Did FAS 166 and FAS 167 Improve The
Transparency of Securitizing Banks?
Seda Oz
seda.oz@stern.nyu.edu
Accounting Department
Stern School of Business
New York University
Please do not circulate without permission
Version: January 24, 2013
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing
Banks?
Seda Oz
I
Abstract
This study investigates whether changes in mandated Financial Accounting Standards No.
166 and 167 (FAS 166 and FAS 167) improved the transparency of securitizing banks. I
use a difference-in-differences research design that compares four measures of information uncertainty (dispersion in analysts’ earnings forecasts, implied volatility, stock illiquidity, and
bid-ask spread) in the pre- versus post-FAS 166 and FAS 167 periods for securitizing versus
non-securitizing banks. I predict and find that information uncertainty of securitizing banks
decreases from the pre- to the post-FAS 166 and FAS 167 periods compared to non-securitizing
banks. I also show that information uncertainty of securitizing banks decreases more from the
pre- to the post-FAS 166 and FAS 167 periods with their involvements with variable interest
entities and implicit recourse practice. I exploit the later timing of the issuance of FAS 166
and FAS 167 than the financial crisis of 2007-2009 to rule out the confounding effects of the
crisis. I conclude that FAS 166 and FAS 167 have improved the transparency of securitizing
banks’ reporting. This study is the first to provide evidence for the disclosure and accounting
implications of FAS 166 and FAS 167 for securitizations and banks’ transparency.
I
Contact E-mail: seda.oz@stern.nyu.edu. This paper is based on my dissertation at the New York University. I greatly appreciate the invaluable guidance and support from my dissertation committee members:
Yakov Amihud, Mary Billings, Michael Jung, Joshua Ronen (chair), and Stephen Ryan. I also thank Karthik
Balakrishnan, Massimiliano Bonacchi, Matthew Cedergren, Jing Chen, Justin Deng, Jamie Diaz, Yiwei Dou,
Mike Gengrinovich, Begum Guney, Igor Kadach, Jessica Keeley, Seil Kim, Anya Kleymenova (discussant), Alina
Lerman, Jianchuan Luo, Bugra Ozel, Sorah Park, Emiliya Schain, Michael Tang, Tuba Toksoz, Chris Williams
(discussant), Xiaolu Xu (discussant), and seminar participants at the 2012 LBS Trans-Atlantic Doctoral Conference, 2012 AAA Northeast Region Meeting, 2013 FARS Meeting, and New York University for their helpful
comments and suggestions. All errors are my own.
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1. Introduction
This study examines whether Financial Accounting Standards No. 166 and 167 (FAS 166
and FAS 167) improve the transparency of reporting by bank holding companies’ (banks) with
respect to their securitization activities. I define banks’ reporting transparency (bank transparency, hereafter) as the degree to which a bank’s value can be estimated using information
available in the financial statements (e.g., Jiang, Lee and Zhang 2005). Securitizations are
complex transactions and banks’ involvements with these transactions were not fully conveyed
by prior accounting and disclosure requirements (e.g., Ryan 2007). Financial report disclosures
for securitizations lacked crucial information (e.g. issuers’ risk retention, risk of the securitized assets) that was already available to management (U.S. Senate Committee on Banking,
Housing, and Urban Affairs 2009). In June 2009, the Financial Accounting Standards Board
(FASB) issued FAS 166, which provides amended guidance to determine whether securitizations and other transfers of financial assets should be treated as sales or secured borrowings,
and FAS 167, which provides amended guidance to determine whether special purpose entities used in securitizations should be consolidated into the financial statements of one of the
parties involved in the transactions. The Board’s objective in issuing these standards was to
“[. . . ] enhance disclosures to provide financial statement users with greater transparency about
transfers of financial assets and a transferor’s continuing involvement with transferred financial
assets [. . . ] and improve transparency in financial reporting about an enterprise’s involvement
with a variable interest entity [. . . ]” (FASB, 2009). Motivated in part by this objective, I explore whether FAS 166 and FAS 167 improved banks’ reporting transparency by estimating the
association between measures of information uncertainty and measures of issuers’ risk retention
in securitizations in the pre- and post-FAS 166 and FAS 167 periods.
This study has four primary motivations. First, prior analytical studies show that bank
transparency benefits banking and financial system stability (e.g. Boot and Thakor, 1998;
Rosengren, 1998). For instance, Gorton and Huang (2004) present a model in which banks
experience both macroeconomic and idiosyncratic shocks. If bank transparency enables investors to observe the idiosyncratic shock, the long-term impact of macroeconomic distress on
these banks will be less severe than for banks with lower levels of transparency. Second, many
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public sector institutions, such as the Basel Committee on Banking Supervision, G7 Finance
Ministers, International Monetary Fund, and the World Bank, have campaigned for improved
bank accounting and disclosure practices (e.g., Basel Committee 1998, 1999). For instance,
due to the complex nature of securitizations, the SEC, the U.S. Senate Banking Subcommittee
on Securities, Insurance, and Investments, The President’s Working Group on Financial Markets, the Financial Crisis Advisory Group (FCAG) and others requested the FASB to improve
accounting and disclosure standards for banks’ securitization activities.
Third, the structure of securitizations and the use of off-balance sheet securitization vehicles (i.e., special purpose entities) by banks represent two reasons why an inquiry about the
impact of FAS 166 and FAS 167 on bank transparency is important. Securitization transactions have complex structures that make it difficult for investors to understand the risk and
performance characteristics of the transactions. Prior literature suggests that the complexity
of securitizations causes information uncertainty for market participants (e.g., Cheng, Dhaliwal, and Neamtiu 2011) and that these transactions require a certain level of expertise and
sophistication to evaluate (e.g., Barth, Clinch and Shibano 2003; Schwarcz 2004).
Fourth, the financial crisis of 2007-2009 demonstrated the potential adverse consequences
of banks’ use of off-balance-sheet securitization vehicles, which contributed to illiquidity and
financial losses for those banks (Ryan 2008). At the end of 2007, cash flows into the securitization vehicles declined as subprime mortgage defaults mounted. Many banks provided
contractual and/or non-contractual recourse to securitization vehicles even though the probability of banks providing recourse was very low initially. Motivated by these reasons, I ask
whether FAS 166 and FAS 167 provide financial statement users with greater transparency
about banks’ securitization activities.
While the FASB intends FAS 166 and FAS 167 to improve bank transparency, there are a
number of reasons why this might not occur. First, banks may have voluntarily provided transparent information even before the implementation of the new standards (e.g., Gurun, Lerman,
and Ronen 2009). Second, in light of the financial crisis of 2007-2009, users of financial reports
may have forced banks to be more transparent prior to FAS 166 and FAS 167. Third, banks
may have entered into (costly) restructuring arrangements to avoid consolidating off-balance
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sheet securitization vehicles under the new accounting standards (Bens and Monahan, 2008).
This may have the unintended consequences of complicating the securitization transactions
and reduce bank transparency. Last, more extensive disclosure requirements do not necessarily mean more clear information for market participants; in particular the information could
be too complex for equity market participants to determine the economic characteristics of
securitizations.
The first research question I address is how FAS 166 and FAS 167 affect the information
environments of securitizing and non-securitizing banks differently. To control for banks’ reasons to securitize, I construct matched samples of securitizing (treatment) and non-securitizing
(control) banks with similar propensity to securitize1 . Using a difference-in-differences research
design I compare four measures of information uncertainty (dispersion in analysts’ earnings forecasts, implied volatility, stock illiquidity, and bid-ask spread) in the pre- versus post-FAS 166
and FAS 167 periods for securitizing versus non-securitizing banks. I predict a decrease in the
information uncertainty of securitizing banks from the pre- to the post-FAS 166 and FAS 167
periods. I use four constructs to measure information uncertainty: analysts’ earnings forecast
dispersion, implied volatility, stock illiquidity2 , and bid-ask spread and cover the period from
2005 through 2011, with the post FAS 166 and FAS 167 period beginning from the first quarter
of 2010. Consistent with my prediction, I find that securitizing banks experience a decrease in
information uncertainty that is larger than the decrease experienced by non-securitizing banks
from the pre- to post-FAS 166 and FAS 167 periods. Further, I find that the association between information uncertainty of securitizing banks with total securitized assets decreases from
the pre- to post-FAS 166 and FAS 167 periods relative to that association of non-securitizing
banks.
The second research question I address is to what extent accounting and disclosure changes
in FAS 166 and FAS 167 affect securitizing banks. To address this question, I focus on my
1
The treatment and control samples have similar balance sheet structures that would result in similar
probability of securitization, all else equal. The primary distinction between these two samples is that one
made the choice to securitize for some endogenous and exogenous.
2
Also known as Amihud’s (2002) equity market liquidity measure.
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treatment sample (securitizing banks) and using a difference research design, examine the
association between FAS 166 and FAS 167 and securitizing banks’ information uncertainty
with regard to these banks’ risk retention in securitizations, relative to the association before
the guidance.
FAS 166 and FAS 167 eliminated the concept of qualifying special purpose entities3 that
were immune from consolidation, clarified and tightened the requirements for consolidation of
securitization vehicles. With these changes FAS 166 and FAS 167 should have generated consolidation of additional VIEs, showing significant increases on securitizing banks’ consolidated
balance sheets. I hypothesize and find that information uncertainty of securitizing banks in
the post-FAS 166 and FAS 167 period, is negatively associated with their involvements with
variable interest entities (VIEs). I measure securitizing banks’ involvement with consolidated
VIEs as the assets of consolidated VIEs scaled by the remaining total assets and find empirical
support for my prediction.
Securitizations raise an important information problem for investors through the practice of
implicit recourse. Implicit recourse is the voluntary support, beyond at contractually required
to a securitization vehicle. Prior literature shows that investors have little information to assess
the probability that implicit recourse will provided (e.g., Mason 2009; Cheng, Dhaliwal, and
Neamtiu 2011). Cheng, Dhaliwal, and Neamtiu (2011) examine the period prior to the issuance
of FAS 166 and FAS 167 and document a positive association between a bank’s likelihood of
providing implicit recourse and information uncertainty. I examine whether FAS 166 and FAS
167 mitigate the impact of implicit recourse on banks’ information uncertainty. I predict that
information uncertainty of securitizing banks in the post-FAS 166 and FAS 167 period, is negatively associated with implicit recourse. Following Calomiris and Mason (2004), I use managed
capital ratio to proxy for the likelihood of a bank providing implicit recourse. Additionally, I
use revolving loans (sum of home equity lines and credit card receivables) as another proxy for
implicit recourse. While prior literature shows implicit recourse is positively associated with
banks’ information uncertainty, I find that this association decreases in the post-FAS 166 and
3
Please see Section 2 for a detailed explanation of qualified special purpose entities.
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FAS 167 period. This suggests that the impact of the implicit recourse on securitizing banks’
information uncertainty is mitigated in the post-FAS 166 and FAS 167 period.
It is possible that my primary results are driven by the confounding effects of the financial
crisis of 2007-2009. Beginning in mid-2009, the financial market started to recover from the
adverse consequences of the crisis, presumably decreasing information uncertainty. Thus, the
decrease I observe in the measures of information uncertainty in the post-FAS 166 and FAS 167
period might reflect the fading effects of the crisis. To rule out this possibility, I conduct four
additional analyses. In the first two analyses, I use different variations of interactions between
total securitized assets and time period covering the crisis effects. Both analyses reveal that
information uncertainty of securitizing banks increases in 2007 and 2008 as opposed to nonsecuritizing banks. However, there is little evidence to show that information uncertainty of
securitizing banks decreases in 2009 after the crisis as opposed to non-securitizing banks. In
contrast, I find that the information uncertainty of securitizing banks decreases in the post-FAS
166 and FAS 167 period. In my third analysis, I use stock illiquidity as an independent variable
to proxy for the effects of the financial crisis. I assume that any change in the information
uncertainty, which can be attributed to the crisis will be captured by illiquidity measure. If so,
the incremental decrease in the information uncertainty of securitizing banks in the post-FAS
166 and FAS 167 will be attributable to the effectiveness of FAS 166 and FAS 167. Last, I
limit the pre-FAS 166 and FAS 167 period to between 2005:Q1 and 2007:Q1, and the post-FAS
166 and FAS 167 period to between 2010:Q1 and 2011:Q3. Similar to the first analysis, the
negative association between information uncertainty and securitizations remains significant
in the post-FAS 166/167 period. These analyses provide support for the idea that my main
results are not driven by the financial crisis of 2007-2009.
My paper makes three primary contributions. First, to the best of my knowledge, it is
the first study to examine the consequences of FAS 166 and FAS 167. The results should be
of interest to accounting regulators and investors, who want to understand whether FAS 166
and FAS 167 improve bank transparency by decreasing the information uncertainty associated
with securitizations. Second, this study is relevant to the mandatory disclosure literature. It is
debatable whether banks are better off with mandatory financial reporting regulations or not
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(e.g., Levine 2005). I provide support for having mandatory regulations on banks by providing
evidence for the beneficial impact of FAS 166 and FAS 167 on banks’ information environment.
Third, this study contributes to the financial institutions literature by providing evidence that
securitizing banks have greater information uncertainty, and that these effects diminished in
the post-FAS 166 and FAS 167 period.
The next section gives background information about special purpose entities, securitization,
FAS 166 and FAS 167. Section 3 discusses the related literature. Section 4 develops testable
hypotheses, while Section 5 describes the research design and reports the empirical results.
Section 6 conducts the robustness tests and Section 7 concludes with future research ideas.
2. Background Information
2.1. Special Purpose Entities (SPEs) and Securitization
SPEs are entities in the form of a trust or other legal vehicle designed to fulfill a specific
limited need of the company that organized it. SPEs are restricted by contract to engage in
specified and generally limited economic activities. Assuming that an SPE is not consolidated
by the firms involved with it, the SPE enables those firms to obtain off-balance sheet financing
of the assets held by the SPE and to recognize income on transactions with the SPE.
SPEs used in securitizations generally are bankruptcy-remote entities; therefore the bankruptcy
of the transferor of assets to the SPE and/or the user of the SPE’s assets cannot affect the
activities of SPE. For this reason, SPEs often receive better credit ratings than the issuer and
therefore can issue securities that pay lower interest rates than uncollateralized debt. There
are also downsides to using SPEs. For instance, the issuer’s perceived credit quality might
be adversely affected by the underperformance of an affiliated SPE. The poor performance of
collateral in an SPE may cause market participants to interpret as similar.
The vast majority of SPEs are used in securitization. The simplest form of securitization is
the creation of asset-backed securities (ABS) through SPEs that involves the following steps:
(i) the issuer sets up a SPE, and transfers financial assets to the SPE in a legal true sale; (ii) the
SPE issues ABS that entail a proportional interest in the principal and interest payments on the
financial assets, after fees are paid to the securitization sponsor; (iii) SPE collects payments on
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the financial assets and distributes them to the appropriate parties; and (iv) when substantially
all the payments are collected and disbursed, the SPE is dissolved (Gorton and Souleles 2006;
Ryan 2007). If the requirements for sale accounting are met, then the assets are recorded on
the balance sheet of the SPE not on the issuer’s balance sheet. Figure 1 shows how a typical
securitization is structured.
2.2. FAS 166 and FAS 167
On June 12, 2009, the FASB issued two new accounting standards: FAS No. 166, “Accounting for Transfers of Financial Assets” and FAS No. 167, “Amendments to FASB Interpretation
No. 46(R)”. These statements became effective on January 1, 2010.
FAS 166 revises prior sale accounting criteria for transfers of financial assets and stipulates
that these transfers need to be evaluated for relinquishment of control by the issuer over these
assets. FAS 166 amends Financial Accounting Standards No. 140 (FAS 140), “Accounting for
Transfers and Servicing of Financial Assets and Extinguishments of Liabilities” (2000). FAS
140 used to determine if transfer of assets is a sale. According to FAS 166, a securitization of
financial assets is accounted for as a sale when the transferring company surrenders control over
the assets transferred and receives cash and other proceeds in return. Control is considered
to be surrendered only if all three of the following conditions are met: (1) the assets have
been legally isolated from the issuer/transferor; (2) the transferee has the ability to pledge or
exchange the assets; and (3) the issuer does not maintain effective control over the assets. If
the securitization does not qualify as a sale, it is accounted for a secured borrowing, which the
assets remaining on the issuer’s balance sheet and no gain or loss being recognized.
Additionally, FAS 166 (along with FAS 167) eliminates the concept of a Qualifying Special
Purpose Entity (QSPE). QSPEs were defined under FAS 140 as passive entities distinct from
the transfer or that only hold financial assets and distributed the cash flows generated by those
assets. QSPEs were exempted from consolidation by the issuer. For securitizations that qualify
for sale accounting, the use of a QSPE ensured the securitized assets and associated debt stay
off the issuer’s books. Former QSPEs are now subject to consolidation like any other SPE.
FAS 167 primarily addresses whether variable interest entities (VIEs) often used in securitization should be included in the consolidated financial statements of any particular interested
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party. VIEs are entities whose equity ownership either is insufficient to finance their operations
or does not involve the usual control rights or financial interest associated with equity (i.e.,
the ability to make significant decisions through voting rights, the right to receive the residual
returns of the entity or the obligation to absorb the losses of the entity). Generally speaking, a
SPE is also a VIE, because the equity investors in SPEs usually lack substantive control rights.
The entity that consolidates a VIE is called its primary beneficiary. A primary beneficiary
has a “controlling financial interest.” FAS 167 amends FIN 46(R), “Consolidation of Variable
Interest Entities” (2003), which defined the characteristics of a controlling financial interest.
Under FAS 167, an entity is deemed to have a controlling financial interest in a VIE if it
has variable interests with both of the following characteristics: (a) the power to direct the
activities of a VIE that most significantly impact the VIE’s economic performance; (b) the
obligation to absorb losses of the VIE or the right to receive benefits from the VIE that could
potentially be significant to the VIE. FAS 167 requires that an enterprise continually reconsider
its conclusion regarding which interest holder, if any is the VIE’s primary beneficiary. If the
enterprise determines that it is no longer the primary beneficiary of a VIE, it would need to
deconsolidate the VIE on the date that the circumstances changed and recognize a gain or loss.
Figure 2 presents a timeline summarizing the developments leading to FAS 166 and FAS 167.
3. Prior Research
3.1. Securitization
Early theoretical work suggests that securitization provides a means of reducing bank risk
(Greenbaum and Thakor 1987; Pavel and Phillis 1987; Hess and Smith 1988). Later research
investigated the effect of securitization on banks focusing on three broad themes: (a) the quality
of the assets securitized or retained, (b) recourse arrangements, and (c) the impact on overall
bank risk.
This first issue relates to the quality of the assets securitized. Cantor and Rouyer (2000)
argue that the credit risk position of the issuer improves if the riskiness of the securities sold to
investors is higher than that of the issuer prior to the securitization. Otherwise, the transaction
might intensify the issuer’s net exposure to the default risk of its assets. In contrast, Ambrose,
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LaCour-Little, and Sanders (2005) find evidence suggesting that, in response to regulatory
capital incentives, lenders retain riskier loans in their portfolios while selling safer loans to the
secondary market.
The second issue relates to the recourse provided by the originating bank. Gorton and
Pennacchi (1995) argue that originating banks retain a portion of the securitized assets on
the balance sheet, or offer an implicit guarantee, to reduce moral hazard problems. Chen,
Liu, and Ryan (2008) find that banks’ retained interests vary by type of securitization and are
relatively low in the case of mortgages and relatively high for revolving loans such as credit card
receivables. Calomiris and Mason (2004) find that in credit card securitizations, risk remains
with the securitizing banks because of implicit recourse. Vermilyea, Webb, and Kish (2008)
also find evidence of implicit recourse in credit card securitizations, where banks with poorly
performing securitization portfolios claim higher fraud losses. Beneficial effects of recourse are
found by Higgins and Mason (2004) in the form of increased short- and long-term stock returns
and improved long-term performance.
The final issue relates to the implications of securitization on overall bank risk. Dionne
and Harchaoui (2003) find a positive association between securitization and bank credit risk.
Krahnen and Franke (2008) and Haensel and Krahnen (2007) find evidence that the issuance
of collateralized debt obligations increases the systematic risk of the issuing bank. Jiangli and
Pritsker (2008), on the other hand, find that mortgage securitization reduces bank insolvency
risk. Cebenoyan and Strahan (2004) suggest that securitization reduces bank risk; however,
banks use the achieved risk reduction to take on new risks. Purnanandam (2011) also provides
evidence that banks use the proceeds from securitizations to issue loans with higher than
average default risk. Barth, Ormazabal, and Taylor (2011) examine the sources of credit risk
associated with asset securitizations and whether credit rating agencies and the bond market
differ in perceptions of this risk. They find that credit rating is positively related to the
securitizing firm’s retained interest in the securitized assets, and is unrelated to the portion of
the securitized assets not retained by the firm.
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3.2. Variable Interest Entities (VIEs)
There is a relatively small amount of research discussing VIEs, and few papers in this
literature examine the VIEs in the context of FIN 46 and FIN 46R, which are now no longer
in effect. Beatty (2007) finds that financial institutions modify their economic behavior in
response to accounting changes made in FAS 140 and FIN 46R. Callahan and Spencer (2008)
examine the valuation and disclosure impact of FIN 46R on firms disclosing a synthetic lease
involving a VIE. They find that disclosed future minimum lease payments are significantly
valued by the market both pre- and post-adoption of FIN 46. Altamuro (2006) finds that
firms are less likely to engage in synthetic lease financing after the issuance of FIN 46, which is
consistent with the conjecture that the primary benefit of such transactions is the off-balance
sheet treatment, made more difficult following the passage of FIN 46. Dickinson, Donohoe, and
McGill (2010) examine the short-window market reaction to the regulatory actions resulting
in the promulgation of FIN 46 and FIN 46R and conclude that, overall, the market exhibits
a negative price reaction to the release of the first exposure draft proposing FIN 46. Finally,
Gurun, Lerman, and Ronen (2009) find that FIN 46 reduced analysts’ incentives to gather
private information. They also find that equity market participants act as if they perceived
higher business risk, as evidenced by reduced earnings response coefficients after 2001 for FIN
46 firms.
4. Hypotheses
To examine the impact of FAS 166/167 on the transparency of banks, it is necessary to
establish (1) the importance of bank transparency, i.e., the extent to which bank stability is
associated with transparency, and that (2) in the absence of efficient regulations, information
available in the financial statements about securitizations is not clear to the investors, hence
investors cannot efficiently estimate bank’s value, which leads deteriorated bank transparency.
First, analytical literature provides evidence to establish the importance of bank transparency. Analytical research examined the idea that increased transparency could enhance
market discipline on banks through bank’s risk taking behavior. Bank transparency increases
the sensitivity of the bank’s funding terms to the risk it takes. This creates incentives for
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the bank to control risk. For instance, Boot and Schmeits (2000) show that in the absence of
transparency the bank will choose low levels of monitoring because committing to high levels
of monitoring is costly. Thus, bank’s choice of low levels of monitoring will result in high risk
because as transparency decreases, risk becomes less observable. Similarly, Cordella and Yeyati
(1998) present a model, which shows how bank transparency impacts bank’s funding terms.
According to the authors’ model, the funding terms are determined after the bank has chosen
its risk of default and a bank’s funding terms are more favorable if the bank chooses lower
levels of risk. When transparency is high and thus, depositors can observe the level of risk
chosen by the bank, the bank chooses low levels of risk, since it would otherwise be punished
by a high required interest rate on its funds. However, when the transparency is low, the level
of risk cannot be observed by lenders. Then, the bank has no way to commit to a low level
of risk because in equilibrium, lenders assume that the bank will choose a high level of risk
and since the bank cannot be rewarded for a low level of risk it chooses a high level of risk in
equilibrium. This line of analytical work suggests that improving bank transparency reduces
bank risk-taking and this equilibrium improves market stability. My study extends this line
of theoretical study using an empirical data by examining the impact of mandatory disclosure
requirements on bank transparency ex post.
To establish the second key point, it is important to understand whether and how banks’
economic leverage and risk arising from off-balance sheet securitization activities are observable
to financial statement users from the banks’ financial reports. Chen, Liu, and Ryan (2008) find
that attributes of the securitizations (e.g., the retention of credit-enhancing interest-only strips)
are significantly associated with banks’ unsystematic risk. Cheng, Dhaliwal, and Neamtiu
(2011) find that the magnitude of the loan securitizations is associated with various measures
of banks’ information uncertainty. These studies, which cover the pre-FAS 166/167 period,
indicate that off-balance sheet securitization positions are not fully understood by the financial
statement users, and thus cause information uncertainty about securitization activities.
This uncertainty about securitizations is attributable in part to the complex structure of
securitizations, and in part to inadequate disclosure that failed to provide clear information
to financial statement users. Previous accounting standards regarding securitizations did not
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provide clear disclosure for investors to understand the true nature of securitizing banks’ offbalance sheet activities and were a limited attempt to describe complex securitization transactions (e.g., Ryan 2007). Financial analysts surveyed by the Chartered Financial Analyst (CFA)
Institute in 1999, 2003, and 2007 consistently rated securitization-related disclosure as high in
importance, yet assigned low ratings for the quality of such disclosure (CFA Institute 2008).
Difficulty in understanding securitization transactions can lead to information uncertainty
among market participants if some market participants have better information and/or better
information-processing abilities. Barth, Clinch, and Shibano (2003) suggest that investors
with a higher level of expertise are able to collect information from disclosures at a lower cost
and/or a faster pace, thus obtain an informational advantage over investors with less expertise.
Even more, Schwarcz (2004) suggests that securitization transactions are so complex that their
disclosure requires a level of sophistication that is challenging even for institutional investors
and security analysts.
FAS 166 and FAS 167 aim to improve existing standards and address concerns about companies who are stretching the use of off-balance-sheet entities. FASB states: “The new standards
eliminate existing exceptions, strengthen the standards relating to securitizations and specialpurpose entities, and enhance disclosure requirements. They’ll provide better transparency
for investors about a company’s activities and risks in these areas” (FASB 2009). Requiring information disclosure as a regulatory tool implies the existence of underlying information
uncertainty. As such, an effective regulatory system compels the disclosing party to provide
information to end-users that somehow redresses this uncertainty. By forcing banks to disclose more and better securitization information about securitization activities FAS 166/167
are expected to decrease information uncertainty of securitizing banks. My first hypothesis (in
alternative form) is:
Hypothesis 1a (H1). Information uncertainty of securitizing banks decreases from the
pre- to the post-FAS 166 and FAS 167 periods when compared to
non-securitizing banks.
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In the pre-FAS 166/167 period, QSPEs were exempt from consolidation and banks would
not consolidate VIEs unless they were the primary beneficiaries. FAS 166/167 eliminated
QSPEs, making these entities eligible for consolidation, and changed the method of determining the primary beneficiaries of the VIEs to require less risk retention. These two key changes
essentially led all QSPEs to be identified as VIEs, and banks to consolidate more VIEs. Thus,
I expect the impact of FAS 166/167 on the information uncertainty of securitizing banks to be
observable in these banks’ involvement with VIEs. My second hypothesis (in alternative form)
is:
Hypothesis 2 (H2). Information uncertainty of securitizing banks decreases MORE
from the pre- to the post-FAS 166 and FAS 167 periods with their
involvements with consolidated VIEs.
In an asset securitization transaction, recourse refers to guaranties promised to investors in
asset-backed securities that transfer some losses on the securitized assets to the guaranteeing
bank when the performance of those assets deteriorates. Banks typically provide implicit recourse, as a means to protect their reputation, in situations where they perceive that the failure
to provide this support, even though not contractually required, would damage future access to
the asset-backed securities market (OCC Guidance, 2002). Loss of reputation exposes banks to
decreased liquidity, increased interest rate risk, and burdensome regulatory supervision. On the
other hand, implicit recourse can cause information uncertainty because investors might lack
understanding about the allocation of losses to tranches of securitized assets absent recourse
and the triggers and terms of this recourse (Mason 2009).
Prior to FAS 166/167, companies provided insufficient and hard-to-interpret securitization
disclosures for market participants to understand recourse fully. Cheng, Dhaliwal, and Neamtiu
(2011) use five different measures of explicit and implicit recourse and illustrate that difficulty
in estimating recourse is positively associated with banks’ information uncertainty. Consistent
with my previous predictions, if FAS 166/167 decreases information uncertainty of securitization activities, I expect securitization recourse to be less opaque to market participants; given
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that Cheng, Dhaliwal, and Neamtiu (2011) established recourse is opaque to investors. Thus,
my third hypothesis (in alternative form) is:
Hypothesis 3 (H3). Information uncertainty of securitizing banks decreases MORE
from the pre- to the post-FAS 166 and FAS 167 periods with their
involvements of implicit recourse practice.
5. Research Design and Empirical Results
5.1. Data Sources
This study uses recently required disclosures about securitization vehicles mandated by FAS
166/167. To identify the sample, I start with all the bank holding companies that are required
to submit regulatory Y-9C reports to the Federal Reserve Board 4 . This report collects basic
financial data from a domestic bank holding company on a consolidated basis in the form of
a balance sheet, an income statement, and detailed supporting schedules, including Schedule
HC-S, a schedule of off-balance-sheet items arising from securitizations. The data are publicly
available on the Federal Reserve Bank of Chicago’s website. I eliminate banks that do not have
valid earnings data for at least five consecutive quarters. Further, I require firms to have daily
return data for at least 90 consecutive trading days.
Schedule HC-S, with securitized assets data, was first provided in Y-9C reports in the second
quarter of 2001. It provides these data for seven types of loans: 1-4 family residential mortgages,
credit card receivables, home equity lines of credit, automobile loans, other consumer loans,
commercial and industrial loans, and all other loans and leases. I aggregate the data provided
for these seven loan types into three categories: mortgages (1-4 family residential mortgages),
consumer loans (credit card receivables, home equity lines of credit, automobile loans, and
4
Banks with total consolidated assets of $150 million ($500 million after March 2006) or more are required
to file this report.
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other consumer loans), and commercial loans (commercial and industrial loans and all other
loans and leases).
Starting from the first quarter of 2011, Schedule HC-S provides assets and liabilities of three
categories of VIEs: securitizations vehicles, asset-backed commercial paper conduits, and other.
For the year of 2010, I hand-collect VIE data from the banks’ 10-Qs. I also gather several other
bank-specific variables (total assets, total loans, charge-offs, securitization income, interest rate
derivatives, tier 1 capital ratio, and nonperforming loans) from the Y-9C reports.
I collect daily returns from the Center for Research in Security Prices (CRSP) and quarterly
earnings per share from Bank Compustat. Additionally, I obtain analyst forecast data from
the Institutional Brokers Estimate System (I/B/E/S) and collect implied volatility data from
OptionMetrics. For each option price quote, OptionMetrics calculates the implied volatility,
using American or European models where appropriate. All option calculations employ historical London Inter-Bank Offered Rate (LIBOR) /Eurodollar rates for interest rate inputs, and
correctly incorporate discrete dividend payments. Lastly, I collect data on VIX, the Chicago
Board Options Exchange Market Volatility Index, through the Chicago Board Options Exchange website. The VIX is a measure of the implied volatility of S&P 500 index options.
5.2. Sample
This study focuses on two groups of banks: (i) securitizing banks, and (ii) non-securitizing
banks. When building my sample I eliminate banks that do not have valid earnings, and assets
data for at least five consecutive quarters. I also eliminate banks whose common equity is
not traded on the NYSE, NASDAQ, or AMEX stock exchanges, as one of my measures of
information uncertainty i.e., bid-ask spread are market-based. Further, I require firms to have
daily return data for at least 90 consecutive trading days. The primary data set contains 8,325
banks from 2005:Q1 to 2011:Q4. I begin with a cross-sectional analysis of the full sample and
compare the characteristics of banks that securitize, with those that do not.
Table 1 reports means and standard deviations of the variables for the full sample, as well
as for the securitizing and non-securitizing banks subsamples. There are 8,325 banks in the
sample, of which 7,899 are non-securitizing banks and 426 are securitizing banks. Despite the
significantly smaller number (5.11% of the sample), securitizing banks have a larger mean of
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total assets than non-securitizing banks.
The most significant difference is total assets. The mean value of the size of securitizing
banks ($8.95 billion) is six times the mean of non-securitizing banks ($1.41 billion). This finding
is consistent with previous research documenting that larger banks are more likely to securitize
(e.g., Minton, Stulz, and Williamson 2009). Securitizing banks tend to hold less liquid assets
(28% versus 35% of total assets), consistent with these banks having better access to external
funding and thus needing a smaller liquidity buffer than non-securitizing banks. The loan ratio
is higher for securitizing banks, with a mean of 72% versus 68% for non-securitizing banks.
Both securitizing and non-securitizing banks are financed mainly by deposits; however, nonsecuritizing banks rely on this source of funding to a larger extent (83% of total assets versus
76%). Further, the percentage of total assets funded by equity capital is slightly higher for
non-securitizing banks (13%) than for securitizing banks (11%).
To test the hypotheses empirically, I need to observe information uncertainty of securitizing
banks had they not securitized. As it is impossible to observe the same bank in both states,
I use non-securitizing banks as a proxy. However, this approach would still entail selection
problems (Heckman and Smith, 1995) because securitizing and non-securitizing banks likely
are ex-ante different. Such differences could arise because securitizing banks have different
balance sheet structure prior to securitizations or different consequences of securitizations. To
address this issue, I use propensity score matching to build a control sample from the nonsecuritizing banks whose balance sheet structure is as close as possible to that of securitizing
banks (e.g., Casu, Clare, Sarkisyan, and Thomas 2011).
The balance sheet structures of securitizing and non-securitizing banks exhibit key differences. Cheng, Dhaliwal and Neamtiu (2011) show that larger banks are more likely to be
securitizers. Prior studies also indicate that liquidity ratio of banks is an important determinant in securitization (e.g., Casu, Clare, Sarkisyan and Thomas 2011). Additionally, due to
the nature of securitizations, securitizing banks likely have a lower deposits ratio while nonsecuritizing banks likely have a lower loan ratio (e.g., Casu, Clare, Sarkisyan, and Thomas
2011). Calomiris and Mason (2004) indicate that securitizing and non-securitizing banks have
different tier 1 risk based capital ratio due to securitizing banks providing implicit recourse.
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They argue that according to efficient contracting (safety net abuse) hypothesis 5 , tier 1 ratio
of securitizing banks will be higher (lower).
The propensity score approach predicts securitization likelihood while balancing the covariates between the two groups; securitizing and non-securitizing banks. Therefore, I include
six variables to consider differences in balance sheet characteristics: (1) size (SIZE), measured
as the natural logarithm of total assets; (2) liquidity ratio (LIQIDUITY RATIO), measured
as the sum of cash, available-for-sale securities, trading assets, federal funds sold, and securities purchased with intent to resell, scaled by total assets; (3) loan ratio (LOAN RATIO),
measured as the proportion of on-balance-sheet net loans to total assets; (4) deposits ratio (DEPOSIT RATIO), measured as the quarterly average for all interest-bearing deposits, scaled by
total assets; (5) equity ratio (EQUITY RATIO), measured as the total equity divided by total assets; (6) tier 1 risk based capital ratio (TIER1). Each quarter, I estimate the following
ordered logistic model, for the banks in the overall sample:
P (SecF irmi,t = 1|Xi , t − 1)
where SecFIRM is a dummy variable, equal to 1 if a bank has securitization activities, 0
otherwise and Xi,t−1 is a vector of six balance sheet characteristics, lagged one quarter.
Table 2 reports the results of the ordered logistic propensity-score regression of the likelihood of securitization. The first column presents the average of the coefficient estimates,
and the second reports an aggregated t-statistic 6 . Consistent with prior research, I find that
securitizing banks are larger, less liquid, and hold a higher loan ratio and lower deposits ratio.
Consistent with the safety net abuse hypothesis, I find that securitizing banks have a lower tier
1 ratio. Table 2 indicates that the propensity-score model has reasonable explanatory power
5
According to the efficient contracting view, if banks establish contracts to satisfy the marketplace, they will
hold adequate risk to absorb their risk. Banks may choose to maintain capital in excess of minimum regulatory
requirements to satisfy market preferences. According to the safety net abuse view, banks that gain the most
from increasing the value of the deposit insurance put option will securitize to a greater extent. Furthermore,
if securitizing banks seek to maximize the value of these put options, then they would maintain capital levels
close to minimum regulatory requirements (Calomiris and Mason, 2004).
6
The aggregated t-statistic is calculated as the mean coefficient divided by the aggregate standard deviation.
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(Adj. Pseudo R2 =22.3%). This is important, since a propensity score with lower explanatory
power yields poorer matching, increasing the likelihood that inferences will be confounded by
correlated omitted variables.
The propensity score for each observation is the predicted probability from this logistic regression. I match securitizing banks with non-securitizing banks, employing nearest-neighbor
matching, i.e. choosing the non-securitizing bank that has the closest propensity score to each
securitizing bank. I impose a 1% tolerance level on the maximum propensity score distance
allowed. I perform 1:1 treatment-control match on the overall bank data and match each
observation from the treatment sample to an observation from the control sample quarterby-quarter. Once a match is made, I assume that match is the best match. Every quarter,
previous matches are reconsidered before making the current match to minimize disjoint ranges
of propensity scores. I allow each treatment-control match to have up to night missing quarterly observations but not more than four consecutive quarters. Matching process yields: 88
securitizing versus 88 non-securitizing banks (n=176).
Covariate balance is achieved if the treatment and control groups exhibit similar levels of
the explanatory variables. Adequate covariate balance is necessary to control for potential
confounding effects. To assess covariate balance between the treatment and control groups,
I estimate parametric t-tests of the difference in means of the explanatory variables between
two samples. Table 3 presents these tests. None of the differences are statistically different,
suggesting that the covariates are balanced across the treatment and control samples, mitigating
the likelihood of confounding effects.
5.3. Testing Hypothesis 1
I test H1 by estimating the coefficients in the following three models using OLS:
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Eq.(1) :
U ncertaintyt = β0 + β1 SecF IRM + β2 SecF IRM ∗ P OST 2010 + Control V ariablest−1 + Eq.(2) :
U ncertaintyt = β0 + β1 ABSt−1 + β2 ABSt−1 ∗ P OST 2010 + Control V ariablest−1 + Eq.(3) :
U ncertaintyt = β0 + β1 COM M BSt−1 + β2 COM M BSt−1 ∗ P OST 2010
+ β3 CON SBSt−1 + β4 CON SBSt−1 ∗ P OST 2010 + β5 M BSt−1 + β6 M BSt−1 ∗ P OST 2010
+ Control V ariablest−1 + 5.3.1. Primary Variables
UNCERTAINTY is the information uncertainty of the firm. I use four constructs to measure information uncertainty: analyst forecast dispersion (DISPERSION), implied volatility
(IMPLIED), stock illiquidity (ILLIQUIDITY) and bid-ask spread (SPREAD).
DISPERSION is the degree of consensus among market participants and it is widely used
as a proxy for information uncertainty in the prior literature (e.g., Lang and Lundholm 1996).
The intuition for this proxy is the following: analysts have different functions of the amount
of private information about the firm, holding the amount of public information constant
(assuming positive). Different functions of private information mean that there is no consensus
about the firm in the market which leads to information uncertainty (Barry and Jennings
1992; Bamber and Cheon 1998). When analysts use common public information more than
their private information, the consensus among the forecasts will increase, and dispersion will
decrease, all else being equal. I calculate DISPERSION as the standard deviation of quarterly
analyst forecasts (with only the latest forecast retained per analyst) issued from one day after
the prior earnings announcement to 1 day before the current on, scaled by the absolute value
of the actual measure at the earnings announcement.
7
7
In an alternative test, I calculate DISPERSION as the standard deviation of analysts’ one-quarter-ahead
earnings forecasts in the month preceding the month of the earnings announcement, scaled by the absolute
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My second proxy for information uncertainty is IMPLIED. Braun, Nelson, and Sunier (1995)
propose that at the market level, realized volatility rises sharply in response to bad news and
falls in response to good news. Their results suggest an association between investor sentiment
and volatility. Fleming (1998) argues that IMPLIED measures market sentiment relating to
perceived risk. Supported by these studies, I use IMPLIED to capture risk perception, an
important component of information uncertainty. For each option price quote, OptionMetrics
calculates the option’s implied volatility using, American or European models where appropriate. All option calculations use historical LIBOR/Eurodollar rates for interest rate inputs, and
correctly incorporate discrete dividend payments.
My third and fourth proxies for information uncertainty are ILLIQUIDITY, and SPREAD,
respectively. The vast majority of literature uses bid-ask spread and stock illiquidity to measure
information asymmetry among investors (traders) 8 . The justification for using these two measures in my study as proxies for information uncertainty is as follows: Information uncertainty
in the market is likely to generate greater private information search, since private information
is expected to resolve the uncertainty. This will lead to groups of investors or traders who
are privately informed, relative to other investors. As such, the information uncertainty in the
market will also generate information asymmetries among investors (traders). Additionally, analytical research provides support for the link between information uncertainty and asymmetry.
For instance, Routledge and Zin (2009) present a model where there is uncertainty about the
probability distribution for the underlying firm’s future cash-flows. Their model exhibits that
when the investor is uncertain about the dynamic consequences of his trading activities, this
uncertainty increases bid-ask spreads and reduces liquidity.
I use the illiquidity measure described in Amihud (2002) to capture stock illiquidity. Amihud’s illiquidity measure is daily absolute stock return divided by the $ trading volume in
value of the mean forecast. The results remain unchanged.
8
The intuition for using bid-ask spread and stock illiquidity as a proxy for information asymmetry is as
follows: when information asymmetry among market participants is high, informed traders can exploit their
informational advantage at the expense of uninformed traders. The market participants recognize the adverseselection problem, trading costs increase with the degree of information asymmetry between the uninformed
and informed investors. This will result in a wider bid-ask spread and lower stock liquidity (e.g., Kyle 1985;
Glosten and Milgrom 1985; Amihud and Mendelson 1991, 1986).
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thousands calculated as: |RETi,m,d |/V OLi,m,d , where |RETi,m,d | is the absolute return on firm
i on day d of month m, and V OLi,m,d is the respective daily volume in dollars. Following prior
research (e.g., Balakrishnan, Billings, Kelly and Ljungqvist 2012), the cross-sectional study in
this paper employs for each bank i the quarterly average of the Amihud’s measure as follows:
ILLIQU IDIT Yi,t,q
Di,t,q
1 X |RETi |
=
Di,t,q 1 V OLi
I measure SPREAD as the quarterly average of the difference between the closing ask
and the closing bid quotes scaled by the average of the ask and bid, expressed in percentage
terms (e.g., Ball, Jayaraman, and Shivakumar 2012). These are obtained from monthly data.
Specifically,
SP READi,t,q
Mi,t,q
1 X (ASKi − BIDi )
∗ 100
=
Mi,t,q 1 (ASKi + BIDi )/2
Where Mi,t,q is the number of months in quarter q of year t for bank i for which closing
monthly bids (BIDi ) and closing monthly asks (ASKi ) are available 9 .
SecFIRM is a dummy variable equal to one for banks with securitization activities, and
zero for control firms. ABS is total securitized assets scaled by the total assets. COMMBS is
commercial securitized assets (commercial and industrial loans and all other loans and leases),
CONSBS is consumer securitized assets (credit card receivables, home equity lines of credit,
automobile loans, and other consumer loans), and MBS is mortgages (1-4 family residential
mortgages). All three of them are scaled by total assets and for non-securitizing these variables
take value of 0. For non-securitizing banks, it takes value of zero. POST2010 is a dummy
variable equal to one if the observation falls in the post-FAS 166/167 period. I expect SecFIRM
and ABS to be positively associated and POST2010 to be negatively associated with the
dependent variables.
I employ quarter-year fixed effects in my models. The quarter-year fixed effects subsume any
non-interaction effect of POST2010 and control for time trends, allowing a clean identification
9
In an alternative analysis, I measure SPREAD as the quarterly mean of daily bid-ask spread, scaled by the
closing stock price. The results remain unchanged.
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of the incremental effect of SecFIRM (ABS) through the coefficient on SecFIRM * POST2010
(ABS * POST2010). This coefficient can be interpreted as the incremental change in information uncertainty between the pre and post periods for banks with securitizing activities relative
to those without.
5.3.2. Control Variables
To ensure that results are not driven by differences in bank characteristics, I use nine control
variables. First, reflecting findings in the previous literature, I control for market-to-book value
(MTBV) and bank size (SIZE). I expect a negative association between my dependent variables.
To control for risk retained through contractual obligations, I include total retained interest
from all asset securitizations scaled by total assets (RETAINED). To control for differences in
bank asset composition, I include the proportion of on-balance-sheet net loans to total assets
(LOAN RATIO). To control the riskiness of on-balance-sheet loans, I include charge-offs on onbalance-sheet loans scaled by total assets (CHGOFF ONBS), and past due on-balance-sheet
loans scaled by total assets (NPL ONBS). I expect these variables to be positively associated
with my dependent variables.
I include two variables to capture differences in banks’ (a) use of risk management products, (b) technical sophistication, and (c) dependence on securitization income. First, I use
securitization income and servicing fees scaled by total net income (SECINC). Second, I use
total notional amount of interest rate derivatives scaled by total assets (DERIVATIVES). I
have no expectation for the coefficient on DERIVATIVES because they can be used for either
hedging or speculation. I expect the coefficient on the SECINC to be positive if this variable
captures the operational risks associated with securitizations incremental to my other securitization variables (Chen, Liu, and Ryan 2008). To control for bank regulatory capital, I include
the tier 1 risk-based capital ratio (TIER1). Banks with lower risk tend to have higher capital
(e.g., Dionne and Harchaoui 2003). Thus, I predict a negative association between TIER1 and
UNCERTAINTY.
Finally, to control for market sentiment, I include VIX- a measure of the implied volatility
of S&P 500 index options by the Chicago Board Options Exchange. A higher value of VIX indicates lower sentiment. Therefore, I expect VIX to be positively associated with my dependent
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variables.
5.3.3. Results
Table 4 presents the results from estimating Eq. (1). The first column of Table 4 shows
results from using DISPERSION as the dependent variable. SecFIRM is significantly positive
(t = 5.72). Thus, securitizing banks are positively associated with information uncertainty as
opposed to non-securitizing banks in the pre-FAS 166/167 period. As predicted, the interaction
variable (SecFIRM * POST2010) is significantly negative (t = -4.64).
The signs of the coefficients on the control variables are mostly consistent with my predictions with two exceptions. I do not make any prediction of how derivatives would affect
information uncertainty. Results show that DERIVATIVES is positively associated with information uncertainty (t = 6.59). Prior research (e.g., Minton, Stulz and Williamson 2009) finds
that banks are more likely to use interest rate derivatives if they are engaged in asset securitization. Given the level of complexity and illiquidity of some derivatives, it is possible that
the use of derivatives also leads to increased bank information uncertainty. Hence, a positive
coefficient on DERIVATIVES is not surprising. On the other hand, I find an insignificant coefficient on LOAN RATIO (t = -1.55). Prior studies find mixed results with respect to whether
more loans increase risk, and information uncertainty. It is more likely that in my analysis, the
loan risk is captured by other bank attributes included in my models. The significance and the
interpretation of the control variables generally do not change for the rest of my study.
In the second column of Table 4, IMPLIED is the dependent variable. The coefficients on
SecFIRM, and SecFIRM * POST2010 are statistically significant (t = 3.62, and t = -3.56,
respectively). In the third column, I use ILLIQUIDITY as the dependent variable. While
SecFIRM is significantly positive (t = 5.23), SecFIRM * POST2010 is significantly negative (t
= -4.66) suggesting that information uncertainty of securitizing banks decrease in the post-FAS
166/167 period. In the fourth column, SPREAD is the dependent variable. The coefficient on
SecFIRM is significantly positive (t = 4.55). The interaction variable, SecFIRM * POST2010,
that is the focus of my study is significantly negative (t = -4.39). These results altogether
support H1 in that information uncertainty decrease in the post-FAS 166/167 period as opposed
to pre-FAS 166/167 period for banks with securitizing activities. Thus, I conclude that FAS
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166/167 achieve the objective of improving transparency of securitizing banks through enhanced
disclosure requirements in FAS 166/167.
Table 5 presents the results from estimating Eq. (2). This analysis uses ABS to capture
the volume of total securitized assets. The results are similar to the analysis of SecFIRM.
In the first column of Table 5, ABS is positively associated with DISPERSION (t = 6.42),
and ABS * POST2010 is negatively associated with DISPERSION (t = -4.28). In the second
column of Table 5, SecFIRM is positively associated with IMPLIED (t = 4.24), and ABS *
POST2010 is negatively associated with IMPLIED (t = -4.42). In the third column, ABS
is positively associated with ILLIQUIDITY (t = 5.71), and ABS * POST2010 is negatively
associated with ILLIQUIDITY (t = -4.11). In the fourth column of Table 5, with SPREAD as
the dependent variable I find similar results. ABS is positively associated with SPREAD (t =
5.72), and as predicted this association becomes negative in the post-FAS 166/167 period (t =
-4.62). Significance of control variable remains similar to the previous analysis. Results in both
Table 5 is consistent with those in Table 4, and they support H1 that information uncertainty
is positively associated with total securitized assets in the pre-FAS 166/167 period, but this
association becomes negative in the post-FAS 166/167 period.
Table 6 presents the results from estimating Eq. (3). This analysis uses three general
characteristics of banks’ asset securitizations - COMMBS, CONSBS and MBS - to capture
differences in banks’ securitizations portfolio. Chen, Liu and Ryan (2008) argue and provide
evidence that commercial loans have relatively high and difficult to verify credit risk, consumer
loans have relatively high but easier to verify credit risk, and mortgages have relatively low and
easy to verify credit risk. Consistent with their evidence, I predict that commercial loans are
more positively associated with information uncertainty than consumer loans and mortgages,
while consumer loans are more positively associated with uncertainty than mortgages. The
results are consistent with this prediction and also similar to the previous results. COMMBS,
CONSBS and MBS are all positively associated with all four proxies of information uncertainty.
The interaction variables between types of securitizations and POST2010 yield negative association, suggesting a decrease in the information uncertainty of securitizations. Consistent with
previous literature, the coefficient on COMMBS is bigger than CONSBS, and the coefficient
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on CONSBS is bigger than MBS. In accordance with this result, I observe a greater decrease
in the information uncertainty of COMMBS while this decrease lessens more for COMNS and
MBS.
Results in Table 4,5 and 6 are all consistent with each other, and they support H1 that
information uncertainty is positively associated with total securitized assets in the pre-FAS 166
and FAS 167 period, but this association becomes negative in the post-FAS 166 and FAS 167
period. Estimating three different OLS models allows me to observe the continuing impact of
FAS 166 and FAS 167 on securitizing banks. The first model gives a broader result concerning
securitizing and non-securitizing banks and documents the initial evidence of these standards on
securitizing banks. The second and third models strengthen this result by providing evidence
that decrease in the information uncertainty remain significant when I detangle securitizing
banks’ loan portfolios.
Overall, results in Table 4, 5 and 6 provide support for H1 that indicates FAS 166 and FAS
167’s effectiveness in decreasing information uncertainty about securitizations. A decrease in
information uncertainty of securitizing banks from the pre- to the post-FAS 166 and FAS 167
periods suggests better and more information about securitizations to market participants and
hence an improvement in bank transparency.
An important caveat of my study is the potential confounding effects of the financial crisis
of 2007-2009. The evidence of transparency improving for securitizing banks in the post-FAS
166 and FAS 167 might be (partially) caused by the fading effects of the crisis. In Section 6,
I discuss this possibility and conduct additional analyses to rule out the possibility that my
results are driven by the confounding effects of the crisis. After conducting robustness tests, my
results still support my predictions that information uncertainty about securitizations decrease
in the post-FAS 166/167 period.
5.4. Testing Hypothesis 2
I test H2 by estimating the following models using OLS:
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Eq.(4) :
U ncertaintyt = β0 + β1 ABSt−1 + β2 ABSt−1 ∗ P OST 2010
+ β3 V IEt−1 + β4 V IEt−1 ∗ P OST 2010 + Control V ariablest−1 + Eq.(5) :
U ncertaintyt = β0 + β1 COM M BSt−1 + β2 COM M BSt−1 ∗ P OST 2010
+ β3 CON SBSt−1 + β4 CON SBSt−1 ∗ P OST 2010 + β5 M BSt−1 + β6 M BSt−1 ∗ P OST 2010
+ β7 V IEt−1 + β8 V IEt−1 ∗ P OST 2010 + Control V ariablest−1 + 5.4.1. Primary Variables
Because banks with consolidated VIEs are mostly securitizing banks, I estimate Eq. (3) only
for my treatment sample, for which ABS is always non-zero. VIE reflects a bank’s involvement
with these entities. I measure this variable as the proportion of consolidated VIEs’ assets
to remaining total assets. VIE * POST2010 is the interaction variable, reflecting the bank’s
involvement with the VIEs in the post-FAS 166 and FAS 167 period. While I expect VIE
to be positive if FAS 166/167 decrease the information uncertainty about securitizing banks’
involvement with VIEs, I expect VIE * POST2010 to be negative. Firm and quarter-year fixed
effects are employed in the models. The quarter-year fixed effects subsume any non-interaction
effect on POST2010. The rest of the variables are as defined in the previous models.
5.4.2. Results
Table 7 presents the results from estimating Eq. (4). In the first column, I use DISPERSION
as the dependent variable. The coefficient on ABS and ABS * POST2010 show a decrease in
information uncertainty about total securitized assets from the pre-FAS 166/167 period to the
post-FAS 166/167 period, as seen in previous results (t = 3.99, and t = -3.74). The coefficient
on VIE is significantly positive (t = 3.13). The positive association between information uncertainty and VIE implies that market did not fully understand securitizing banks’ involvement
with VIE in the pre-FAS 166/167 period. There are two reasons for this result. First, if a bank
has consolidated VIEs, this might imply that the bank is involved with many off-balance-sheet
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activities. There might be many nonconsolidated VIEs about which investors would have less
information. Such perception will increase the information uncertainty for banks with consolidated VIEs. Second, when a bank consolidates VIEs, the information about these VIEs and
their components (e.g., assets, liabilities) is disclosed in its financial statements. Investors can
access this information easily; however, they might not have the level of sophistication required
to analyze these entities (i.e., determine the risk involved with these entities). This can be
partially caused by the insufficient regulations, which proves the validity of FAS 166/167. I
find that the coefficient on VIE * POST2010 is significantly negative (t = -3.39).
The second column presents a similar result when IMPLIED is the dependent variable. The
coefficients on ABS is significantly positive (t = 3.16) while ABS * POST2010 is significantly
negative (t = -3.41). VIE is significantly positive (t = 2.98). The coefficient on the interaction
variable, VIE * POST2010, is significantly negative (t = -2.69). The result suggests that for
securitizing banks, information uncertainty decreases in the consolidated VIEs from the preFAS 166/167 period to the post-FAS 166/167 period. Third column presents results when
I use ILLIQUIDITY as the dependent variable. Results exhibit similar results for ABS and
ABS * POST2010 to the previous ones (t = 3.30, t = -2.52, respectively). VIE is significantly
positive (t = 3.51). As predicted the coefficient on VIE * POST2010 is negative (t = -3.03),
suggesting a decrease in the information uncertainty from the pre-FAS 166/167 to the post-FAS
166/167 period. Parallel results occur when I use SPREAD as the dependent variable. The
coefficient on ABS is significantly positive (t = 3.53), the coefficient on ABS * POST2010 is
significantly negative (t = -2.73) and the coefficient on VIE is significantly positive (t = 3.14).
The interaction variable is again significantly negative (t = -2.73).
As a robustness test, I estimate Eq. (5) to examine whether the coefficient on VIE *
POST2010 remains significantly negative when different loan characteristics are taken into
account The results in Table 8 support my prediction. COMMBS, CONSBS and MBS are all
positively associated with four proxies of information uncertainty in the pre-FAS 166 and FAS
167 period, but these associations become negative in the post-FAS 166 and FAS 167 period.
As predicted in H2, I find a positive association between VIE and information uncertainty in
the pre-FAS 166 and FAS 167 period, and the interaction variable between VIE * POST2010
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becomes negative.
The evidence in Table 7 and 8 suggests that in the pre-FAS 166 and FAS 167 periods,
information about VIEs were not entirely reflected in the financial statements, which caused
information uncertainty because market participants couldn’t entirely assess banks’ involvement with these entities. My results provide evidence that information uncertainty of a securitizing bank decreases MORE from the pre- to the post-FAS 166 and FAS 167 period with
their involvements with consolidated VIEs. My results support that FAS 166 and FAS 167 in
fact improve financial reporting of securitizing banks with regard to VIEs. I conclude that the
accounting changes about VIEs in FAS 166 and FAS 167 improve transparency of securitizing
banks.
5.5. Testing Hypothesis 3
I test H3 by estimating the following model using OLS:
Eq.(6) :
U ncertaintyt = β0 + β1 ABSt−1 + β2 ABSt−1 ∗ P OST 2010
+ β3 RECOU RSE M CRt−1 (or RECOU RSE REVt−1 )
+ β4 RECOU RSE M CRt−1 ∗ P OST 2010 (or RECOU RSE REVt−1 ∗ P OST 2010)
+ Control V ariablest−1 + 5.5.1. Primary Variables
Implicit recourse is the non-contractual support that a securitizing bank provides to securitization vehicles. To capture the likelihood of a bank providing implicit recourse I use two
different proxies
10
. First proxy is managed capital ratio (RECOURSE MCR). Capital ratio
can be perceived as an emergency buffer to support on balance sheet loans. The idea in using
managed capital ratio is to take off-balance sheet securitized loans into account, which gives a
10
Cheng et. al. (2011) uses a first factor from a principal component analysis using total securitized assets,
securitization income, past due loans, charge-offs, and total retained interest from all asset securitizations.
Their measure captures both explicit and implicit recourse, and for that reason it is not suitable for my study.
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better sense about the vulnerabilities of a bank’s means to support its loans. The intuition to
use managed capital ratio as a proxy for implicit recourse proxy is as follows. Banks keep the
risks of securitized assets on their balance sheets by providing implicit recourse to securitization vehicles. When a securitizing bank anticipates providing non-contractual support it may
choose to increase its regulatory capital to ensure enough capital to support these vehicles. It
is also likely that a bank provide implicit recourse to reduce its regulatory capital requirements
without reducing their asset risk. To the best of my knowledge there are very few studies,
which focus on implicit recourse practice, and none of them provide evidence in the banks’
behavioral dilemma mentioned above. Calomiris and Mason (2004) provide preliminary evidence that many banks adjust their capital ratio in the anticipation of implicit recourse rather
than take advantage of reduced capital requirements. Therefore, I use managed capital ratio
to capture the likelihood of a bank’s providing implicit recourse. I assume that a securitizing
bank will adjust its capital requirements if there is an anticipation of implicit recourse practice,
so I assume a positive relation between managed capital ratio and implicit recourse practice.
I measure managed capital ratio by following Calomiris and Mason (2004). It is calculated as
tier 1 capital ratio plus tier 2 capital ratio divided by on-balance-sheet assets plus off-balance
sheet credit card receivables. It is likely that banks provide recourse to other securitized assets
too, which are not included in this definition of managed capital ratio. In practice, credit card
securitizations are a clear example of recourse. Calomiris and Mason (2004) establish that
credit card securitizers maintain capital ratios relative to the sum of on- and off-balance-sheet
assets that are lower than those of non-securitizing credit card banks or of all banks. Thus, to
increase the homogeneity of my recourse proxy, I solely focus on credit card receivables.
Second proxy is RECOURSE REV, which is calculated as the sum of all revolving loans
(home equity lines and credit card receivables). Implicit recourse generally is an issue only for
securitizations of revolving loans with no fixed maturity, in particular, credit card receivables
and home equity lines of credit. Because the principal in revolving loan securitizations fluctuates
with uncertain future borrowings and payments on the revolving loans, issuers typically must
contribute or remove loans from the SPEs as needed to maintain the outstanding principal
at the desired levels. Because of the ongoing relationship between issuers and the SPEs in
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these securitizations, issuers have the incentive to maintain their reputation to facilitate future
securitizations. Thus, the nature of revolving loans yields incentives for issuers to provide
implicit recourse to avoid losing the financing provided by the ABS (Chen, Liu and Ryan 2008).
When calculating RECOURSE REV, I don’t need to make any assumptions with regard to
banks’ capital requirements. This allows me to analyze implicit recourse practice on a broader
perspective. However, the problem with this proxy is that I dismiss other type loans. It might
be less frequent for other type of loans to need non-contractual support but because of that it
might cause even more information uncertainty. However, these drawbacks will only cause a
bias against finding support for H3 by decreasing the power of my proxies.
I estimate Eq. (6) only for my treatment sample. Prior literature establishes that securitization recourse is positively associated with information uncertainty, thus I expect both
implicit recourse proxies to be positive. The main variable that my study is interested in is
the interaction variable RECOURSE MCR * POST2010 (or RECOURSE REV * POST2010).
If FAS 166 and FAS 167 mitigates the impact of implicit recourse on the information uncertainty of the securitizing banks, I expect the interaction variable to be negative. Both firm and
quarter-year fixed effects are employed in my models. The quarter-year fixed effects subsume
any non-interaction effect on POST2010. The rest of the variables are as defined in the previous
models.
5.5.2. Results
Table 9 presents the results from estimating Eq. (4). The first column presents the results
with DISPERSION as the dependent variable. Consistent with my overall results, the coefficient on ABS is significantly positive (t = 4.00), and the coefficient on ABS * POST2010 is
significantly negative (t = -2.74). The coefficient on RECOURSE MCR is significantly positive (t = 3.74). This result is consistent with the findings of Cheng, Dhaliwal and Neamtiu
(2011). As predicted, I find that the coefficient on the interaction variable, RECOURSE MCR
* POST2010, is significantly negative (t = -2.65). The result suggests that the information
uncertainty about a securitizing bank providing recourse decrease in the post-FAS 166/167
period.
The second column of Table 9 presents the results with IMPLIED as the dependent variable.
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The coefficients on ABS, ABS * POST2010, and RECOURSE are statistically significant (t
= 3.71, t = -3.35, and t = 3.81, respectively). While the coefficient on RECOURSE MCR *
POST2010 has the predicted negative sign, it is statistically insignificant (t = -1.45). However,
this result does not contradict H3 hypothesis. The significant positive association between
information uncertainty and RECOURSE in the pre-FAS 166 and FAS 167 period is now
insignificant, which shows that information uncertainty about implicit recourse has lessened in
the post-FAS 166 and FAS 167 period. The insignificant association is probably caused by the
limited sample size.
In the third column, I use ILLIQUIDITY as the dependent variable. I find that ABS is
significantly positive (t = 5.34), and ABS * POST2010 is significantly negative (t = -3.13).
I find a positive association between RECOURSE MCR and ILLIQUIDITY (t = 3.27). This
suggests that RECOURSE MCR cause information uncertainty in the pre-FAS 166 and FAS
167, which is consistent with prior literature’s findings. However, what differs from the prior
literature, this association becomes negative in the post-FAS 166 and FAS 167 period (t =
-2.51). In the last column of Table 9, SPREAD is the dependent variable. The coefficients on
ABS, ABS * POST2010, and RECOURSE are statistically significant (t = 3.23, t = -3.90, t
= 2.85, respectively). On the other hand, the interaction variable RECOURSE * POST2010
is significantly negative (t = -3.72). These results provide statistical evidence that FAS 166
and FAS 167 mitigates the impact of implicit recourse on the information uncertainty about
securitizing banks.
Table 10 presents results from estimating Eq. (6) with RECOURSE REV. Since I use some
portion of securitized asset in my recourse variable, I can’t include ABS as an independent
variable. Instead I use OTHER ABS, which is all securitized assets except home equity lines
and credit card receivables. The results in Table 10 are very similar with my previous analysis.
RECOURSE REV is positively associated with all proxies of information uncertainty in the
pre-FAS 166 and FAS 166 period but as predicted this association becomes negative in the
post-FAS 166 and FAS 167 period.
My analysis of implicit recourse show significant but relative to my other tests less strong
results. Information uncertainty of securitizing banks decreases MORE from the pre- to the
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post-FAS 166 and FAS 167 period with their involvement with implicit resource practice.
Unfortunately my implicit recourse proxies are not perfect and they don’t capture all the
angles of implicit recourse. However, I still provide compelling evidence that FAS 166 and FAS
167 decrease uncertainty caused by the banks’ practice of implicit recourse.
6. Robustness Tests
My sample period runs from 2003 to 2011, so the financial crisis falls right in the sample
period. Thus, an important caveat of the study is the potential confounding effects of the
financial crisis of 2007-2009.
A complete chronology of the recent financial crisis might start in February 2007, when
several large subprime mortgage lenders started to report losses. However, most salient shock
regarding market liquidity came on August 9, 2007, when the French bank BNP Paribas temporarily halted redemptions from three of its funds because it could not reliably value assets
backed by U.S. subprime mortgages held in the funds. Financial firms worldwide began to
question the value of a variety of collateral they had been accepting in their lending operations. The result was a sudden hoarding of cash and cessation of interbank lending, which led
to severe liquidity constraints on many financial institutions (Cecchetti 2009).
Distress in this market is evident from the behavior of the (London Inter-Bank Offered
Rate) LIBOR . It is a key interest rate used to price various consumer and business loans,
including a variety of mortgages. LIBOR can be compared to the federal funds market because
both involve uncollateralized loans. Figure 3 plots the difference between the one-month fixedrate LIBOR and the monthly average federal funds rate. The divergence between these two
rates is typically less than 20 basis points. This small gap arises from an arbitrage that allows
a bank to borrow at LIBOR, lend for one month, and hedge the risk that the overnight rate
will move in the federal funds futures market, leaving only a small residual level of credit and
liquidity risk that accounts for the usually small spread. However, on August 9, 2007, the
difference between these two interest rates jumped to 40 basis points. The “LIBOR spread”
continued to deteriorate with investor and institution flight into safe securities through the
winter of 2008. In 2009, the fluctuations in LIBOR spread started to diminish. Starting in
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June 2009, the spread was again less than 20 basis points like before the financial crisis. This
evidence indicates that the effects of the financial crisis diminished in 2009 before the effective
date of FAS 166/167.
I conduct four alternate analyses to take the financial crisis into effect. First, to test the
possibility that the previously presented results might be driven by the financial crisis of 20072009, I estimate the following model using OLS for the treatment and control samples:
Eq.(7) :
U ncertaintyt = β0 + β1 ABSt−1 + β2 ABSt−1 ∗ BEG CRISIS + β3 ABSt−1 ∗ EN D CRISIS
+ β4 ABSt−1 ∗ P OST 2010 + Control V ariablest−1 + Based on the LIBOR spread, I create two crisis dummy variables. BEG CRISIS is a
dummy variable, which takes value 1 if the observation falls between 2007:Q3 and 2008:Q4.
END CRISIS is a dummy variable, which takes value 1 if the observation falls between 2009:Q1
and 2009:Q4. The expectation is that uncertainty is positively associated with BEG CRISIS
dummy variable but negatively associated with END CRISIS dummy variable. If my main results presented in Tables 4, 5 and 6 are driven by the confounding effects of the financial crisis
no significant association between information uncertainty and POST2010 * ABS should occur.
I employ f quarter-year fixed effects in my model. The quarter-year fixed effects subsume any
non-interaction effect of time dummy variables. The rest of the variables are as defined in the
previous models.
Table 11 presents the results from estimating Eq. (7). ABS is significantly positive for all
four dependent variables. The key point in this analysis is the interaction between ABS and
crisis dummy variables. Using all four dependent variables; DISPERSION, IMPLIED, ILLIQUIDITY and SPREAD I present a significantly positive association between the dependent
variables and ABS * BEG CRISIS (t = 1.98, t = 2.11, t = 2.14, t = 2.01, respectively) at
5% level, and a significantly negative association between the dependent variables and ABS *
END CRISIS (t = -1.65, t = -1.65, t = -1.88, t = -1.67, respectively) at 10
Second, I estimate the following model:
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Eq.(8) :
U ncertaintyt = β0 + β1 ABSt−1 + β2 ABSt−1 ∗ 2007Q1 + β3 ABSt−1 ∗ 2007Q2 + [. . .]+
+ β12 ABSt−1 ∗ 2009Q3 + β13 ABSt−1 ∗ 2009Q4 + β14 ABSt−1 ∗ P OST 2010
+ Control V ariablest−1 + I create a time dummy variables for each quarter between 2007 and 2010. If my results
presented in Tables 4, 5 and 6 are driven by the confounding effects of the financial crisis I
should not observe any significant association between information uncertainty and POST2010
* ABS interaction variable and interaction variables between 2009 time dummy variables and
ABS should capture the decrease in information uncertainty. I employ quarter-year fixed effects
in my models. The quarter-year fixed effects subsume any non-interaction effect of time dummy
variables. The rest of the variables are as defined in the previous models.
Table 12 presents the results from estimating Eq. (8). ABS is significantly positive for
all four dependent variables. The analysis reveals that information uncertainty about total
securitized assets increase from 2007 to 2008 in every quarter. This increase stops in 2009,
and most interactions variables between ABS and 2009 time dummy variables become statistically insignificant. As expected, these results show that information uncertainty about total
securitized assets increase from 2007Q3 until 2008Q4 and even it starts diminishing in the last
quarters of 2009, the most significant decrease in information uncertainty starts after 2010. I
find that the coefficients on ABS * POST2010 are negative for all four dependent variables (t
= -3.77, t = -3.30, t = -3.20, t = -3.38, respectively) at 1% level. These results support my
primary findings.
While 2009 quarter time dummy variables are insignificant and ABS * POST2010 is significantly negative the magnitude and the sign of the coefficients are very similar. One can
interpret this result as such that the decrease in information uncertainty of securitizing banks
partially starts in 2009. Such an interpretation is not entirely implausible. In 2009, FASB
issued FSP (FASB Staff Position FIN 140-4) on disclosures about transfers of financial assets
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Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
and interests in variable interest entities transactions
11
35
. FAS 166 and FAS 167 superseded
FSP.
While most of the disclosures in FAS 166 and FAS 167 are identical to those included in
the FSP, the FASB decided to enhance certain disclosures included in the FSP and expand
their scope to all transfers of financial assets and not just for securitizations and asset-backed
financing arrangements, as required by the FSP. Additionally, unlike the FSP, which applies
only to public entities, the disclosure requirements of FAS 166 and FAS 167 apply to both
public and non-public entities. The issuance of FSP suggests an anticipation period for the
disclosures mandated by FAS 166 and FAS167 in the market. Such anticipation might cause a
partial decrease in the information uncertainty of securitizing banks in 2009. This result does
not contradict with my primary results since FSP included similar disclosures about the transfer
of financial assets to FAS 166 and FAS 167. In fact, a decrease in information uncertainty of
securitizing banks following FSP supports my conjecture that changes in disclosures about the
transferred of financial assets and variable interest entities improved information environment
of securitizing banks.
Third, I estimate the following model:
Eq.(9) :
U ncertaintyt = β0 + β1 ABSt−1 + β2 ABSt−1 ∗ P OST 2009 + β3 ABSt−1 ∗ P OST 2010
+ β4 ILLIQU IDIT Yt−1 + β5 ILLIQU IDIT Yt−1 ∗ P OST 2009
+ β5 ILLIQU IDIT Yt−1 ∗ P OST 2010 + Control V ariablest−1 + In this model, I only use DISPERSION and IMPLIED as two proxies of information uncertainty.. Financial crisis of 2007-2009 caused serious illiquidity problems on banks. It is fair
to assume that the impact of financial crisis started to lessen when banks started getting relief
on their illiquidity problems. I include ILLIQUIDTY as an independent variable to capture
‘the lessening impact of the financial crisis’. I assume that ILLIQUIDITY will capture any
11
Please see Figure 2
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Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
36
portion of information uncertainty attributable to the financial crisis, and then if I still find a
significantly negative association between UNCERTAINTY and BAS in the post-FAS 166 and
FAS 167 period, I can attribute this incremental decrease to the effectiveness of FAS 166 and
FAS 167.
Table 13 presents results from estimating Eq. (9). As expected, ILLIQUIDTY is positively
associated with UNCERTAINTY at all time periods. But ILLIQUIDTY is less positively
associated with UNCERTAINTY in the post-crisis and FAS 166 and FAS 167 periods. This
is consistent with the fact that the effects of the financial crisis started to fade away after
2009. I find ABS to be positively associated with DISPERSION and IMPLIED in the preFAS 166 and FAS 167 period (t = 4.65, t = 4.19, respectively). In the post 2009 period,
ABS is negatively associated with DISPERSION and IMPLIED, but these associations are
not statistically significant (t = -0.75, t = -1.08, respectively). Most importantly, ABS has
significant negative association between DISPERSION and IMPLIED in the post-FAS 166 and
FAS 167 period (t= -2.87, t=-2.49, respectively). All together, these results suggest that the
decrease in the information uncertainty of securitizing banks in the post-FAS 166 and FAS 167
period is in fact not attributable to the financial crisis, but mainly to the effectiveness of these
standards.
In the last analysis to rule out the effect of the financial crisis of 2007-2009, I exclude the
financial crisis from my sample period. Therefore, I limit the pre-FAS 166/167 period between
2005:Q1 and 2007:Q2, and the post-FAS 166/167 period between 2010:Q1 and 2011:Q4. Then,
I estimate Eq. (2) with the adjusted sample period. The results presented in Table 8, Panel
B remain very similar to previously presented analysis in Table 5. ABS * POST2010 remains
significantly negative for all four dependent variables.
All four analyses provide enough evidence that my main results presented in Tables 4, 5
and 6 are not driven by the fading effects of the financial crisis of 2007-2009, and thus H1 has
sufficient support.
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7. Concluding Remarks
In this study, I examine whether information uncertainty about banks’ securitization activities decreases in the post- versus pre-FAS 166 and FAS 167 period. I use dispersion in analysts’
earnings forecasts, implied volatility, stock illiquidity, and bid-ask spread to capture different
aspects of information uncertainty. I predict and find that (i) securitizing banks experience a
decrease in information uncertainty compared to non-securitizing banks from the pre- to the
post-FAS 166 and FAS 167 periods; (ii) information uncertainty of total securitized assets decreases from the pre- to the post-FAS 166 and FAS 167 periods; (iii) information uncertainty
of securitizing banks is negatively associated with their involvement with consolidated VIEs in
the post-FAS 166 and FAS 167 period; and (iv) information uncertainty of securitizing banks
is negatively associated with implicit recourse in the post-FAS 166 and FAS 167 period. Additional analyses conducted to rule out the confounding effects of the financial crisis of 2007-2009
confirm the findings. In the empirical models, I control for a host of variables that prior research shows to be associated with banks’ equity risk, banks’ loan portfolio composition, banks’
regulatory capital, and banks’ operating performance.
My study has direct implications for equity market participants. The results highlight the
benefits of FAS 166 and FAS 167 for the market participants and provide evidence that these
standards bring more clear information about securitizations to market participants. Also, this
study has direct implications for accounting regulators. These results indicate that reporting
standards improve the bank transparency.
My study also brings up new research questions for future. One avenue for future research is
to examine whether FAS 166 and FAS 167 change banks’ risk taking behavior. Prior theoretical
research suggests that less information uncertainty in the market leads less risk taking by
managers, but securitizing banks exhibit more risk taking ex ante. Thus, it is open empirical
question how FAS 166 and FAS 167 would affect risk taking behavior of banks. Last, research
to further our understanding of implicit recourse is necessary. While implicit recourse is very
crucial part of securitizations, because it is difficult to empirical measure, its implications are
not very well understood.
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Gorton, G. B., & Pennacchi, G. G. (1995). Banks and loan sales Marketing nonmarketable
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Figure 1: Structure of a SPE in a Securitization
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Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
43
Figure 2: Timeline of FAS 166 and FAS 167
S. Oz
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
44
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
Figure 3: LIBOR Spread During the Financial Crisis of 2007-2009
S. Oz
45
5.066
0.133
0.328
0.831
0.677
0.005
0.010
0.203
SIZE
EQU IT Y RAT IO
LIQU IDIT Y RAT IO
DEP OSIT RAT IO
LOAN RAT IO
CHGOF F RAT IO
N P L RAT IO
T IER1
U nique Banks
N umber of
M ean
V ariables
8325
0.349
0.021
0.246
0.164
0.123
0.133
0.133
4.225
Std.Dev.
All Banks
0.142
0.011
0.003
0.715
0.757
0.284
0.116
16.949
426
0.093
0.009
0.032
0.158
0.168
0.168
0.074
62.229
Std.Dev.
0.213
0.010
0.061
0.676
0.830
0.348
0.133
1.413
M ean
7899
0.328
0.020
1.106
0.164
0.113
0.174
0.092
2.797
Std.Dev.
Banks
Banks
M ean
Non-Securitizing
Securitizing
-0.071
0.000
-0.058
0.039
-0.072
-0.064
-0.017
15.536
M ean
0.0000
0.3458
0.2967
0.0093
0.0000
0.0003
0.0001
0.0000
Std.Dev.
(Percentage)
Difference in Means
Descriptive Statistics for Securitizing and Non-Securitizing Banks
SIZE is the natural logarithm of total assets. EQUITY RATIO is the total equity divided by total assets. LIQUIDITY RATIO is
the sum of cash, available-for-sale securities, trading assets, federal funds sold, and securities purchased with intent to resell, scaled
by total assets. DEPOSIT RATIO is the quarterly average for all interest-bearing deposits, scaled by total assets. LOAN RATIO
is the proportion of on-balance sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by
total assets. NPL ONBS is past due on-balance sheet loans scaled by total assets. TIER1 is the tier 1 risk based capital ratio.
Table 1: Descriptive Statistics
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Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
46
233, 454
0.223
Adj. R − squared
N umber of Observations
Y es
-0.569
Bank Dummies
+/−
+
EQU IT Y RAT IO
T IER1
-1.030
−
DEP OSIT RAT IO
0.501
0.394
-0.156
0.227
Coef f icient
Avg.
+
+/−
+
P rediction
LOAN RAT IO
LIQU IDIT Y RAT IO
SIZE
= SecF IRM
Dependent V ariable
-2.16
6.42
-6.76
7.07
-2.11
233, 454
0.187
No
-1.423
1.211
-1.877
0.477
-0.218
0.329
Coef f icient
t − statistic
8.08
Avg.
Aggr.
-2.44
7.73
-6.81
8.02
-2.54
9.47
t − statistic
Aggr.
Aggregated estimates of the ordered logistic propensity-score regression of the securitization likelihood
SIZE is the natural logarithm of total assets. LIQUIDITY RATIO is the sum of cash, available-for-sale securities,
trading assets, federal funds sold, and securities purchased with intent to resell, scaled by total assets. LOAN RATIO
is the proportion of on-balance sheet net loans to total assets. DEPOSIT RATIO is the quarterly average for all
interest-bearing deposits, scaled by total assets. EQUITY RATIO is the total equity divided by total assets. TIER1
is the tier 1 risk based capital ratio.
Table 2: Propensity Score Regression
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Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
47
0.774
0.842
0.187
0.196
LOAN RAT IO
DEP OSIT RAT IO
EQU IT Y RAT IO
T IER1
U nique Banks
88
0.273
LIQU IDIT Y RAT IO
N umber of
8.339
88
0.201
0.187
0.844
0.778
0.273
8.357
Control
T reatment
SIZE
M ean
M ean
M atchedSample
-0.005
0.000
-0.002
-0.004
0.000
-0.018
Dif f erence
0.9112
0.9945
0.9874
0.9712
0.9961
0.7872
values
p−
Test statistics of covariate distributions for the treatment (Securitizers) and the control
(Non-securitizers) samples
SIZE is the natural logarithm of total assets. LIQUIDITY RATIO is the sum of cash,
available-for-sale securities, trading assets, federal funds sold, and securities purchased with
intent to resell, scaled by total assets. LOAN RATIO is the proportion of on-balance sheet
net loans to total assets. DEPOSIT RATIO is the quarterly average for all interest-bearing
deposits, scaled by total assets. EQUITY RATIO is the total equity divided by total assets.
TIER1 is the tier 1 risk based capital ratio.
Table 3: Covariate Balance
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Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
48
2.804 ***
−
+
+
+
+
RET AIN ED
LOAN RAT IO
CHGOF F RAT IO
N P L ON BS
SECIN C
+
V IX
0.472
0.498
4, 827
Adj. R − squared
N umber of Observations
3, 184
Y es
0.183 ***
-0.251 ***
0.895 ***
2.020 ***
6.407 ***
3.618 **
-0.210
3.393 ***
-0.684 ***
-0.452 ***
-0.943 ***
2.062 ***
0.692 **
10.45
-4.50
2.69
5.41
3.33
2.43
-0.78
3.17
-9.89
-2.68
-3.56
3.62
2.05
Estimates t − value
Y es
15.48
-5.39
6.59
2.64
2.39
2.53
-1.55
3.42
-5.15
-1.83
-4.64
5.72
2.15
t − value
Implied
4, 189
0.488
Y es
0.228 ***
-0.518 ***
0.680 ***
2.505 ***
5.481 ***
3.937 ***
-0.441
3.002 ***
-0.586 ***
-0.346 **
-1.348 ***
2.354 ***
0.454 **
Estimates
11.56
-3.72
6.81
5.97
3.97
4.90
-0.56
5.74
-3.02
-2.49
-4.66
5.23
2.07
t − value
Illiquidity
4, 189
0.512
Y es
0.245 ***
-0.161 ***
0.354 ***
3.885 ***
4.795 ***
4.472 ***
-0.933
2.686 ***
-0.685 ***
-0.121 **
-1.003 ***
2.821 ***
0.789 **
Estimates
12.41
-5.19
8.63
6.66
2.86
3.56
-0.71
5.37
-3.26
-2.12
-4.39
4.55
1.97
t − value
Spread
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
T ime F ixed Ef f ects
-0.802 ***
−
T IER1
0.075 ***
0.062 ***
1.801 ***
5.499 **
5.143 **
+/−
DERIV AT IV ES
-0.201 ***
−
M T BV
-0.348
-0.072 *
−
SIZE
2.308 ***
-1.711 ***
+
−
SecF IRM ∗ P OST 2010
0.709 **
Estimates
SecF IRM
+/−
Intercept
P rediction
Dispersion
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on SecFIRM (dummy variable for
securitizing banks), POST2010 (dummy variable for the post-FAS 166 and FAS 167 period), and control variables for the treatment and control
samples.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the
absolute value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock
illiquidity (ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume);
(4) bid-ask spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of the
ask and the bid, expressed in percentage terms. Independent variables: SecFIRM is a dummy variable equal to 1 if a bank is a securitizing bank.
POST2010 is a dummy variable equal to 1 if the observation falls between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is
the market-to-book ratio. RETAINED is the total retained interest from all asset securitizations scaled by total assets. LOAN RATIO is the proportion
of on-balance sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due
on-balance sheet loans scaled by total assets. SECINC is the securitization income and servicing fees scaled by total net income. DERIVATIVES is
the total notional amount of interest rate derivatives scaled by total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of
S&P 500 index options by the Chicago Board Options Exchange. t-statistics are based on standard errors clustered by firm and quarter-year to correct
for time-series and cross-sectional dependence respectively. ***, **, * represent statistical significance at 1%, 5%, 10% levels.
Table 4: SecFIRM Analysis, OLS Estimation of Equation (1)
S. Oz
49
4.016 ***
−
+
+
+
+
RET AIN ED
LOAN RAT IO
CHGOF F RAT IO
N P L ON BS
SECIN C
+
V IX
0.461
0.522
4, 827
Adj. R − squared
N umber of Observations
3, 184
Y es
0.237 ***
-0.286 ***
0.934 **
1.309 ***
6.065 ***
1.445 **
-0.708
4.836 ***
-0.488 ***
-0.053 ***
-1.890 ***
3.762 ***
0.795 **
10.54
-4.78
2.57
5.33
4.19
2.55
-1.04
9.05
-10.06
-3.35
-4.42
4.24
3.19
Estimates t − value
Y es
16.09
-5.91
2.66
3.11
2.61
2.24
-0.13
3.52
-4.06
-2.14
-4.28
6.42
2.49
t − value
Implied
4, 189
0.508
Y es
0.182 ***
-0.210 ***
1.041 ***
2.094 ***
5.927 ***
2.553 ***
-0.340
2.029 ***
-0.149 ***
-0.063 **
-1.346 ***
2.639 ***
0.830 **
Estimates
12.95
-4.58
2.75
6.12
3.71
4.26
1.47
5.63
-3.15
-2.18
-4.11
5.71
2.55
t − value
Illiquidity
4, 189
0.512
Y es
0.093 ***
-0.343 ***
0.651 ***
2.266 ***
4.286 ***
1.723 ***
-0.124
3.110 ***
-0.331 **
-0.088 **
-1.232 ***
2.786 ***
0.335 **
Estimates
12.53
-5.00
8.16
4.20
3.04
3.75
0.96
5.24
-2.45
-2.22
-4.62
5.72
2.24
t − value
Spread
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
T ime F ixed Ef f ects
-0.283 ***
−
T IER1
0.087 ***
0.690 ***
1.794 ***
5.003 ***
1.527 **
+/−
DERIV AT IV ES
-0.113 ***
−
M T BV
-0.662
-0.038 **
−
SIZE
2.488 ***
-1.392 ***
+
−
0.680 **
ABS ∗ P OST 2010
+/−
Estimates
ABS
Intercept
P rediction
Dispersion
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on ABS (total securitized assets),
POST2010 (dummy variable for the post-FAS 166 and FAS 167 period), and control variables for the treatment and control samples.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the
absolute value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock
illiquidity (ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume);
(4) bid-ask spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of the
ask and the bid, expressed in percentage terms. Independent variables: ABS is the total securitized assets scaled by total assets. POST2010 is a dummy
variable equal to 1 if the observation falls between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is the market-to-book
ratio. RETAINED is the total retained interest from all asset securitizations scaled by total assets. LOAN RATIO is the proportion of on-balance
sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due on-balance sheet
loans scaled by total assets. SECINC is the securitization income and servicing fees scaled by total net income. DERIVATIVES is the total notional
amount of interest rate derivatives scaled by total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of S&P 500 index
options by the Chicago Board Options Exchange. t-statistics are based on standard errors clustered by firm and quarter-year to correct for time-series
and cross-sectional dependence respectively. ***, **, * represent statistical significance at 1%, 5%, 10% levels.
Table 5: ABS Analysis, OLS Estimation of Equation (2)
S. Oz
50
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on COMMBS, CONSBS, MBS (total
securitized assets by loan type), POST2010 (dummy variable for the post-FAS 166 and FAS 167 period), and control variables for the treatment and
control samples.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the absolute
value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock illiquidity
(ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume); (4) bid-ask
spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of the ask and the bid,
expressed in percentage terms. Independent variables: COMMBS is securitized commercial loans (commercial and industrial loans and all other loans,
leases, and assets) scaled by total assets. CONSBS is securitized consumer loans (home equity lines of credit, credit card receivables, automobile loans,
and other consumer loans) scaled by total assets. MBS is securitized 1-4 family residential mortgages scaled by total assets. POST2010 is a dummy
variable equal to 1 if the observation falls between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is the market-to-book
ratio. RETAINED is the total retained interest from all asset securitizations scaled by total assets. LOAN RATIO is the proportion of on-balance
sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due on-balance sheet
loans scaled by total assets. SECINC is the securitization income and servicing fees scaled by total net income. DERIVATIVES is the total notional
amount of interest rate derivatives scaled by total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of S&P 500 index
options by the Chicago Board Options Exchange. t-statistics are based on standard errors clustered by firm and quarter-year to correct for time-series
and cross-sectional dependence respectively. ***, **, * represent statistical significance at 1%, 5%, 10% levels.
CONTINUED
Table 6: COMMBS, CONSBS, and MBS Analysis, OLS Estimation of Equation (3)
S. Oz
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
51
−
+
+/−
M T BV
RET AIN ED
LOAN RAT IO
-0.043 ***
−
+
T IER1
V IX
Y es
0.561
4, 827
T ime F ixed Ef f ects
Adj. R − squared
N umber of Observations
0.074 ***
0.364 ***
0.361 **
2.791 **
1.148 ***
-0.667
1.935 ***
0.982 ***
-1.521 ***
1.071 ***
-1.750 ***
+/−
DERIV AT IV ES
+
−
SIZE
+
−
M BS ∗ P OST 2010
SECIN C
+
M BS
N P L ON BS
-0.117 ***
−
CON SBS ∗ P OST 2010
+
-0.081 **
+
CON SBS
CHGOF F ON BS
-1.174 ***
−
COM M BS ∗ P OST 2010
0.619 **
+
2.336 ***
+/−
COM M BS
15.83
-5.05
4.51
2.36
2.24
2.69
1.16
3.58
-3.71
-2.11
-3.20
4.51
-3.84
4.33
-3.21
4.38
2.31
3, 184
0.487
Y es
0.062 ***
-0.123 ***
1.378 **
1.521 ***
3.687 ***
0.245 **
-0.007
3.763 ***
-0.096 ***
-0.022 ***
-0.790 ***
1.400 ***
-1.368 ***
1.913 ***
-1.247 ***
2.125 ***
0.552 ***
10.23
-3.87
2.48
4.67
3.79
2.09
-0.18
5.92
-4.19
-2.87
-2.90
4.16
-3.28
4.23
-3.28
4.95
3.06
Estimates t − value
Estimates t − value
Intercept
P rediction
Implied
Dispersion
Table 6, continued
4, 189
0.488
Y es
0.201 ***
-0.015 ***
0.417 ***
1.248 ***
2.379 ***
0.422 ***
0.278 *
3.393 ***
-0.050 **
-0.028 **
-1.037 ***
0.910 ***
-1.164 ***
1.610 ***
-1.242 ***
2.209 ***
0.483 ***
Estimates
13.09
-2.98
3.25
3.92
3.94
4.04
1.93
5.29
-2.02
-2.01
-3.54
5.44
-2.97
5.33
-2.86
5.10
3.13
t − value
Illiquidity
4, 189
0.467
Y es
0.047 ***
-0.032 ***
0.271 ***
1.298 ***
2.777 ***
0.349 ***
0.822 *
2.955 ***
-0.025 **
-0.027 **
-1.591 ***
0.887 ***
-1.692 ***
0.927 ***
-1.715 ***
2.305 ***
0.060 **
Estimates
12.77
-3.58
3.56
2.83
2.79
3.61
1.65
5.60
-2.09
-1.97
-3.52
5.45
-3.30
5.51
-3.27
5.63
2.77
t − value
Spread
S. Oz
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
52
+/−
+
−
+
−
−
−
−
+
+
+
+
+/−
−
+
Y es
0.524
2, 455
0.565 *
2.611 ***
-1.032 ***
1.873 ***
-0.975 ***
-0.400 *
-0.303 ***
3.264 ***
-0.206
4.349 **
3.779 **
1.998 **
0.973 *
-0.822 **
0.387 **
Estimates
1.88
3.99
-3.74
3.13
-3.39
-1.93
-4.72
3.31
-0.75
2.39
2.55
1.96
1.66
-2.22
2.12
t − value
Y es
0.501
1, 456
0.794 **
2.337 ***
-1.070 ***
1.903 ***
-1.093 ***
-0.981 **
-0.642 ***
2.055 ***
-0.666
2.743 *
4.060 **
1.829 ***
0.560 **
-0.929 **
0.309 ***
2.45
3.16
-3.41
2.98
-2.69
-2.49
-6.18
2.92
-0.87
1.65
2.28
2.82
2.39
-2.43
4.07
Estimates t − value
Implied
Y es
0.499
2, 098
0.668 **
2.243 ***
-1.188 **
2.093 ***
-1.094 ***
-0.841 **
-0.420 ***
3.235 ***
-0.224
3.337 **
4.769 ***
1.038 ***
0.848 **
-0.154 **
0.265 **
Estimates
2.01
3.30
-2.52
3.51
-3.03
-2.53
-3.02
3.49
-1.37
2.46
2.81
2.01
2.55
-2.08
4.11
t − value
Illiquidity
Y es
0.496
2, 098
0.604 **
2.986 ***
-1.181 ***
2.088 ***
-0.937 ***
-0.431 **
-0.737 **
3.803 ***
-0.140
3.695 **
4.013 ***
2.026 ***
0.650 **
-0.161 **
0.130 **
Estimates
2.11
3.53
-2.73
3.14
-2.73
-2.57
-3.41
3.03
-1.23
2.34
2.84
2.94
2.32
-2.18
3.97
t − value
Spread
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
T ime F ixed Ef f ects
Adj. R − squared
N umber of Observations
Intercept
ABS
ABS ∗ P OST 2010
V IE
V IE ∗ P OST 2010
SIZE
M T BV
RET AIN ED
LOAN RAT IO
CHGOF F ON BS
N P L ON BS
SECIN C
DERIV AT IV ES
T IER1
V IX
P rediction
Dispersion
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on VIE (a securitizing bank’s involvement with variable interest entities), POST2010 (dummy variable for the post-FAS 166 and FAS 167 period), and control variables for the treatment
sample.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the
absolute value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock
illiquidity (ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume);
(4) bid-ask spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of the ask
and the bid, expressed in percentage terms. Independent variables: ABS is the total securitized assets scaled by total assets. VIE is a securitizing bank’s
involvement with VIEs (variable interest entities), calculated as the total assets of consolidated VIEs scaled by total assets. POST2010 is a dummy
variable equal to 1 if the observation falls between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is the market-to-book
ratio. RETAINED is the total retained interest from all asset securitizations scaled by total assets. LOAN RATIO is the proportion of on-balance
sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due on-balance sheet
loans scaled by total assets. SECINC is the securitization income and servicing fees scaled by total net income. DERIVATIVES is the total notional
amount of interest rate derivatives scaled by total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of S&P 500 index
options by the Chicago Board Options Exchange. t-statistics are based on standard errors clustered by firm and quarter-year to correct for time-series
and cross-sectional dependence respectively. ***, **, * represent statistical significance at 1%, 5%, 10% levels.
Table 7: VIE Analysis, OLS Estimation of Equation (4)
S. Oz
53
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on VIE (a securitizing bank’s involvement with variable interest entities), POST2010 (dummy variable for the post-FAS 166 and FAS 167 period), and control variables for the treatment
sample.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the
absolute value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock
illiquidity (ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume);
(4) bid-ask spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of the
ask and the bid, expressed in percentage terms. Independent variables: COMMBS is securitized commercial loans (commercial and industrial loans and
all other loans, leases, and assets) scaled by total assets. CONSBS is securitized consumer loans (home equity lines of credit, credit card receivables,
automobile loans, and other consumer loans) scaled by total assets. MBS is securitized 1-4 family residential mortgages scaled by total assets. VIE
is a securitizing bank’s involvement with VIEs (variable interest entities), calculated as the total assets of consolidated VIEs scaled by total assets.
POST2010 is a dummy variable equal to 1 if the observation falls between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is
the market-to-book ratio. RETAINED is the total retained interest from all asset securitizations scaled by total assets. LOAN RATIO is the proportion
of on-balance sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due
on-balance sheet loans scaled by total assets. SECINC is the securitization income and servicing fees scaled by total net income. DERIVATIVES is
the total notional amount of interest rate derivatives scaled by total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of
S&P 500 index options by the Chicago Board Options Exchange. t-statistics are based on standard errors clustered by firm and quarter-year to correct
for time-series and cross-sectional dependence respectively. ***, **, * represent statistical significance at 1%, 5%, 10% levels.
CONTINUED
Table 8: VIE Analysis - Robustness, OLS Estimation of Equation (5)
S. Oz
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
54
T ime F ixed Ef f ects
Adj. R − squared
N umber of Observations
Intercept
COM M BS
COM M BS ∗ P OST 2010
CON SBS
CON SBS ∗ P OST 2010
M BS
M BS ∗ P OST 2010
V IE
V IE ∗ P OST 2010
SIZE
M T BV
RET AIN ED
LOAN RAT IO
CHGOF F ON BS
N P L ON BS
SECIN C
DERIV AT IV ES
T IER1
V IX
+/−
+
−
+
−
+
−
+
−
−
−
+
+/−
+
+
+
+/−
−
+
P rediction
Y es
0.501
2, 455
3.26
4.77
-2.94
5.08
-3.30
5.26
-3.05
3.84
-3.83
-2.55
-2.71
4.33
-1.52
3.02
2.51
2.36
5.00
-4.69
16.11
Y es
0.492
1, 456
0.052 ***
1.907 ***
-1.226 ***
1.420 ***
-0.632 ***
1.102 ***
-0.426 ***
1.301 ***
-0.541 ***
-0.015 **
-0.390 ***
2.802 ***
-0.003
3.056 **
2.508 ***
0.861 ***
0.853 ***
-0.090 ***
0.049 ***
3.85
5.28
-3.03
5.17
-2.28
4.71
-2.58
3.30
-3.84
-2.40
-3.53
5.93
-1.02
2.57
3.85
5.34
3.07
-3.05
11.12
Estimates t − value
Estimates t − value
0.025 ***
2.083 ***
-0.949 ***
1.699 ***
-0.747 ***
1.369 ***
-0.493 ***
1.691 ***
-0.789 ***
-0.054 **
-0.444 ***
2.695 ***
-0.254
3.427 ***
1.503 **
0.123 **
0.289 ***
-0.006 ***
0.031 ***
Implied
Dispersion
Table 8, continued
Y es
0.454
2, 098
0.385 ***
1.923 ***
-0.992 ***
1.500 ***
-0.691 ***
1.459 ***
-0.594 ***
1.406 ***
-0.662 ***
-0.020 **
-0.342 **
2.442 ***
0.085 **
3.243 ***
2.235 ***
1.153 ***
0.093 ***
-0.007 ***
0.172 ***
Estimates
4.01
5.79
-3.47
5.94
-2.21
5.52
-3.45
3.56
-2.59
-2.51
-2.01
5.59
2.28
4.29
4.54
4.10
3.86
-3.40
13.65
t − value
Illiquidity
Y es
0.495
2, 098
0.035 ***
1.809 ***
-1.236 ***
1.785 ***
-0.729 ***
1.438 ***
-0.630 ***
1.306 ***
-0.612 ***
-0.007 **
-0.003 **
2.199 ***
0.233 *
3.122 ***
2.665 ***
0.879 ***
0.262 ***
-0.080 ***
0.037 ***
Estimates
3.11
5.75
-3.55
5.64
-2.40
5.11
-2.65
3.75
-2.59
-1.99
-1.37
5.94
1.95
4.13
3.80
3.22
4.32
-3.32
13.69
t − value
Spread
S. Oz
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
55
+/−
+
−
+
−
−
−
−
+
+
+
+
+/−
+
Y es
0.521
2, 455
0.554
1.932 ***
-1.013 ***
0.086 ***
-0.028 ***
-0.549 **
-0.891 ***
2.380 ***
-0.771
1.095
1.148
0.878 *
0.154 *
0.059 **
1.43
4.00
-2.74
3.74
-2.65
-2.21
-4.03
3.89
-1.45
0.33
0.35
1.70
1.65
1.99
Y es
0.514
1, 456
0.283
1.376 ***
-0.927 ***
0.113 ***
-0.079
-0.192 **
-0.528 ***
2.820 ***
-0.974
2.026
1.963
1.785 *
0.836 **
0.168 **
1.01
3.71
-3.35
3.81
-1.45
-2.51
-3.91
3.13
-0.84
0.31
0.91
1.77
2.45
1.95
Estimates t − value
Estimates t − value
Y es
0.497
0, 098
0.626
1.644 ***
-0.634 ***
0.093 ***
-0.058 **
-0.640 ***
-0.490 ***
2.726 ***
-0.534
0.400
3.823
1.607 *
0.319 *
-0.156 ***
Estimates
0.54
5.34
-3.13
3.27
-2.51
-2.63
-3.16
3.60
1.43
0.08
0.64
1.67
1.85
2.79
t − value
Illiquidity
Y es
0.503
2, 098
0.623
1.919 ***
-0.813 ***
0.214 ***
-0.030 ***
-0.108 **
-0.134 ***
0.918 ***
-0.587
0.963
4.012
2.687 *
0.241 ***
-0.390 **
Estimates
0.80
3.23
-3.90
2.85
-3.72
-2.25
-3.58
3.76
-1.11
0.41
0.42
1.79
2.60
2.00
t − value
Spread
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
T ime F ixed Ef f ects
Adj. R − squared
N umber of Observations
Intercept
ABS
ABS ∗ P OST 2010
RECOU RSE M CR
RECOU RSE M CR ∗ P OST 2010
SIZE
M T BV
RET AIN ED
LOAN RAT IO
CHGOF F ON BS
N P L ON BS
SECIN C
DERIV AT IV ES
V IX
P rediction
Implied
Dispersion
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on RECOURSE (a securitizing bank’s
likelihood of providing implicit recourse), POST2010 (dummy variable for the post-FAS 166/167 period), and control variables for the treatment
sample.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the
absolute value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock
illiquidity (ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume);
(4) bid-ask spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of
the ask and the bid, expressed in percentage terms. Independent variables: ABS is the total securitized assets. RECOURSE MCR is the likelihood
of a securitizing bank providing implicit recourse, calculated as tier 1 capital ratio plus tier 2 capital ratio divided by on-balance-sheet assets plus
off-balance sheet credit card receivables. POST2010 is a dummy variable equal to 1 if the observation falls between 2010Q1 and 2011Q3. SIZE is the
natural logarithm of total assets. MTBV is the market-to-book ratio. RETAINED is the total retained interest from all asset securitizations scaled
by total assets. LOAN RATIO is the proportion of on-balance sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet
scaled by total assets. NPL ONBS is past due on-balance sheet loans scaled by total assets. SECINC is the securitization income and servicing fees
scaled by total net income. DERIVATIVES is the total notional amount of interest rate derivatives scaled by total assets. TIER1 is the tier 1 risk based
capital ratio. VIX is the implied volatility of S&P 500 index options by the Chicago Board Options Exchange. t-statistics are based on standard errors
clustered by firm and quarter-year to correct for time-series and cross-sectional dependence respectively. ***, **, * represent statistical significance at
1%, 5%, 10% levels.
Table 9: RECOURSE Analysis 1, OLS Estimation of Equation (6)
S. Oz
56
+/−
+
−
+
−
−
−
+
+/−
+
+
+
+/−
−
+
Y es
0.521
2, 455
0.015
1.557 ***
-0.689 ***
1.102 ***
-0.272 ***
-0.331 *
-0.226 ***
2.255 ***
0.884 *
-0.029
0.566
-0.307 *
0.020 **
-0.124 ***
0.009 ***
1.47
5.98
-3.41
6.75
-2.71
-1.89
-2.63
3.06
1.87
-0.34
0.63
-1.65
2.48
-5.32
2.72
Estimates t − value
Y es
0.495
1, 456
0.315 **
1.406 ***
-0.444 ***
1.396 ***
-0.278 ***
-0.085 *
-0.301 ***
0.248 ***
0.424 **
-0.067
0.947
-0.175 *
0.246 **
-0.178 ***
0.008 **
Estimates
2.03
4.34
-2.85
4.09
-2.77
-1.94
-2.62
3.89
2.07
-0.03
0.55
-1.77
2.30
-4.01
2.12
t − value
Implied
Y es
0.487
2, 098
0.240 ***
1.811 ***
-0.592 ***
1.311 ***
-0.364 **
-0.428 *
-0.002 **
2.840 ***
0.972 **
-0.034
0.054
-0.116 *
0.109 ***
-0.214 ***
0.054 ***
Estimates
3.32
5.91
-2.88
5.32
-2.50
-1.78
-2.69
3.74
2.52
-0.56
0.97
-1.70
2.69
-4.11
2.85
t − value
Illiquidity
Y es
0.511
2, 098
0.023 ***
1.853 ***
-0.530 ***
1.215 ***
-0.352 **
-0.007 *
-0.287 ***
2.478 ***
0.404 *
-0.163
0.994
-0.137 *
0.286 *
-0.091 ***
0.017 ***
Estimates
5.65
5.51
-3.79
5.50
-2.48
-1.86
-2.78
3.32
1.74
-0.08
0.83
-1.83
1.71
-4.32
2.83
t − value
Spread
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
T ime F ixed Ef f ects
Adj. R − squared
N umber of Observations
Intercept
OT HER ABS
OT HER ABS ∗ P OST 2010
RECOU RSE REV
RECOU RSE REV ∗ P OST 2010
SIZE
M T BV
RET AIN ED
LOAN RAT IO
CHGOF F ON BS
N P L ON BS
SECIN C
DERIV AT IV ES
T IER1
V IX
P rediction
Dispersion
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on RECOURSE (a securitizing bank’s
likelihood of providing implicit recourse), POST2010 (dummy variable for the post-FAS 166/167 period), and control variables for the treatment
sample.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the
absolute value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock
illiquidity (ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume);
(4) bid-ask spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of the ask
and the bid, expressed in percentage terms. Independent variables: OTHER ABS is the total securitized assets minus revolving securitized loans (total
of home quity lines of credit and credit card receivables) scaled by total assets. RECOURSE REV is the likelihood of a securitizing bank providing
implicit recourse, calculated as the revolving securitized loans (total of home quity lines of credit and credit card receivables) scaled by total assets.
POST2010 is a dummy variable equal to 1 if the observation falls between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is
the market-to-book ratio. RETAINED is the total retained interest from all asset securitizations scaled by total assets. LOAN RATIO is the proportion
of on-balance sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due
on-balance sheet loans scaled by total assets. SECINC is the securitization income and servicing fees scaled by total net income. DERIVATIVES is
the total notional amount of interest rate derivatives scaled by total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of
S&P 500 index options by the Chicago Board Options Exchange. t-statistics are based on standard errors clustered by firm and quarter-year to correct
for time-series and cross-sectional dependence respectively. ***, **, * represent statistical significance at 1%, 5%, 10% levels.
Table 10: RECOURSE Analysis 2, OLS Estimation of Equation (6)
S. Oz
57
6.100 *
+
V IX
Y es
0.458
4, 827
T ime F ixed Ef f ects
Adj. R − squared
N umber of Observations
11.35
-5.55
5.48
1.97
1.85
1.85
-0.25
3.23
-4.35
-1.64
-3.01
-1.65
1.98
4.27
1.74
3, 184
0.412
Y es
0.022 ***
-0.180 ***
2.120 **
1.960 ***
10.060 ***
1.890 **
-0.023
10.290 ***
-0.240 ***
-0.060 **
-2.030 ***
-1.000 *
2.010 **
1.030 ***
0.533 **
Estimates
8.74
-4.12
1.96
4.66
3.30
1.97
-0.96
3.01
-9.42
-2.11
-3.03
-1.65
2.11
3.57
1.96
t − value
Implied
4, 189
0.435
Y es
0.447 ***
-0.317 ***
0.508 ***
3.607 ***
5.577 ***
1.120 ***
1.437 *
3.557 ***
-0.376 ***
-0.389 ***
-1.233 ***
-0.663 *
2.537 **
1.497 ***
0.862 **
Estimates
9.66
-4.53
8.78
6.61
2.72
3.12
1.63
4.38
-2.83
-3.09
-3.87
-1.88
2.14
4.26
1.88
t − value
Illiquidity
4, 189
0.417
Y es
0.024 ***
-0.110 ***
0.081 ***
3.180 ***
5.150 ***
0.693 ***
1.010
3.130 ***
-0.051 ***
-0.038 **
-1.660 ***
-1.090 *
2.110 **
1.070 ***
0.435 **
Estimates
9.53
-4.66
8.65
6.48
2.59
2.99
1.10
4.25
-2.96
-2.22
-3.00
-1.67
2.01
4.13
1.75
t − value
Spread
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
0.021 ***
-0.140 ***
−
T IER1
1.120 **
0.020 ***
+
SECIN C
-0.870
6.280 *
+/−
+
N P L ON BS
DERIV AT IV ES
+
+/−
LOAN RAT IO
CHGOF F ON BS
+
RET AIN ED
3.330 ***
-0.060 *
-0.480 ***
−
−
-1.678 ***
−
ABS ∗ P OST 2010
SIZE
-1.100 *
M T BV
2.130 **
+
−
ABS ∗ EN D CRISIS
1.267 ***
0.735 *
Estimates t − value
ABS ∗ BEG CRISIS
+
+/−
ABS
Intercept
P rediction
Dispersion
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on ABS (total securitized assets), crisis
related time dummy variables, and control variables for the treatment and control samples.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the absolute
value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock illiquidity
(ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume); (4) bid-ask
spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of the ask and
the bid, expressed in percentage terms. Independent variables: ABS is the total securitized assets scaled by total assets. BEG CRISIS. END CRISIS.
POST2010 is a dummy variable equal to 1 if the observation falls between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is
the market-to-book ratio. RETAINED is the total retained interest from all asset securitizations scaled by total assets. LOAN RATIO is the proportion
of on-balance sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due
on-balance sheet loans scaled by total assets. SECINC is the securitization income and servicing fees scaled by total net income. DERIVATIVES is
the total notional amount of interest rate derivatives scaled by total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of
S&P 500 index options by the Chicago Board Options Exchange. t-statistics are based on standard errors clustered by firm and quarter-year to correct
for time-series and cross-sectional dependence respectively. ***, **, * represent statistical significance at 1%, 5%, 10% levels.
Table 11: Additional Analysis 1 - Financial Crisis of 2007-2009 , OLS Estimation of Equation (7)
S. Oz
58
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on ABS (total securitized assets), crisis
related time dummy variables, and control variables for the treatment and control samples.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the
absolute value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock
illiquidity (ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume);
(4) bid-ask spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of the
ask and the bid, expressed in percentage terms. Independent variables: ABS is the total securitized assets scaled by total assets. 2007Q1, 2007Q2, [. . . ],
2009Q4 represent time dummy variables for each quarter for 2007, 2008 and 2009. POST2010 is a dummy variable equal to 1 if the observation falls
between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is the market-to-book ratio. RETAINED is the total retained interest
from all asset securitizations scaled by total assets. LOAN RATIO is the proportion of on-balance sheet net loans to total assets. CHGOFF ONBS
is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due on-balance sheet loans scaled by total assets. SECINC is the
securitization income and servicing fees scaled by total net income. DERIVATIVES is the total notional amount of interest rate derivatives scaled by
total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of S&P 500 index options by the Chicago Board Options Exchange.
t-statistics are based on standard errors clustered by firm and quarter-year to correct for time-series and cross-sectional dependence respectively. ***,
**, * represent statistical significance at 1%, 5%, 10% levels.
CONTINUED
Table 12: Additional Analysis 2 - Financial Crisis of 2007-2009 , OLS Estimation of Equation (8)
S. Oz
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
59
-1.211
-1.232 *
−
−
−
−
−
−
−
−
−
−
ABS ∗ 2007Q2
ABS ∗ 2007Q3
ABS ∗ 2007Q4
ABS ∗ 2008Q1
ABS ∗ 2008Q2
ABS ∗ 2008Q3
ABS ∗ 2008Q4
ABS ∗ 2009Q1
ABS ∗ 2009Q2
ABS ∗ 2009Q3
-0.541 ***
2.737 ***
−
−
+
+
+
+
M T BV
RET AIN ED
LOAN RAT IO
CHGOF F ON BS
N P L ON BS
SECIN C
-0.153 ***
−
+
T IER1
V IX
Y es
0.433
4, 827
Adj. R − squared
N umber of Observations
2.31
11.46
-5.64
6.34
1.98
2.60
2.26
-0.51
3.10
-2.88
-2.03
-3.77
1.56
1.52
1.52
1.12
2.40
2.30
2.40
2.56
2.56
2.55
2.41
2.98
4.82
0.408 ***
3, 184
0.451
Y es
0.079 ***
-0.198 ***
0.058 **
1.897 **
3.373 ***
3.872 **
-0.559
1.851 ***
-0.778 ***
-0.096 ***
-1.517 ***
-1.763 *
-1.724 *
-0.833
-0.967
1.772 ***
0.333 ***
1.885 **
1.756 ***
2.998 ***
1.086 ***
2.481 ***
2.605 ***
2.164 ***
2.80
8.91
-4.86
2.54
2.16
3.79
2.48
-1.04
3.30
-2.66
-2.98
-3.30
1.57
1.56
1.18
1.51
3.23
2.85
2.42
2.95
3.34
2.70
3.35
3.02
5.16
4, 189
0.421
Y es
0.094 ***
-0.131 ***
0.268 ***
0.840 ***
2.353 ***
2.350 ***
-0.279 *
1.581 ***
-0.277 ***
-0.128 ***
-1.339 ***
-0.145 *
-0.720
-1.715
-1.301
0.890 ***
3.424 ***
0.268 ***
3.182 ***
1.601 ***
1.142 ***
1.365 **
0.361 ***
2.998 ***
0.752 ***
Estimates
10.19
-4.16
3.14
3.08
3.20
3.83
-1.12
3.14
-3.69
-3.35
-3.20
1.58
1.55
0.59
0.61
3.13
2.65
2.94
2.56
3.23
3.20
2.48
3.87
4.90
2.98
t − value
Illiquidity
4, 189
0.412
Y es
0.096 ***
-0.263 ***
0.530 ***
0.759 ***
2.818 ***
2.821 ***
-0.526
2.848 ***
-0.755 ***
-0.026 **
-1.455 ***
-1.318 *
-0.172
-0.960
-0.450
0.467 ***
0.218 **
2.125 ***
3.066 **
1.508 **
1.477 **
1.277 ***
0.056 ***
1.150 ***
0.453 **
Estimates
9.94
-5.09
3.51
3.73
2.95
3.13
-0.13
4.73
-3.29
-3.11
-3.38
1.57
1.52
0.93
1.23
2.62
2.51
3.00
2.27
2.08
2.17
2.75
3.36
5.74
2.30
t − value
Spread
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
T ime F ixed Ef f ects
0.043 ***
0.021 ***
1.252 **
4.126 ***
4.452 **
+/−
DERIV AT IV ES
-0.055 **
−
SIZE
-0.835
-1.280 ***
−
−
ABS ∗ 2009Q4
ABS ∗ P OST 2010
-1.266
-1.211
1.044 **
0.365 **
2.111 **
2.496 ***
1.495 ***
1.543 **
2.903 **
1.820 ***
−
ABS ∗ 2007Q1
0.065 **
2.882 ***
+
+/−
Estimates t − value
Estimates t − value
ABS
Intercept
P rediction
Implied
Dispersion
Table 12, continued
S. Oz
60
+/−
+
−
−
+
−
+/−
−
−
+
+/−
+
+
+
+/−
−
+
Y es
0.458
4, 827
0.827 *
2.685 ***
-0.720
-0.956 ***
3.124 ***
1.312 ***
0.992 ***
-0.590
-0.237 ***
4.192 ***
0.093
6.673 ***
6.685 **
2.064 **
0.341 ***
-0.133 ***
0.152 ***
Estimates
2.68
4.65
-0.75
-2.87
3.88
3.09
3.84
-1.33
-3.91
3.66
0.35
2.66
2.04
2.28
6.27
-5.24
11.78
t − value
Y es
0.412
3, 184
0.910 **
2.838 ***
-0.624
-0.857 ***
2.984 ***
1.213 ***
0.942 ***
-0.099 *
-0.040 ***
10.696 ***
-0.703
2.062 **
10.076 ***
1.993 ***
2.566 ***
-0.166 ***
0.582 ***
Estimates
2.89
4.19
-1.08
-2.69
4.15
3.34
3.33
-1.75
-8.94
3.67
-0.64
2.32
3.65
5.19
2.65
-3.75
9.09
t − value
Implied
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
T ime F ixed Ef f ects
Adj. R − squared
N umber of Observations
Intercept
ABS
ABS ∗ P OST 2009
ABS ∗ P OST 2010
ILLIQU IDIT Y
ILLIQU IDIT Y ∗ P OST 2009
ILLIQU IDIT Y ∗ P OST 2010
SIZE
M T BV
RET AIN ED
LOAN RAT IO
CHGOF F ON BS
N P L ON BS
SECIN C
DERIV AT IV ES
T IER1
V IX
P rediction
Dispersion
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED) on ABS (total securitized assets), crisis related time dummy
variables, and control variables for the treatment and control samples.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the
absolute value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics. Independent
variables: ABS is the total securitized assets scaled by total assets. ILLIQUIDITY is the quarterly average of the Amihud (2002) illiquidity measure
(daily absolute return divided by the $ trading volume). POST2009 is a dummy variable equal to 1 if the observation falls between XX. POST2010
is a dummy variable equal to 1 if the observation falls between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is the
market-to-book ratio. RETAINED is the total retained interest from all asset securitizations scaled by total assets. LOAN RATIO is the proportion
of on-balance sheet net loans to total assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due
on-balance sheet loans scaled by total assets. SECINC is the securitization income and servicing fees scaled by total net income. DERIVATIVES is
the total notional amount of interest rate derivatives scaled by total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of
S&P 500 index options by the Chicago Board Options Exchange. t-statistics are based on standard errors clustered by firm and quarter-year to correct
for time-series and cross-sectional dependence respectively. ***, **, * represent statistical significance at 1%, 5%, 10% levels.
CONTINUED
Table 13: Additional Analysis 3 - Financial Crisis of 2007-2009 , OLS Estimation of Equation (9)
S. Oz
61
2.280 ***
−
+
+
+
+
RET AIN ED
LOAN RAT IO
CHGOF F ON BS
N P L ON BS
SECIN C
+
V IX
0.398
0.421
2, 822
Adj. R − squared
N umber of Observations
1, 870
Y es
0.049 ***
-0.618 ***
0.307 ***
0.108 ***
4.744 ***
2.282 **
-0.186
2.882 ***
-0.014 **
-0.057 **
-1.147 ***
2.208 ***
0.230 *
10.56
-5.21
2.68
4.84
3.54
2.42
-1.57
3.17
-2.59
-2.05
-3.10
4.04
1.83
Estimates t − value
Y es
16.09
-3.62
2.90
2.17
2.24
2.66
-1.03
3.86
-3.52
-2.46
-3.60
5.53
2.23
t − value
Implied
2, 550
0.411
Y es
0.089 ***
-0.231 ***
0.846 **
0.116 ***
3.865 ***
4.027 ***
-0.093
3.012 ***
-0.086 ***
-0.175 **
-1.268 ***
2.435 ***
0.189 **
Estimates
12.48
-4.93
2.49
3.34
3.53
3.48
-1.37
4.91
-2.87
-2.40
-3.89
5.45
2.52
t − value
Illiquidity
2, 550
0.386
Y es
0.027 ***
-0.409 ***
0.274 ***
0.297 ***
3.404 **
4.592 ***
-0.701
2.106 ***
-0.092 ***
-0.177 **
-1.231 ***
2.780 ***
0.388 ***
Estimates
12.76
-5.07
2.89
4.52
2.54
3.68
-1.36
5.26
-3.09
-2.43
-3.15
5.05
2.80
t − value
Spread
Did FAS 166 and FAS 167 Improve the Transparency of Securitizing Banks?
T ime F ixed Ef f ects
-0.474 ***
−
T IER1
0.134 ***
0.149 ***
0.157 **
3.642 **
3.810 ***
+/−
DERIV AT IV ES
-0.313 ***
−
M T BV
-0.666
-0.086 **
−
SIZE
2.815 ***
-1.086 ***
+
−
0.420 **
ABS ∗ P OST 2010
+/−
Estimates
ABS
Intercept
P rediction
Dispersion
The OLS regression of information uncertainty measures (DISPERSION, IMPLIED, ILLIQUIDITY, SPREAD) on ABS (total securitized assets), and
control variables for the treatment and control samples. For this analysis, the sample period covers 2005:Q1-2006:Q4 vs. 2010:Q1-2011:Q4.
Dependent variables are: (1) analysts’ earnings forecast dispersion (DISPERSION) is the standard deviation of quarterly analyst forecasts (with only
the latest forecast retained per analyst) issued from one day after the prior earnings announcement to 1 day before the current on, scaled by the
absolute value of the actual measure at the earnings announcement; (2) implied volatility (IMPLIED) is the given value in OptionMetrics; (3) Stock
illiquidity (ILLIQUIDITY) is the quarterly average of the Amihud (2002) illiquidity measure (daily absolute return divided by the $ trading volume);
(4) bid-ask spread (SPREAD) is the quarterly mean of the difference between the closing ask and the closing bid quotes scaled by the average of the
ask and the bid, expressed in percentage terms. Independent variables: ABS is the total securitized assets. POST2010 is a dummy variable equal to 1
if the observation falls between 2010Q1 and 2011Q3. SIZE is the natural logarithm of total assets. MTBV is the market-to-book ratio. RETAINED is
the total retained interest from all asset securitizations scaled by total assets. LOAN RATIO is the proportion of on-balance sheet net loans to total
assets. CHGOFF ONBS is the charge-offs on on-balance sheet scaled by total assets. NPL ONBS is past due on-balance sheet loans scaled by total
assets. SECINC is the securitization income and servicing fees scaled by total net income. DERIVATIVES is the total notional amount of interest rate
derivatives scaled by total assets. TIER1 is the tier 1 risk based capital ratio. VIX is the implied volatility of S&P 500 index options by the Chicago
Board Options Exchange. t-statistics are based on standard errors clustered by firm and quarter-year to correct for time-series and cross-sectional
dependence respectively. ***, **, * represent statistical significance at 1%, 5%, 10% levels.
Table 14: Additional Analysis 4 - Financial Crisis of 2007-2009, OLS Estimation of Equation (2)
S. Oz
62