credit risk of the underlying assets.
Following this prior research, we use the number of tranches in a deal as a measure of the
complexity of credit risk tranching. Increasing the number of tranches with distinct seniority
causes the payoffs to the representative individual tranche to become more nonlinear in the
31
subsequent performance of the underlying assets and thus to be more sensitive to assumptions
about that performance (Furfine, 2014).
To conduct the cross-sectional analysis of the complexity of credit risk tranching, we split
the treatment sample each year into above- versus below-median number of tranches.26 Panels A
through C of Table 8 report the estimated coefficient on the explanatory variable of interest in
OLS estimations of equations (1) through (3), respectively, for the combination of each treatment
subsample and the control sample. Columns 1, 3, and 5 of each panel present the results for the
above-median number of tranches treatment deals for the 30, 60, and 90, respectively, days past
due delinquency measures as the dependent variable. Columns 2, 4, and 6 present these results
for the below-median number of tranches treatment deals. These panels also report the difference
in the estimated coefficient on the explanatory variable of interest for the above- versus below-
median number of tranches deals for each of the delinquency measures.
Panel A reports the OLS estimations of equation (1) used in the investor valuation
analysis. The coefficient on the explanatory variable of interest, Yield × Treat × Post, is
significantly positive in columns 1, 3, and 5 for the above-median number of tranches treatment
deals, but is insignificant in columns 2, 4, and 6 for the below-median treatment deals. However,
the differences in the coefficient on Yield × Treat × Post for the above- versus below-median
number of tranches deals are insignificant. These results are consistent with asset-level
disclosures improving investor valuation of ABS for the above-median number of tranches
treatment deals.
26 We split our treatment sample, not our overall sample, each year based on number of tranches for two reasons.
First, our treatment sample is auto ABS deals, and these deals typically have fewer tranches than the control sample
deals. Second, we do not expect the complexity of credit-risk tranching to affect the control sample observations
differently in the pre- and post-Reg AB II periods.
32
Panel B reports the OLS estimations of equation (2) used in the credit rating quality
analysis. As in Panel A, the coefficient on the explanatory variable of interest, Rating × Treat ×
Post, is significantly positive in columns 1, 3, and 5 for the above-median number of tranches
treatment deals, but is insignificant in columns 2, 4, and 6 for the below-median deals. However,
the differences in the coefficient on Rating × Treat × Post for the above- versus below-median
number of tranches deals are insignificant. These results are consistent with asset-level
disclosures improving the quality of ABS credit ratings for the above-median number of tranches
treatment deals.
Lastly, Panel C reports the OLS estimations of equation (3) used in the investor due
diligence analysis. The coefficient on the explanatory variable of interest, Yield_Residual ×
Treat × Post, is insignificant in column 1 but significantly positive in columns 3 and 5 for the
above-median number of tranches treatment deals. The coefficient on Yield_Residual × Treat ×
Post is insignificant in columns 2, 4, and 6 for the below-median number of tranches treatment
deals. The differences in the coefficient on Yield_Residual × Treat × Post for the above- versus
below-median number of tranches deals are significant only for Delinquency_90plus. These
results are consistent with asset-level disclosures having an impact on investor due diligence for
the above-median number of tranches treatment deals.
Overall, the results from the cross-sectional analyses reported in Tables 7 and 8 are
consistent with the observed improvements in investors’ valuation of ABS, credit rating
agencies’ evaluation of ABS, and investors’ due diligence independent of credit ratings being
driven by asset-level information improving these market participants’ ability to analyze auto
ABS deals.
33
7. Robustness Tests
7.1. Falsification Test
A potential alternative explanation for our results is that omitted time-varying factors
other than the effective date of Reg AB II’s asset-level disclosure requirements differentially
affect auto ABS (treatment) deals and other types of ABS (control) deals. In this section, we
conduct a falsification test to help rule out this possibility. We eliminate all deals with issuance
dates on or after November 23, 2016, the effective date of the requirements, and use only the
prior sample period to conduct the falsification test. To maintain the length of the post-period
used in our main tests, which spans the approximately 18 months from November 23, 2016 to
May 31, 2018, we use June 1, 2015 as the “pseudo” effective date of the asset-level disclosure
requirements in this falsification test. Panels A to C of Table 9 report the coefficients on the main
explanatory variables of interest for the investor valuation, credit rating agency evaluation, and
investor due diligence analyses, respectively. Columns 1 through 3 of each panel present the
results for the 30, 60, and 90, respectively, days past due delinquency measures.
Panel A reports the OLS estimations of equation (1) used in the investor valuation test.
The coefficient on the explanatory variable of interest, Yield × Treat × PostFalsif, is
insignificant in all three columns. Panel B reports the OLS estimations of equation (2) used in
the credit rating quality analysis. The coefficient on the explanatory variable of interest, Rating ×
Treat × PostFalsif, is insignificant in all three columns. Lastly, Panel C reports the OLS
estimations of equation (3) used in the investor due diligence analysis. The coefficient on the
explanatory variable of interest, Yield_Residual × Treat × PostFalsif, is insignificant in all three
columns. Collectively, the results of these falsification tests suggest that omitted time-related
factors are unlikely to explain our primary findings.
34
7.2. Using Alternative Pre-Periods
Two potentially important events related to Reg AB II occurred during the sample period
prior to the effective date of its asset-level disclosure requirements. First, the nominal effective
date of Reg AB II is November 23, 2014, two years prior to the effective date of its asset-level
disclosure requirements. Second, the effective date of all of Reg AB II’s requirements other than
its asset-level disclosure requirements is November 23, 2015, one year prior. These two events
conceivably might have affected ABS markets sufficiently to render our pre-and post-periods
incomparable for reasons unrelated to the asset-level disclosure requirements. To address this
concern, we define alternative sample periods that begin at each these events (i.e., November 23,
2014 or November 23, 2015) and end on May 31, 2018. Panel A through C of Table 10 reports
the estimations of equations (1) through (3) using these shorter sample periods. Columns 1, 3,
and 5 of each panel present the results for the sample period beginning on November 23, 2014
for the 30, 60, and 90, respectively, days past due delinquency measures as the dependent
variables. Columns 2, 4, and 6 present these results for the sample period beginning on
November 23, 2015.
Panel A reports the results for the investor valuation analysis. The coefficient on Yield ×
Treat × Post is significantly positive in the four columns with Delinquency_60plus and
Delinquency_90plus as the dependent variables. However, in column 1, with
Delinquency_30plus as the dependent variable and the sample period beginning on November
23, 2014, the coefficient is significantly positive only at the 10 percent level. In column 2, with
Delinquency_30plus as the dependent variable and the sample period beginning on November
23, 2015), the coefficient is insignificant.
35
Panel B reports the results for the credit rating quality analysis. The coefficient on Rating
× Treat × Post is significantly positive in all six columns (10 percent level in column 2 with
Delinquency_30plus as the dependent variable and the sample period beginning on November
23, 2015).
Panel C reports the results for the investor due diligence analysis. The coefficient on
Yield_Residual × Treat × Post is significantly positive in the four columns with
Delinquency_60plus and Delinquency_90plus as the dependent variables. However, in columns 1
and 2 with Delinquency_30plus as the dependent variable, the coefficient is insignificant.
In summary, although the loss of power from the use of shorter sample periods yields
insignificant results our results in some of the models with Delinquency_30plus as the dependent
variable, our primary results are largely robust to the use of the two alternative shorter pre-
periods.
8. Conclusion
Many financial market observers believe that the opacity of the assets underlying ABS
prior to the 2007–2009 financial crisis contributed to under-appreciation of the risks of ABS by
investors and to excessively optimistic credit ratings of ABS by credit rating agencies. As an
important part of the post-crisis effort to reform and revive the ABS markets, the asset-level
disclosure requirements of Reg AB II substantially increase the transparency of the assets
underlying auto ABS. We employ a difference-in-differences research design to test for the
impact of asset-level disclosures on the (e)valuation of ABS by investors and credit rating
agencies. We find that asset-level disclosures increase the ability of initial ABS yield spreads to
predict the subsequent performance of the underlying assets, consistent with these disclosures
improving investors’ valuation of ABS. We also find that asset-level disclosures increase the
36
ability of initial credit ratings to predict the subsequent performance of the underlying assets,
consistent with these disclosures improving credit rating agencies’ evaluation of ABS. Since
credit rating agencies did not change their methodologies or reliance on asset-level information
around Regulation AB II, our findings for credit ratings suggest that public asset-level
disclosures either discipline the agencies’ evaluation of ABS or increase their use of ABS price-
related information. We further find that that the improvements in investors’ valuation of ABS
are in part attributable to investors conducting more effective due diligence independent of credit
ratings. However, contrary to a stated primary objective of the SEC for Reg AB II, we find no
evidence that asset-level disclosures reduce the extent to which investors rely on credit ratings.
We examine two mechanisms by which asset-level disclosures might lead to more
accurate (e)valuation of ABS by investors and credit rating agencies. We expect the incremental
value of asset-level disclosures to be greater for ABS deals that involve more risk layering of the
underlying assets and more complex tranching of credit risk. Consistent with these expectations,
we find that our primary findings obtain only in auto ABS deals with above-median risk-layered
loans and above-median number of tranches.
The imposition of Reg AB II’s asset-level disclosure requirements provides numerous
possibilities for future research in accounting and finance. These requirements may influence the
types and quality of the assets securitized, as well as the decisions of parties others than the two
types we examine, such as ABS underwriters, financial regulators, and auditors. For example,
underwriters play important roles in the structuring, terms, and marketing of ABS deals. Like
underwriters of other types of financial assets, ABS underwriters have reputations to develop,
maintain, or lose.
37
References
Acharya, V. V., T. Philippon, M. Richardson, N. Roubini. 2009. A bird’s-eye view: The
financial crisis of 2007–2009: Causes and remedies. Prologue in Restoring Financial
Stability, eds. V. V. Acharya and M. Richardson, John Wiley & Sons: Hoboken, NJ.
Acharya, V., S. Ryan. 2016. Banks’ financial reporting and financial system stability. Journal of
Accounting Research 54: 277–340.
Ally Financial, Ford Motor Credit Company, General Motors Financial Company, etc. 2011.
Comment letter on re-proposal of shelf eligibility conditions for asset-backed securities and
other additional requests for comment (Release. Nos. 33-9244; 34-64968; File No. S7-08-
10), October 13. Available at https://www.sec.gov/comments/s7-08-10/s70810-228.pdf
Altman, E., T. Oncu, M. Richardson, A. Schmeits, and L. White. 2010. Regulation of ratings
agencies. Chapter 15 in Regulating Wall Street: The Dodd-Frank Act and the New
Architecture of Global Finance, eds. Acharya, V. V., T. F. Cooley, M. P. Richardson, and I.
Walter, John Wiley & Sons: Hoboken, NJ.
American Securitization Forum. 2010. Comment letter on asset-backed securities proposed rule
(Release Nos. 33-9117, 34-61858; File No. S7-08-10), August 31. Available at
https://www.sec.gov/comments/s7-08-10/s70810-157.pdf
Ashcraft, A. B., T. Schuermann. 2008. Understanding the securitization of subprime mortgage
credit. Foundations and Trends in Finance 2 (3): 191-309.
Ashcraft, A. B., P. Goldsmith-Pinkham, J. I. Vickery. 2010. MBS ratings and the mortgage credit
boom. Federal Reserve Bank of New York Staff Report No. 449.
Bank of America. 2010. Comment letter on asset-backed securities proposed rule (Release Nos.
33-9117, 34-61858; RIN 3235-AK37; File No. S7-08-10), August 2. Available at
https://www.sec.gov/comments/s7-08-10/s70810-108.pdf
Banerjee, S., J. Davis, N. Gondhi. 2018. When transparency improves, must prices reflect
fundamentals better? Review of Financial Studies 31 (6): 2377-2414.
Beatty, A., S. Liao. 2014. Financial accounting in the banking industry: A review of the
empirical literature. Journal of Accounting and Economics 58: 339-383.
Bernanke, B. 2007. The subprime mortgage market. Speech at the Federal Reserve Bank of
Chicago’s 43rd Annual Conference on Bank Structure and Competition, Chicago, Illinois,
May 17, 2007. Available at
https://www.federalreserve.gov/newsevents/speech/bernanke20070517a.htm
Bhattacharya, N., F. Ecker, P. M. Olsson, K. Schipper. 2012. Direct and mediated associations
among earnings quality, information asymmetry, and the cost of equity. The Accounting
Review 87 (2): 449–482.
Bolton, P., X. Freixas, J. Shapiro. 2012. The credit ratings game. Journal of Finance 67(1): 85–
111.
Bushman, R. 2014. Thoughts on financial accounting and the banking industry. Journal of
Accounting and Economics 58: 384-395.
Bushman, R., Q. Chen, E. Engel, A. Smith. 2004. Financial accounting information,
organizational complexity and corporate governance systems. Journal of Accounting and
Economics 37 (2): 167-201.
Bushman, R., C. Williams. 2012. Bank transparency and the discipline of risk-taking behavior.
Journal of Accounting and Economics 54 (1): 1-18.
38
Bushman, R., C. Williams, 2015. Delayed expected loss recognition and the risk profile of
banks. Journal of Accounting Research 53 (3): 511-554.
Capital One. 2010. Comment letter on proposed release on asset-backed securities (File No. S7-
08-10), August 2. Available at https://www.sec.gov/comments/s7-08-10/s70810-85.pdf
Citigroup Global Markets. 2010. Comment letter on proposed rule for asset-backed securities
(Release Nos. 33-9117; 34-61858; File No. S7-08-10), August 2. Available at
https://www.sec.gov/comments/s7-08-10/s70810-137.pdf
Coval, J. D., J. W. Jurek, E. Stafford. 2009. Economic catastrophe bonds. American Economic
Review 99 (3): 628-66.
Dang, T.V., G. Gorton, B. Holmström, G. Ordoñez. 2017. Banks as secret keepers. American
Economic Review 107 (4): 1005–1029.
Department of the Treasury, Federal Reserve, FDIC, Federal Housing Finance Agency, SEC,
and Department of Housing and Urban Development. 2014. Credit risk retention, Final rule.
Available at https://www.gpo.gov/fdsys/pkg/FR-2014-12-24/pdf/2014-29256.pdf
Duyn, A., J. Chung. 2008. S&P discloses errors in product rating models. Financial Times, June
14.
Efing, M., H. Hau. 2015. Structured debt ratings: Evidence on conflicts of interest. Journal of
Financial Economics 116: 46-60.
Ertan, A., M. Loumioti, R. Wittenberg-Moerman. 2017. Enhancing loan quality through
transparency: Evidence from the European Central Bank loan level reporting initiative.
Journal of Accounting Research 55 (4): 877-918.
Federal Deposit Insurance Corporation (FDIC). 2017. Crisis and response: An FDIC history,
2008–2013. Available at https://www.fdic.gov/bank/historical/crisis/chap1.pdf
Financial Crisis Inquiry Commission. 2011. The financial crisis inquiry report: Final report of
the national commission on the causes of the financial and economic crisis in the United
States. New York, NY: Public Affairs.
Firth, M., K. Wang, S.M.L. Wong. 2014. Corporate transparency and the impact of investor
sentiment on stock prices. Management Science 61 (7): 1630-1647.
Fitch, 2017. U.S. auto loan ABS rating criteria. Available at
https://www.fitchratings.com/site/re/10021140
Francis, J. R., S. Huang, I. K. Khurana, R. Pereira. 2009. Does corporate transparency contribute
to efficient resource allocation? Journal of Accounting Research 47 (4): 943-989.
Frederickson, J.E., L. Zolotoy. 2016. Competing earnings announcements: Which announcement
do investors process first? The Accounting Review 91(2): 441–462.
Furfine, C. 2014. Complexity and loan performance: Evidence from the securitization of
commercial mortgages. Review of Corporate Finance Studies 2 (2): 154-187.
Ghent, A. C., W. N. Torous, R. Valkanov. 2017. Complexity in structured finance. The Review of
Economic Studies, forthcoming.
Goldstein, I., Y. Leitner. 2018. Stress tests and information disclosure. Journal of Economic
Theory 177: 34-69.
Goldstein, I., H. Sapra, 2013. Should banks’ stress tests results be made public? An analysis of
the costs and benefits. Foundations and Trends in Finance 8: 1–53.
Goldstein, I., L. Yang, 2017. Information disclosure in financial markets. Annual Review of
Financial Economics 9: 101-125.
Gorton, G. B., 2010. Slapped by the invisible hand: The panic of 2007. Oxford University Press:
New York.
39
Granja, J. 2018. Disclosure regulation in the commercial banking industry: Lessons from the
national banking era. Journal of Accounting Research 56 (1): 173-216.
He, J., J. Qian, P. E. Strahan. 2012. Are all ratings created equal? The impact of issuer size on
the pricing of mortgage-backed securities. Journal of Finance 67 (6): 2097–2137.
He, J., J. Qian, P. E. Strahan. 2016. Does the market understand rating shopping? Predicting
MBS losses with initial yields. Review of Financial Studies 29 (2): 457-485.
Healy, P. M., G. Serafeim. 2016. An analysis of firms’ self-reported anticorruption efforts. The
Accounting Review 91 (2): 489–511.
International Law Office. 2010. Dodd-Frank Act: Elimination of rating agency protections.
August 24. Available at https://www.internationallawoffice.com/Newsletters/Capital-
Markets/USA/Weil-Gotshal-Manges-LLP/Dodd-Frank-Act-elimination-of-rating-agency-
protections
Jiang, J., I. Y. Wang, K. P. Wang. 2018. Revolving rating analysts and ratings of mortgage-
backed and asset-backed securities: Evidence from LinkedIn. Management Science,
forthcoming.
Jones, S., G. Tett, P. J. Davies. 2008. Moody’s error gave top ratings to debt products. Financial
Times, May 20.
J.P. Morgan, 2010. Comment letter on asset-backed securities (Release Nos. 33-9117 and 34-
61858; File No. S7-08-10), August 2. Available at https://www.sec.gov/comments/s7-08-
10/s70810-110.pdf
Kleymenova, A. 2016. Consequences of mandated bank liquidity disclosures. Working Paper,
University of Chicago.
Lang, M., K. V. Lins, M. Maffett. 2012. Transparency, liquidity, and valuation: International
evidence on when transparency matters most. Journal of Accounting Research 50 (3): 729-
774.
Lee, H. S., A. L. Nagy, A. B. Zimmerman. 2018. Audit partner assignments and audit quality in
the United States. The Accounting Review, forthcoming.
Moody’s. 2007. Moody’s revised U.S. mortgage loan-by-loan data fields. Special Report.
Moody’s. 2010. Comment letter on proposed amendments to Regulation AB (File No. S7-08-
10). Available at https://www.sec.gov/comments/s7-08-10/s70810-155.pdf
Moody’s. 2014. Comment letter on disclosure of asset-level data (File No. S7-08-10). Available
at https://www.sec.gov/comments/s7-08-10/s70810-300.pdf
Office of the Comptroller of the Currency (OCC). 2018. Comptroller’s handbook: Retail lending,
Version 1.1, May 23, 2018. Available at https://www.occ.treas.gov/publications/publications-
by-type/comptrollers-handbook/retail-lending/pub-ch-retail-lending.pdf
Ohlson, J.F., Lane, B.J., Mueller, R.O., Goodman, A.L., Fabens, A. 2010. Repeal of credit
ratings agency exemption from regulation FD. Gibson, Dunn & Crutcher LLP. Available at
https://www.gibsondunn.com/repeal-of-credit-ratings-agency-exemption-from-regulation-fd/
Richardson, M., J. Ronen, M. Subrahmanyam, 2011. Securitization reform. Chapter 16 in
Regulating Wall Street: The Dodd-Frank Act and the New Architecture of Global Finance,
eds. Acharya, V. V., T. F. Cooley, M. P. Richardson, and I. Walter, John Wiley & Sons:
Hoboken, NJ.
Ryan, S. 2007. Financial Instruments and Institutions: Accounting and Disclosure Rules, 2nd
edition, John Wiley & Sons: Hoboken, NJ.
Ryan, S. 2018. Recent research on banks’ financial reporting and financial stability. Annual
Review of Financial Economics, forthcoming.
40
Securities and Exchange Commission. 2000. Selective disclosure and insider trading. Available
at https://www.sec.gov/rules/final/33-7881.htm
Securities and Exchange Commission. 2010. Removal from Regulation FD of the exemption for
credit rating agencies. Federal Register 75 (191). Available at
https://www.sec.gov/rules/final/2010/33-9146fr.pdf
Securities and Exchange Commission. 2014. Asset-backed securities disclosure and registration,
Final Rule. 17 CFR Parts 229, 230, 232, et al. Federal Register 79 (185). Available at
https://www.gpo.gov/fdsys/pkg/FR-2014-09-24/pdf/2014-21375.pdf
Schmidt, B., and H. Zhang. 2018. Periodic disclosure of asset-backed securities and bond
trading. Working paper, Ohio State University.
Scott, K. E., J. B. Taylor. 2009. Why toxic assets are so hard to clean up: Securitization was
maddeningly complex: Mandated transparency is the only solution. Wall Street Journal, July
21.
White, L. J. 2010. Markets: The credit rating agencies. Journal of Economic Perspectives 24 (2):
211-226.
Yu, F. 2005. Accounting transparency and the term structure of credit spreads. Journal of
Financial Economics 75 (1): 53-84.
41
Appendix A: Examples of Disclosures for Auto ABS Deals (the Treatment Sample) before Versus
after the Effective Date of Reg AB II’s Asset-Level Disclosure Requirements
Part I: Example of Pool-Level Disclosures for Auto ABS before the Effective Date of Reg AB II’s
Asset-Level Disclosure Requirements
Capital Auto Receivables Asset Trust 2015-1
This auto ABS deal was issued in 2015, the year prior to the effective date of Reg AB II’s asset-level
disclosure requirements.27 The prospectus (Form 424-B5) for this deal includes the following pool-level
summary information:
In addition, the prospectus includes distributions for the underlying asset pool of buckets of a few
individual risk dimensions such as interest rate, loan-to-value ratio, borrower FICO score, and geographic
location.
27 https://www.sec.gov/Archives/edgar/data/1630924/000119312515020446/0001193125-15-020446-index.htm
42
Part II: Example of Asset-Level Disclosures for Auto ABS After Reg AB II’s Asset-Level Disclosure
Requirements Became Effective
Toyota Auto Receivables Owner Trust 2017-A
This auto ABS deal was issued in 2017, the year after the effective date of Reg AB II’s asset-level
disclosure requirements.28 Similar to auto loan deals issued before this date, the prospectus (Form 424-
B5) filed on March 9, 2017 contains pool-level summary information and distributions for the underlying
asset pool of buckets of a few individual risk dimensions:
28 https://www.sec.gov/Archives/edgar/data/1694919/000092963817000308/0000929638-17-000308-index.htm
43
In addition, the issuer also filed Form ABS-EE on February 27, 2017. This filing contains 73 variables for
each loan in the asset pool, including many variables that were not included in the prospectus before Reg
AB II. The following excerpt from the Form ABS-EE filing indicates some of these variables.
Note: the original file is in XML format; the above excerpt is in Excel format converted using
www.Finsight.com.
44
Appendix B: Examples of Asset-Level Disclosure for CMBS (Part of the Control Sample) before
Versus after the Effective Date of Reg AB II’s Asset-Level Disclosure Requirements
Part I: Example of Asset-Level Disclosure for CMBS before the Effective Date of Reg AB II’s
Asset-Level Disclosure Requirements
CSAIL 2015-C1 Commercial Mortgage Trust
The CMBS deal was issued in 2015, the year before the adoption of Reg AB II’s asset-level disclosure
requirements. The prospectus (Form 424-B5) for this deal contains 20 pages of detailed loan-level
information, as well as pictures of some of the mortgaged buildings.29 The following excerpt indicates
some of this information.
Part II: Example of Asset-Level Disclosure for CMBS after the Effective Date of Reg AB II’s Asset-
Level Disclosure Requirements
Citigroup Commercial Mortgage Trust 2017-C4
The CMBS deal was issued in 2017, the year after the effective date of Reg AB II’s asset-level disclosure
requirements.30 The issuer filed the final prospectus (Form 424-B2) with the SEC on October 31, 2017
and Form ABS-EE with asset-level disclosures on October 16, 2017. The following excerpt from the
ABS-EE filing indicates some of these disclosures.
29 https://www.sec.gov/Archives/edgar/data/1634172/000153949715000370/0001539497-15-000370-index.htm 30 https://www.sec.gov/Archives/edgar/data/1258361/000153949717001896/0001539497-17-001896-index.htm
45
Note: the original file is in XML format; the above excerpt is in Excel format converted using
www.Finsight.com.
The prospectus for this deal also provides similar loan-level data to that provided in prospectuses prior to
the effective date of Reg AB II’s asset-level disclosure requirements. The following excerpt from this
prospectus indicates some of this information.
We carefully compared the information in the prospectus pre-Reg AB II and the prospectus and Form
ABS-EE post-Reg AB II. We found that the prospectus in the post-Reg AB II period includes the same
variables as the prospectus prior to Reg AB II. Moreover, we found that the prospectus contains nearly all
of the variables included in the ABS-EE filing as well as a number of variables not included in the ABS-
EE filing. Thus, we conclude that Reg AB II does not significantly increase the transparency of the assets
underlying CMBS.
46
Appendix C: Variable Definitions
Dependent Variables
• Delinquency_30plus: the percentage of the underlying assets delinquent for 30 days or more.
• Delinquency_60plus: the percentage of the underlying assets delinquent for 60 days or more.
• Delinquency_90plus: the percentage of the underlying assets delinquent for 90 days or more.
• Yield: the initial (at issuance) tranche yield spread (in percentage points). For tranches with
floating coupon rates, we measure Yield as the specified fixed markup over the reference rate
(e.g., one-month LIBOR). For tranches with fixed coupon rates, we measure Yield as the
initial coupon rate of the tranche less the yield on a Treasury security whose maturity is
similar to the disclosed Average Life of the tranche (He et al. 2016).
Independent Variables of Interest
• Yield: the intial tranche yield spread, as defined above.
• Rating: the average of the initial credit ratings of the tranche provided by S&P, Moody’s, and
Fitch. Higher numerical values correspond to higher credit risk.
• Yield_Residual: the portion of the initial tranche yield spread that is not explained by the
initial tranche credit rating. Defined as the residual from an OLS regression for all sample
tranches of Yield on Rating.
• Risk Layering: the proportion of the underlying assets in an auto ABS deal in the post Reg
AB II period that exhibits three or more of the following five high-risk indicators: bottom
quartile borrower FICO score, highest quartile loan-to-value ratio, lowest quartile income,
actual payment more than 90 percent below the scheduled payment, and no documentation of
borrower income or employment. See Appendix D for further details of this definition.
• Treat: an indicator variable equal to one for SEC-registered (i.e., publicly offered) auto ABS
deals and zero for other deals.
• Post: an indicator variable equal to one for ABS deals issued on or after November 23, 2016
(the effective date of Reg AB II’s asset-level disclosure requirements), and zero otherwise.
• PostFalsifi: an indicator variable for after the pseudo effective date of asset-level disclosure
requirements in the falsification test. The variable equals one for ABS deals issued from June
1, 2015 to November 22, 2016, and zero for deals issued before June 1, 2015.
Control Variables
• Number of tranches: number of tranches in an ABS deal.
• Tranche size: the natural log of the initial principal amount of a tranche.
• Average Life: the disclosed expected amount of time (in years) for a tranche’s principal to be
repaid.
• Subordination: the principal amount of the tranches in an ABS deal with credit ratings worse
than the credit rating of the tranche, divided by the total principal amount of the tranches in
the deal.
• Floating: an indicator variable equal to one for a tranche with a floating coupon rate, and
zero for other tranches.
• One Rating: an indicator variable equal to one if a tranche is rated by only one CRA, and
zero if the tranche is rated by more than one CRA.
47
Appendix D: Definition of Risk Layering
Risk Layering is a continuous variable that measures the proportion of the underlying assets in an
auto ABS deal in the post-Reg AB II period that exhibit at least three of the following five high
credit risk dimensions: low borrower FICO score, high loan-to-value ratio, low borrower income,
actual payment below scheduled payment, and no documented income or employment. The
calculation of Risk Layering follows four steps.
Step 1: Define assets with high credit risk on individual dimensions
The indicator variable D_FICO equals one for an underlying asset with borrower FICO score in
the bottom quartile of the FICO score distribution (648 or below) for the 76 publicly offered auto
ABS deals issued in the US between November 23, 2016 and May 31, 2018, and zero otherwise.
The indicator variable D_LTV equals one for an underlying asset with loan-to-value ratio (LTV)
in the top quartile of the LTV distribution (above 0.96 to 1.25 depending on the vehicle value
source) for the 76 auto ABS deals, and zero otherwise. The indicator variable D_Income equals
one for an underlying asset with the borrower’s monthly income is in the bottom quartile of the
income distribution for the 76 auto deals, and zero otherwise. The indicator variable D_ATS
equals one for an underlying asset with actual-payment-to-scheduled-payment ratio (ATS) below
0.9 (i.e., the actual payment is meaningfully smaller than the scheduled payment), and zero
otherwise. The indicator variable D_LowDoc equals one for an underlying asset if the borrower
has no documented income or employment, and zero otherwise.
Step 2: Determine the number of high-credit-risk dimensions for an asset
For each underlying asset in a deal, Sum_Layers = D_FICO + D_LTV + D_ Income + D_ATS +
D_LowDoc.
Step 3: Determine if an asset is risk-layered
For each underlying asset in a deal, D_MultipleLayers equals 1 if Sum_Layers equals 3 or above,
and zero otherwise.
Step 4: Determine the proportion of risk-layered assets in the pool
Risk Layering equals the weighted average of D_Multiple_Layers for the underlying assets in a
deal. The weights are based on the unamortized balance of the assets. Specifically, for a deal
with n underlying assets,
𝑅𝑖𝑠𝑘 𝐿𝑎𝑦𝑒𝑟𝑖𝑛𝑔 = ∑ 𝐷_𝑀𝑢𝑙𝑡𝑖𝑝𝑙𝑒𝐿𝑎𝑦𝑒𝑟𝑠𝑖 ×𝐿𝑜𝑎𝑛 𝑆𝑖𝑧𝑒𝑖
𝐷𝑒𝑎𝑙 𝑠𝑖𝑧𝑒 × 100𝑛
𝑖=1 .
48
Table 1: Descriptive Statistics
Panel A: Sample Selection
No. of
tranches
No. of
deals
ABS deals issued over January 2013 – May 2018 15,170 2,353
Less:
Privately placed (144A) ABS deals 9,127 1,412
Federally-insured student loan ABS deals 78 29
Missing information on credit ratings, delinquency, or other variables 1,942 228
Equity tranches (with necessary information) 39 0
Total 3,984 684
Panel B: Composition of Sample Tranches by Collateral Type and Year of Issuance
2013 2014 2015 2016 2017 2018 Total Pre-Period Post-Period Total
Auto 257 280 307 258 235 31 1,368 1,102 266 1,368
Commercial Mortgage 361 497 523 475 464 11 2,331 1,788 543 2,331
Credit card 35 33 26 26 29 0 149 119 30 149
Equipment 37 34 30 20 15 0 136 121 15 136
Total 690 844 886 779 743 42 3,984 3,130 854 3,984
Panel C: Descriptive Statistics
Variable N Mean STD p25 Median p75
Dependent variables:
Delinquency_30plus (%) 3,984 0.84 1.84 0 0 0.78
Delinquency_60plus (%) 3,984 0.24 0.59 0 0 0.16
Delinquency_90plus (%) 3,984 0.10 0.31 0 0 0.04
Independent variables of interest:
Yield (%) 3,984 1.06 0.74 0.55 1.12 1.56
Rating 3,984 2.45 2.49 1 1 3
Yield_Residual (%) 3,984 0.00 0.67 -0.40 0.04 0.49
Treat 3,984 0.34 0.47 0 0 1
Post 3,984 0.21 0.41 0 0 0
Control variables:
Number of tranches 3,984 14.20 7.20 7 17 20
Tranche size 3,984 18.40 1.01 17.66 18.35 19.20
Average Life 3,984 5.88 3.37 2.75 4.91 9.85
Subordination 3,984 0.29 0.17 0.19 0.26 0.30
Floating 3,984 0.06 0.24 0 0 0
One Rating 3,984 0.21 0.41 0 0 0
Panel A reports the sample selection procedure. Panel B reports the composition of the 3,984 overall sample
tranches issued from January 1, 2013 to January 31, 2018 by asset type and year of issuance, and also by asset type
and for each of the period before the effective date of Reg AB II’s asset-level disclosure requirements (the pre-
period), the period after that date (the post-period), and the overall sample period. Panel C reports descriptive
statistics for the variables for the overall sample tranches. All variables are defined in Appendix C.
49
Table 2: Impact of Asset-Level Disclosures on Investors’ Valuation of ABS
Delinquency_30plus Delinquency_60plus Delinquency_90plus
(1) (2) (3)
Yield × Treat × Post 0.542** 0.334*** 0.353***
(0.048) (<0.001) (<0.001)
Yield 0.058*** 0.020*** 0.010**
(0.001) (0.001) (0.012)
Yield × Treat 0.343*** 0.023 -0.040**
(0.006) (0.481) (0.045)
Yield × Post -0.067* -0.016 -0.007
(0.051) (0.188) (0.357)
Treat × Post -0.246 -0.057 -0.051
(0.179) (0.374) (0.240)
Number of tranches -0.018*** -0.020*** -0.014***
(0.009) (<0.001) (<0.001)
Tranche size 0.092*** 0.039*** 0.022***
(<0.001) (<0.001) (<0.001)
Average Life -0.015*** -0.011*** -0.008***
(0.009) (<0.001) (<0.001)
Subordination 0.309* -0.080* -0.077***
(0.052) (0.057) (0.002)
Floating 0.283*** 0.048 0.011
(<0.001) (0.160) (0.645)
One Rating 0.117* 0.021 0.011
(0.072) (0.252) (0.258)
Constant 0.096 -0.272** -0.235***
(0.827) (0.018) (0.001)
Issuer FEs Y Y Y
Issuing year-quarter FEs Y Y Y
Observations 3,984 3,984 3,984
Adjusted R-squared 0.77 0.83 0.76
This table reports OLS estimations of equation (1), which examines the effect of asset-level disclosures on investors’
valuation of ABS, using the overall sample of 3,984 ABS tranches issued from January 1, 2013 to January 31, 2018.
The dependent variables in columns 1, 2, and 3 are the proportions of the underlying assets that are 30 days or more
delinquent, 60 days or more delinquent, and 90 days or more delinquent, respectively, at 120 days after ABS
issuance. All variables are defined in Appendix C. Statistical significance is assessed using two-tailed t-tests with
standard errors calculated clustering observations by deal. p-values are reported in parentheses below coefficient
estimates. Coefficients that are more significant than common thresholds are designated as follows: *** p<0.01, **
p<0.05, and * p<0.1.
50
Table 3: Impact of Asset-Level Disclosures on Credit Rating Agencies’ Evaluation of ABS
Delinquency_30plus Delinquency_60plus Delinquency_90plus
(1) (2) (3)
Rating × Treat × Post 0.068** 0.041*** 0.040***
(0.038) (0.002) (<0.001)
Rating 0.023** 0.005* 0.003*
(0.025) (0.068) (0.089)
Rating × Treat 0.024** 0.001 -0.004
(0.041) (0.801) (0.139)
Rating × Post -0.007 -0.002 -0.001
(0.408) (0.535) (0.614)
Treat × Post -0.081 0.060 0.085*
(0.709) (0.376) (0.066)
Number of tranches -0.019*** -0.020*** -0.014***
(0.005) (<0.001) (<0.001)
Tranche size 0.093*** 0.040*** 0.023***
(<0.001) (<0.001) (<0.001)
Average Life -0.016*** -0.011*** -0.008***
(<0.001) (<0.001) (<0.001)
Subordination 0.343* -0.069 -0.065**
(0.086) (0.165) (0.016)
Floating 0.169*** 0.020 -0.003
(0.005) (0.507) (0.869)
One Rating 0.081 0.015 0.009
(0.266) (0.460) (0.389)
Constant 0.279 -0.262** -0.283***
(0.510) (0.021) (<0.001)
Issuer FEs Y Y Y
Issuing year-quarter FEs Y Y Y
Observations 3,984 3,984 3,984
Adjusted R-squared 0.77 0.83 0.75
This table reports OLS estimations of equation (2), which examines the effect of asset-level disclosures on credit
rating agencies’ evaluation of ABS, using the overall sample of 3,984 ABS tranches issued from January 1, 2013 to
January 31, 2018. The dependent variables in columns 1, 2, and 3 are the proportions of the underlying assets that
are 30 days or more delinquent, 60 days or more delinquent, and 90 days or more delinquent, respectively, at 120
days after ABS issuance. All variables are defined in Appendix C. Statistical significance is assessed using two-
tailed t-tests with standard errors calculated clustering observations by deal. p-values are reported in parentheses
below coefficient estimates. Coefficients that are more significant than common thresholds are designated as
follows: *** p<0.01, ** p<0.05, and * p<0.1.
51
Table 4: Impact of Asset-Level Disclosures on Investors’ Independent Due Diligence
Delinquency_30plus Delinquency_60plus Delinquency_90plus
(1) (2) (3)
Yield_Residual×Treat×Post 0.648 0.512** 0.670***
(0.353) (0.012) (<0.001)
Yield_Residual 0.044*** 0.019*** 0.010***
(0.002) (0.002) (0.008)
Yield_Residual × Treat 0.611* 0.018 -0.105***
(0.098) (0.829) (0.010)
Yield_Residual × Post -0.060** -0.015 -0.008
(0.033) (0.151) (0.258)
Rating 0.029*** 0.006** 0.003
(0.004) (0.024) (0.107)
Rating × Treat 0.005 0.002 0.001
(0.746) (0.685) (0.652)
Rating × Post -0.009 -0.002 -0.001
(0.366) (0.573) (0.651)
Rating × Treat × Post 0.069** 0.029*** 0.020**
(0.015) (0.008) (0.018)
Treat × Post 0.255 0.306** 0.404***
(0.590) (0.027) (<0.001)
Number of tranches -0.019*** -0.020*** -0.014***
(0.005) (<0.001) (<0.001)
Tranche size 0.093*** 0.041*** 0.023***
(<0.001) (<0.001) (<0.001)
Average Life -0.017*** -0.012*** -0.008***
(<0.001) (<0.001) (<0.001)
Subordination 0.467** -0.056 -0.072***
(0.015) (0.246) (0.009)
Floating 0.317*** 0.054 0.016
(<0.001) (0.151) (0.524)
One Rating 0.077 0.015 0.009
(0.288) (0.478) (0.393)
Constant 0.493 -0.271** -0.317***
(0.284) (0.034) (<0.001)
Issuer FEs Y Y Y
Issuing year-quarter FEs Y Y Y
Observations 3,984 3,984 3,984
Adjusted R-squared 0.77 0.83 0.76
This table reports OLS estimations of equation (3), which examines the effect of asset-level disclosures on investors’
due diligence independent of credit ratings, using the sample of 3,984 ABS tranches issued from January 1, 2013 to
January 31, 2018. The dependent variables in columns 1, 2, and 3 are the proportions of the underlying assets that
are 30 days or more delinquent, 60 days or more delinquent, and 90 days or more delinquent, respectively, at 120
days after ABS issuance. All variables are defined in Appendix C. Statistical significance is assessed using two-
tailed t-tests with standard errors calculated clustering observations by deal. p-values are reported in parentheses
below coefficient estimates. Coefficients that are more significant than common thresholds are designated as
follows: *** p<0.01, ** p<0.05, and * p<0.1.
52
Table 5: Impact of Asset-Level Disclosures on Investors’ Reliance on Credit Ratings
Yield
(1)
Rating × Treat × Post 0.065***
(0.001)
Rating 0.087***
(<0.001)
Rating × Treat 0.004
(0.662)
Rating × Post -0.080***
(<0.001)
Treat × Post 0.048
(0.350)
Number of tranches -0.012***
(0.002)
Tranche size -0.175***
(<0.001)
Average Life 0.076***
(<0.001)
Subordination 0.224***
(0.007)
Floating -0.073**
(0.032)
One Rating 0.017
(0.618)
Constant 3.555***
(<0.001)
Issuer FEs Y
Issuing year-quarter FEs Y
Observations 4,589
Adjusted R-squared 0.48
This table reports the OLS estimation of equation (4), which examines the effect of asset-level disclosures on
investors’ reliance on credit ratings, using the sample of 4,589 ABS tranches issued from January 1, 2013 to May
31, 2018. The sample size is about 15% larger than that in Tables 2-4, because it includes 605 ABS tranches with
missing delinquency data. The dependent variable, Yield, is the initial (at issuance) tranche yield spread. All
variables are defined in Appendix C. Statistical significance is assessed using two-tailed t-tests with standard errors
calculated clustering observations by deal. p-values are reported in parentheses below coefficient estimates.
Coefficients that are more significant than common thresholds are designated as follows: *** p<0.01, ** p<0.05,
and * p<0.1.
53
Table 6: Role of Risk Layering Information in Asset-Level Disclosures
Panel A: Does Risk-Layering Explain Initial Yield Spreads and Credit Ratings?
Yield Rating
(1) (2) (3) (4)
Risk Layering
0.025*** 0.100***
(<0.001) (0.009)
FICO -0.002*** -0.001* -0.013*** -0.009**
(<0.001) (0.074) (<0.001) (0.013)
Loan-to-Value 2.088*** 1.266** 8.858** 5.761*
(0.003) (0.019) (0.019) (0.064)
Log_Income 0.010 -0.032 -0.097 -0.268
(0.887) (0.557) (0.810) (0.456)
Actual-payment-to-scheduled-payment ratio 0.003 0.338 0.846 2.278
(0.992) (0.176) (0.529) (0.107)
Low documentation 4.866*** 4.198*** 29.228*** 27.034***
(<0.001) (<0.001) (<0.001) (<0.001)
Tranche size -0.108*** -0.100*** -0.880*** -0.839***
(<0.001) (<0.001) (<0.001) (<0.001)
Average life 0.032 0.037 0.252 0.276
(0.191) (0.170) (0.137) (0.116)
Subordination -1.737*** -1.878*** -12.596*** -13.306***
(<0.001) (<0.001) (<0.001) (<0.001)
Floating -0.358*** -0.339*** -0.407 -0.304
(<0.001) (<0.001) (0.102) (0.226)
One Rating 0.105 0.163 0.174 0.408
(0.437) (0.170) (0.794) (0.462)
Auto loans -0.384*** -0.321*** -0.818 -0.601
(0.001) (0.001) (0.148) (0.221)
Constant 2.642** 2.463*** 22.279*** 21.294***
(0.011) (0.003) (<0.001) (<0.001)
Observations 353 353 379 379
Adjusted R-squared 0.67 0.71 0.65 0.67
Standard errors clustered by issuer Y Y Y Y
Panel B: Does Risk Layering Explain Subsequent Loan Performance?
Delinquency_30plus Delinquency_60plus Delinquency_90plus
(1) (2) (3) (4) (5) (6)
Risk Layering
0.435*** 0.206***
0.122***
(<0.001) (<0.001)
(<0.001)
FICO -0.030*** -0.007 -0.010*** 0.000 -0.005*** 0.001
(<0.001) (0.292) (<0.001) (0.845) (<0.001) (0.357)
Loan-to-Value 8.478 -1.345 2.696 -1.946* 1.242 -1.502
(0.145) (0.504) (0.229) (0.074) (0.355) (0.140)
Log_Income 0.967 -0.388 0.541 -0.099 0.351 -0.027
(0.220) (0.173) (0.164) (0.455) (0.156) (0.754)
Actual-payment-to-scheduled-payment ratio -7.833** 1.088 -4.160** 0.056 -2.679** -0.187
(0.017) (0.522) (0.013) (0.943) (0.017) (0.709)
Low documentation 10.067 -8.000 6.415 -2.124 3.795 -1.251
(0.342) (0.298) (0.174) (0.564) (0.268) (0.677)
Deal size 0.456 0.298 0.206 0.131 0.124 0.080
(0.528) (0.350) (0.463) (0.354) (0.443) (0.467)
Auto loans 0.273 -0.041 0.333 0.185 0.253 0.166
(0.751) (0.919) (0.372) (0.358) (0.289) (0.267)
Constant 3.462 3.224 -0.484 -0.596 -0.776 -0.842
(0.771) (0.585) (0.925) (0.840) (0.817) (0.722)
Observations 57 57 57 57 57 57
Adjusted R-squared 0.83 0.92 0.79 0.92 0.72 0.85
Standard errors clustered by issuer Y Y Y Y Y Y
Panel A (B) reports OLS estimations examining whether risk layering explains initial ABS tranche yield spreads and credit ratings (the subsequent performance
of the underlying assets). The explanatory variable of interest in both panels, Risk Layering, captures the proportion of underlying assets in auto ABS (treatment)
deals after the effective date of Reg AB II’s asset-level disclosure requirement that exhibit at least three of the following five high credit risk indicators: bottom
quartile borrower FICO score, top quartile loan-to-value ratio, bottom quartile borrower income, actual payment less than 90 percent of scheduled payment, and
no documentation of borrower income or employment. Details of the calculation of Risk Layering are provided in Appendix D.
In Panel A, the dependent variable is initial tranche yield spreads in columns 1 and 2 and initial tranche credit ratings in columns 3 and 4. The regressions
reported in columns 1 and 3 include only the control variables, while those reported in columns 3 and 4 include both Risk Layering and the control variables. The
sample in columns 1 and 2 (columns 3 and 4) consists of 353 (379) ABS tranches with non-missing data for 76 treatment ABS deals issued from November 23,
55
2016 to May 31, 2018; the additional 26 sample tranches in columns 3 and 4 have missing initial yield spreads and hence are excluded from the sample in
columns 1 and 2. In Panel B, the dependent variable in columns 1 and 2 (3 and 4) [5 and 6] is the proportion of the underlying assets that are 30 days or more
delinquent (60 days or more delinquent) [90 days or more delinquent] at 120 days after ABS issuance. The regressions reported in columns 1, 3, and 5 include
only the control variables, while those reported in columns 2, 4, and 6 include both Risk Layering and the control variables. The sample consists of 57 treatment
ABS deals issued from November 23, 2016 to January 31, 2018 with non-missing delinquency data.
Statistical significance is assessed using two-tailed t-tests with standard errors calculated clustering observations by deal. p-values are reported in parentheses
below coefficient estimates. Coefficients that are more significant than common thresholds are designated as follows: *** p<0.01, ** p<0.05, and * p<0.
Variables are defined in Appendix C except for the following. FICO is the weighted average borrower FICO score of the auto loans or leases in a treatment deal,
using initial loan or lease amount as the weights. Loan-to-Value is the weighted average loan-to-value ratio of the auto loans or leases in a treatment deal, using
initial loan or lease amount as the weights. Log_Income is the natural log of the weighted average borrower income in a treatment deal, using the auto loan or
lease amount as the weights. Actual-payment-to-scheduled-payment Ratio is the weighted average actual-payment-to-scheduled-payment ratio of the auto loan
and leases in a treatment deal, using initial loan or lease amount as the weight. Low documentation is the weighted average of D_LowDoc for the auto loans and
leases in a treatment deal, using loan or lease amount as the weight; D_LowDoc is an indicator variable that equals one if there is no documentation of borrower
income or employment, and zero otherwise. Auto Loans is an indicator variable that equals one for ABS collateralized by auto loans, and zero for ABS
collateralized by auto leases.
56
Table 7: Cross-sectional Analyses Partitioning on Risk Layering
Panel A: Investor Valuation Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
High Layering Low Layering High Layering Low Layering High Layering Low Layering
(1) (2) (3) (4) (5) (6)
Yield × Treat × Post 0.542*** 0.171 0.292*** 0.087 0.314*** 0.063
(0.008) (0.115) (<0.001) (0.178) (<0.001) (0.212)
Coefficient difference 0.371* 0.205** 0.251***
(0.099) (0.032) (0.003)
Controls Y Y Y Y Y Y
Observations 3,355 3,120 3,355 3,120 3,355 3,120
Adjusted R-squared 0.81 0.53 0.86 0.78 0.78 0.75
Panel B: Credit Rating Quality Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
High Layering Low Layering High Layering Low Layering High Layering Low Layering
(1) (2) (3) (4) (5) (6)
Rating × Treat × Post 0.069*** 0.022 0.035*** 0.011 0.031*** 0.006
(0.004) (0.103) (0.003) (0.141) (0.003) (0.243)
Coefficient difference 0.047* 0.024* 0.025**
(0.081) (0.063) (0.022)
Controls Y Y Y Y Y Y
Observations 3,355 3,120 3,355 3,120 3,355 3,120
Adjusted R-squared 0.81 0.53 0.85 0.78 0.77 0.75
57
Panel C: Investor Due Diligence Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
High Layering Low Layering High Layering Low Layering High Layering Low Layering
(1) (2) (3) (4) (5) (6)
Yield_Residual × Treat × Post 0.544 0.109 0.490* 0.072 0.741*** 0.083
(0.526) (0.532) (0.083) (0.454) (0.001) (0.284)
Coefficient difference 0.435 0.418* 0.658***
(0.521) (0.079) (<.0001)
Controls Y Y Y Y Y Y
Observations 3,355 3,120 3,355 3,120 3,355 3,120
Adjusted R-squared 0.81 0.53 0.86 0.78 0.78 0.75
This table reports the results of cross-sectional analyses of investor valuation (Panel A), credit rating quality (Panel B), and investor due diligence (Panel C)
partitioning the treatment (auto ABS) deals issued after the effective date of Reg AB II’s asset-level disclosure requirements into above- and below-median Risk
Layering. Since we observe very stable levels of Risk Layering across deals for the same issuer after this effective date, we partition the treatment deals issued
prior to this date into above- and below-median Risk Layering based on the average level of Risk Layering for the same issuer’s deals for after the date. The
sample in the “High Layering” (“Low Layering”) columns consists of the treatment sample with above-median (below-median) Risk Layering plus the control
sample. The dependent variable in columns 1 and 2 (3 and 4) [5 and 6] is the proportion of underlying assets that is 30 (60) [90] or more days delinquent at 120
days after security issuance. All models include issuer and issuing year-quarter fixed effects. Statistical significance is assessed using two-tailed t-tests with
standard errors calculated clustering observations by deal. p-values are reported in parentheses below the estimates of (cross-sample differences in) coefficients.
Coefficients that are more significant than common thresholds are designated as follows: *** p<0.01, ** p<0.05, and * p<0.1. All variables are defined in
Appendix C, and details about the construction of Risk Layering are provided in Appendix D.
58
Table 8: Cross-sectional Analyses Partitioning on the Complexity of Credit Risk Tranching
Panel A: Investor Valuation Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
More complex Less complex More complex Less complex More complex Less complex
(1) (2) (3) (4) (5) (6)
Yield × Treat × Post 0.516** 0.137 0.261*** 0.094 0.277*** 0.090
(0.023) (0.681) (<0.001) (0.465) (<0.001) (0.341)
Coefficient difference 0.379 0.167 0.187
(0.341) (0.251) (0.087)
Controls Y Y Y Y Y Y
Observations 3,533 3,067 3,533 3,067 3,533 3,067
Adjusted R-squared 0.80 0.56 0.85 0.79 0.78 0.76
Panel B: Credit Rating Quality Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
More complex Less complex More complex Less complex More complex Less complex
(1) (2) (3) (4) (5) (6)
Rating × Treat × Post 0.065*** 0.023 0.027*** 0.016 0.024*** 0.010
(0.008) (0.605) (0.007) (0.489) (0.004) (0.513)
Coefficient difference 0.042 0.011 0.014
(0.387) (0.633) (0.402)
Controls Y Y Y Y Y Y
Observations 3,533 3,067 3,533 3,067 3,533 3,067
Adjusted R-squared 0.79 0.56 0.84 0.79 0.77 0.76
59
Panel C: Investor Due Diligence Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
More complex Less complex More complex Less complex More complex Less complex
(1) (2) (3) (4) (5) (6)
Yield_Residual × Treat × Post 0.650 0.181 0.510** 0.135 0.705*** 0.126
(0.411) (0.605) (0.034) (0.311) (<0.001) (0.200)
Coefficient difference 0.469 0.375 0.579***
(0.579) (0.159) (0.004)
Controls Y Y Y Y Y Y
Observations 3,533 3,067 3,533 3,067 3,533 3,067
Adjusted R-squared 0.80 0.56 0.85 0.79 0.78 0.77
This table reports the results of the cross-sectional analyses of investor valuation (Panel A), credit rating quality (Panel B), and investor due diligence (Panel C)
that partition the treatment (auto ABS) deals issued after the effective date of Reg AB II’s asset-level disclosure requirements into above- and below-median
complex credit risk tranching, as proxied by Number of Tranches. The sample in the “More complex” (“Less complex”) columns consists of the treatment sample
with above-median (below-median) Number of Tranches plus the control sample. The dependent variable in columns 1 and 2 (3 and 4) [5 and 6] is the proportion
of underlying assets that is 30 (60) [90] or more days delinquent at 120 days after security issuance. All models include issuer and issuing year-quarter fixed
effects. Statistical significance is assessed using two-tailed t-tests with standard errors calculated clustering observations by deal. p-values are reported in
parentheses below estimates of (cross-sample differences in) coefficients. Coefficients that are more significant than common thresholds are designated as
follows: *** p<0.01, ** p<0.05, and * p<0.1. All variables are defined in Appendix C.
60
Table 9: Falsification Tests
Panel A: Falsification Tests of the Investor Valuation Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
(1) (2) (3)
Yield × Treat × PostFalsif 0.146 0.057 0.017
(0.522) (0.294) (0.439)
Controls Y Y Y
Observations 3,124 3,124 3,124
Adjusted R-squared 0.77 0.84 0.78
Panel B: Falsification Tests of the Credit Rating Quality Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
(1) (2) (3)
Rating × Treat × PostFalsif 0.008 0.008 0.004
(0.763) (0.334) (0.310)
Controls Y Y Y
Observations 3,124 3,124 3,124
Adjusted R-squared 0.78 0.85 0.79
Panel C: Falsification Tests of the Investor Due Diligence Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
(1) (2) (3)
Yield_Residual × Treat ×
PostFalsif 0.171 0.075 0.036
(0.794) (0.568) (0.434)
Controls Y Y Y
Observations 3,124 3,124 3,124
Adjusted R-squared 0.77 0.84 0.78
This reports the results of falsification tests of the analyses of investor valuation (Panel A), rating quality (Panel B),
and investor due diligence (Panel C) that eliminate all ABS deals issued on or after November 23, 2016 (the
effective date of Reg AB II’s asset-level disclosure requirements), and treat June 1, 2015 as if it were the effective
date of these requirements. The dependent variable in column 1 (2) [3] is the proportion of underlying assets that is
30 (60) [90] or more days delinquent at 120 days after security issuance. All models include issuer and issuing year-
quarter fixed effects. Statistical significance is assessed using two-tailed t-tests with standard errors calculated
clustering observations by deal. p-values are reported in parentheses below coefficient estimates. Coefficients that
are more significant than common thresholds are designated as follows: *** p<0.01, ** p<0.05, and * p<0.1. All
variables are defined in Appendix C.
Table 10: Alternative Pre-Disclosure Periods
Panel A: Investor Valuation Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
Since
Nov
2014
Since
Nov
2015
Since
Nov
2014
Since
Nov
2015
Since
Nov
2014
Since
Nov
2015
(1) (2) (3) (4) (5) (6)
Yield×Treat×Post 0.596* 0.593 0.344*** 0.355*** 0.341*** 0.324***
(0.051) (0.111) (<0.001) (0.002) (<0.001) (<0.001)
Controls Y Y Y Y Y Y
Observations 2,528 1,626 2,528 1,626 2,528 1,626
Adjusted R-
squared 0.73 0.71 0.85 0.86 0.80 0.83
Panel B: Credit Rating Quality Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
Since
Nov
2014
Since
Nov
2015
Since
Nov
2014
Since
Nov
2015
Since
Nov
2014
Since
Nov
2015
(1) (2) (3) (4) (5) (6)
Rating×Treat×Post 0.078** 0.083* 0.042*** 0.045*** 0.038*** 0.039***
(0.039) (0.059) (0.003) (0.006) (0.001) (0.005)
Controls Y Y Y Y Y Y
Observations 2,528 1,626 2,528 1,626 2,528 1,626
Adjusted R-squared 0.73 0.71 0.84 0.85 0.79 0.82
62
Panel C: Investor Due Diligence Analysis
Delinquency_30plus Delinquency_60plus Delinquency_90plus
Since Nov
2014
Since Nov
2015
Since Nov
2014
Since Nov
2015
Since Nov
2014
Since Nov
2015
(1) (2) (3) (4) (5) (6)
Yield_Residual ×
Treat × Post 0.746 0.682 0.544** 0.597** 0.657*** 0.651***
(0.348) (0.501) (0.015) (0.030) (<0.001) (0.001)
Controls Y Y Y Y Y Y
Observations 2,528 1,626 2,528 1,626 2,528 1,626
Adjusted R-squared 0.73 0.71 0.85 0.86 0.80 0.83
This table reports the results of the analyses of investor valuation (Panel A), rating quality (Panel B), and investor
due diligence (Panel C) that use alternative sample periods prior to the effective date of Reg AB II’s asset-level
disclosure requirements. In columns 1, 3, and 5 (2, 4, and 6), November 23, 2014 (November 23, 2015) is the first
day of the pre-disclosure period. The dependent variable in columns 1 and 2 (3 and 4) [5 and 6] is the proportion of
underlying assets that is 30 (60) [90] or more days delinquent at 120 days after security issuance. All models include
issuer and issuing year-quarter fixed effects. Statistical significance is assessed using two-tailed t-tests with standard
errors calculated clustering observations by deal. p-values are reported in parentheses below coefficient estimates.
Coefficients that are more significant than common thresholds are designated as follows: *** p<0.01, ** p<0.05,
and * p<0.1. All variables are defined in Appendix C.
