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Securitization and Insider Trading
Stephen Ryan Stern School of Business
New York University (212) 998-0020 (office)
Jennifer Wu Tucker Fisher School of Accounting
University of Florida (352) 273-0214 (office)
Ying Zhou School of Business
University of Connecticut (860) 486-3019 (office)
July 2015
Forthcoming, The Accounting Review We thank Stephen Asare, Gauri Bhat (discussant), Stephen V. Brown, Michael Donohoe, Leslie Hodder (the editor), Kathy Rupar, Stanley Veliotis, James Vincent, Dushyant Vyas, two anonymous reviewers, and participants of the 2011 American Accounting Association Annual Meeting and the accounting workshops at the University of Florida, Fordham University, University of International Business and Economics, Shanghai University of Finance and Economics, and Yale University.
Securitization and Insider Trading
ABSTRACT: Securitizations are complex and opaque transactions. We hypothesize that bank insiders trade on private information about banks’: (1) securitization-related recourse risks, (2) not-yet-reported current-quarter securitization income, and (3) securitization-based business model sustainability. We provide evidence that proxies for each of these types of insider information are positively associated with insider trading. Specifically, we find that net insider sales in the 2001Q2-2007Q2 pre-financial crisis quarters predict not-yet-reported non-performing securitized loans and securitization income for those quarters and that net insider sales during 2006Q4 predict write-downs of securitization-related assets during the 2007Q3-2008Q4 crisis period. We find that net insider sales are more negatively associated with banks’ subsequent stock returns in their securitization quarters than in other quarters. In supplemental analysis, we show that the above findings are driven by trades by banks’ CEOs and CFOs, and that insiders avoid larger stock price losses through 10b5-1 plan sales than through non-plan sales. Keywords: Securitization, insider trading, opacity, banks. Data availability: All data are available from public sources.
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I. INTRODUCTION
We examine whether bank insiders exploit private information about an important type of
complex structured-finance transaction, securitization, by trading for personal gain. Financial
reporting requirements portray banks’ securitization-related risks in limited fashions, rendering
these risks opaque to users of financial reports. We expect that bank insiders, particularly top
executives, have informational advantages about these risks that enable profitable trading.
In a typical securitization, the issuer (assumed to be a bank) transfers financial assets to a
special-purpose entity (SPE), which sells asset-backed securities (ABS), i.e., claims to the future
cash flows generated by the securitized assets, to outside investors. The SPE conveys the cash
received from investors to the bank. Banks engage in securitizations for the economic purposes of
transferring risks of the securitized assets and raising funds. Banks for which securitization is a
business model, rather than one-off transactions, generally aim to earn income from securitizations
and to use the cash received to originate securitizable assets on an ongoing basis. Banks also
engage in securitizations for accounting purposes. While the applicable accounting rules have been
tightened over time, banks continue to account for most securitizations as sales with the
securitization SPEs unconsolidated. This accounting leaves the SPEs’ borrowings off the banks’
balance sheets and enables them to report gains on sale that front-load income compared to the
alternative of earning interest income on the securitized assets over time.
We examine securitizations instead of other types of complex financial transactions (e.g.,
hedging) for three reasons. First, securitization is the most common type of structured-finance
transaction. At the end of March 2007, the outstanding principal of ABS in the US equaled $8.9
trillion, compared to $5.4 trillion of corporate bonds and $4.5 trillion of US Treasuries (Cheng,
Dhaliwal, and Neamtiu 2011). Although securitization volume for most asset classes other than
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agency mortgages fell dramatically during the financial crisis, volume has begun to rebound as the
US economy and financial markets recover.1 Second, banks’ securitization-based business models
worked well before but poorly during the crisis, allowing us to examine large realizations of
downside risks on banks’ existing exposures to these transactions. Third, banks’ public quarterly
regulatory reports contain detailed and standardized data about their securitizations.
Securitization is a contractually and economically complex transaction for which the
issuing banks may be exposed to three types of risk. First, banks always retain some risk of
providing recourse on previously securitized assets (“recourse risks”). Securitizations of credit
risky assets typically create risk-layered tranches of securities and other contractual asset and
liability interests in the securitized assets. Issuing banks often retain risky and illiquid residual
securities to “credit enhance” the senior securities sold to outside investors. Alternatively, banks
may provide non-contractual (“implicit”) recourse. In essentially all securitizations, banks provide
contractual representations and warranties that the securitized assets have the characteristics
specified in the securitization prospectus. Banks violating representations and warranties are
required to buy back the assets at par or other specified amounts if requested by the purchaser.
Although historically viewed as distinct from recourse, buybacks of impaired securitized assets at
prices above fair value due to actual or alleged violations effectively amount to recourse. Second,
banks are exposed to uncertainty about their securitization income for the current period. This
income depends on the prices that investors are willing to pay for the sold ABS and the estimated
fair values of banks’ retained interests. Banks typically do not publicly disclose securitization
1 In 2006, the issuance of mortgage-related ABS was $2.6 trillion (including $1.4 trillion of non-agency mortgages) and of other ABS was $268 billion. By 2010, the issuance of mortgage-related ABS had fallen to $2.0 trillion (including only $69 billion of non-agency mortgages) and of other ABS to $106 billion. In 2013, the issuance of mortgage-related ABS was $2.0 trillion (including $108 billion of non-agency mortgages) and of other ABS was $189 billion. See http://www.sifma.org/research/statistics.aspx.
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income for a quarter until they file their financial and regulatory reports midway through the
subsequent quarter. Third, banks for which securitization is a business model are exposed to
uncertainty about the sustainability of this model, which requires continuing access to financing
on acceptable terms that can evaporate quickly in credit crunches.
Our study is motivated by and contributes to both the securitization and insider trading
literatures. The former literature finds that securitizations accounted for as sales have attributes of
both secured borrowings and sales (Niu and Richardson 2006; Landsman, Peasnell, and
Shakespeare 2008; Chen, Liu, and Ryan 2008). Issuers time these securitizations to window-dress
their balance sheets and manipulate reported earnings (Dechow and Shakespeare 2009; Dechow,
Myers, and Shakespeare 2010).2 Due to their complex economics and accounting, securitizations
contribute to banks’ opacity (Cheng et al. 2011). We extend this literature, especially Cheng et al.,
by examining how bank insiders exploit their securitization-related information advantages
through trading. The later literature finds that insiders trade before significant price movements
and the public disclosure of earnings (Lakonishok and Lee 2001; Ke, Huddart, and Petroni 2003).
Few studies in this literature examine specific sources of insiders’ information advantage,
however. A notable exception is Aboody and Lev (2000), who identify research and development
(R&D) as a source of private information and find higher insider trading profits at R&D-intensive
firms than at other firms. By examining specific sources of insiders’ private information,
researchers can identify and test for direct links between that information and insiders’ trading.
Such tests help policymakers identify gaps in required disclosures and thereby limit insider trading.
We extend this literature by examining securitizations as a source of bank insiders’ private
information.
2 Barth and Taylor (2010) question whether Dechow et al.’s (2010) findings are attributable to earnings management.
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To mitigate self-selection issues, our full sample includes bank holding companies only if
they report securitized assets outstanding or non-zero securitization income in at least one quarter
during our full sample period of 2001Q2-2007Q2. We view securitization as a feasible choice for
these “Securitization Banks.” We identify subsamples that correspond to the three types of
securitization-related risks about which we expect insiders to have private information. The
“Recourse Risk” subsample includes all bank-quarters with securitized assets outstanding at the
end of the quarter; insiders in this subsample have private information about the banks’ recourse
risks. The “Securitization Income” subsample includes all bank-quarters with non-zero
securitization income reported for the current quarter or the previous quarter; insiders in this
subsample have private information about the banks’ securitization income. The “Crisis”
subsample includes all banks with non-zero securitized assets outstanding or securitization income
in 2006Q4 on the verge of the financial crisis;3 insiders in this subsample have private information
about the (non)sustainability of the banks’ securitization business models.
We develop three hypotheses about the association between bank insiders’ securitization-
related private information and their insider trading that we test using the relevant subsamples and
proxies for bank insiders’ private information about the specific risks involved. First, we
hypothesize that bank insiders’ securitization-related private information in a quarter is
contemporaneously positively associated with their trading volume in that quarter. We test this
hypothesis using the Recourse Risk and Securitization Income subsamples. In the Recourse Risk
subsample analysis, we proxy for insiders’ private information about recourse risks using quarter-
end securitized assets, non-performing securitized loans, and retained securities, as well as charge-
3 The financial crisis unfolded in waves, with the first wave (the subprime crisis) arriving with the announcement of significant losses on subprime mortgage-related positions by New Century Financial and HSBC Holdings on February 4, 2007 (Ryan 2008).
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offs of securitized loans during the quarter. In the Securitization Income subsample analysis, we
proxy for insiders’ private information about current-quarter securitization income using the
absolute value of unexpected securitization income. As predicted, we find that these proxies are
positively associated with bank insiders’ trading volume. This association is stronger for trades by
banks’ CEOs and CFOs, who are more likely to possess private information, than by other insiders,
and for the type of securitized assets most subject to implicit recourse, revolving consumer loans,
than for other types of securitized assets.4
Second, we hypothesize that net insider sales during a quarter predict unexpected values of
specified securitization-related performance measures for that quarter or subsequent quarters that
are reported after quarter end. This hypothesis differs from the first in examining the direction of
insider trades as well as the predictive implications of these trades. We test this hypothesis using
all three subsamples. In the Recourse Risk subsample analysis, we examine whether net insider
sales predict unexpected non-performing securitized loans for the quarter. We find a significant
positive association for trades by CEOs and CFOs but not by other insiders. In the Securitization
Income subsample analysis, we examine whether net insider sales predict unexpected
securitization income for the quarter. We find a significantly negative association, again driven by
CEO/CFO trades. In contrast, we do not find any association between net insider sales and
unexpected non-securitization income. In the Crisis subsample analysis, we examine whether net
insider sales in 2006Q4 predict write-downs of securitization-related assets during the financial
crisis period of 2007Q3-2008Q4, our proxy for the breakdown of banks’ securitization-related
business model during the crisis,5 and find this is the case. Hence, bank insiders appear to trade on
4 See Chen et al. (2008) for discussion of how implicit recourse applies only to certain types of securitized assets. 5 Ideally, we would use decreases in securitization volume and/or income (rather than write-downs of preexisting securitization-related exposures) during the financial crisis as proxies for the breakdown of banks’ securitization-related business model during the crisis. Such alternative proxies are difficult to develop and employ effectively,
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specific types of securitization-related private information. In contrast, we do not find any relation
between net insider sales and write-downs of non-securitization assets during the crisis.
Third, we hypothesize that bank insiders profit from trading on securitization-related
private information. Using the Recourse Risk and Securitization Income subsamples, we find
economically and statistically significant negative abnormal stock returns for the three and six
months after insider sales. For example, the median six-month stock return is -2.4% (-3.9%) for
the Recourse Risk (Securitization Income) subsample. Abnormal returns after insider purchases
are consistently positive in both subsamples, but exhibit smaller magnitudes than after sales. The
Recourse Risk and Securitization Income subsamples exhibit significantly more negative
abnormal returns after insider sales than does a Control subsample that is comprised of
Securitization Banks’ quarters without securitization activity. Furthermore, abnormal returns are
significantly more negative after sales by CEOs and CFOs than by other insiders. Hence, bank
insiders appear to avoid significant losses by selling shares before unfavorable securitization-
related news becomes public.
The Crisis subsample provides a vivid example of insiders selling early to avoid losses.
Banks’ securitization-based business models became compromised as the performance of
subprime mortgages and other types of credit risky assets began to deteriorate before the financial
crisis and the availability of financing evaporated early in the crisis. Due to banks’ central role in
originating, holding, and securitizing these credit risky assets, bank managers were among the first
market participants to observe these adverse events. Crisis subsample banks experienced an
average raw return of -64.8% during 2007-2008, 31.7% more negative than the market and 4.7%
however, due to limitations of Y-9C report data (e.g., amounts for unaffected agency and highly affected non-agency mortgage securitizations are not disaggregated), the occurrence of non-trivial apparent “fire sale” securitization volume during the crisis, and the attrition of the 33 Crisis subsample observations as the crisis unfolds.
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more negative than similarly sized banks. Net insider sales by bank insiders in the Crisis subsample
totaled $1.19 billion in 2006Q4, over twice the amount in any other sample quarter; these sales
enabled the insiders to avoid losses of $0.99 billion during the crisis.
We conduct two supplemental analyses. First, we investigate the role of litigation risk in
securitization-related insider sales. Beginning in October 2000, the SEC allows insider sales under
10b5-1 plans that provide insiders with affirmative defenses against allegations of insider trading.
Prior research finds that insiders avoid larger losses through 10b5-1 plan sales than through non-
plan sales, consistent with these plans being used for private-information-based trades (Jagolinzer
2009; Shon and Veliotis 2013). We observe that 98.3% of plan sales for our full sample occur in
securitization quarters when we expect bank insiders to possess securitization-related private
information. We find that the significant negative stock returns after insider sales in securitization
quarters are driven by plan sales rather than by non-plan sales. Moreover, plan sales constitute
47% of sales by banks’ CEOs and CFOs but only 23% of sales by other bank insiders, enabling
CEOs and CFOs to avoid more losses than do other insiders. Hence, bank insiders appear to sell
shares under 10b5-1 plans to provide cover for information-based trades, thereby mitigating
litigation risk.
Second, we test the claim that insider trading benefits investors by impounding insiders’
private information more quickly into stock prices (Manne 1970; Boudreaux 2009) using the future
earnings response coefficient (FERC) framework (Collins, Kothari, Shanken and Sloan 1994;
Tucker and Zarowin 2006). We find that securitization and insider trading each separately reduce
the informativeness of price with respect to future earnings and that insider trading does not alter
the effect of securitization on this price informativeness. Hence, we find no evidence that
securitization-related insider trading benefits investors.
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We believe our findings have implications for policy makers and investors. Our findings
suggest that complex structured-finance transactions such as securitizations require greater
scrutiny from the SEC in enforcing insider trading rules and from investors in analyzing banks’
financial reports. In addition to their current approach of requiring firms to describe existing
securitizations in detail, the SEC and FASB should consider requiring MD&A or financial
statement note disclosures that reveal bank managers’ information about the likelihood of banks’
future losses from providing recourse on securitizations and the sustainability of their
securitization-based business models.
The rest of the paper is organized as follows. Section II develops our hypotheses. Section
III describes the sample and data. Section IV presents the research designs and test results. Section
V provides supplemental analyses. Section VI concludes.
II. HYPOTHESIS DEVELOPMENT
Since the effective date of SFAS 125 in 1997, banks account for securitizations in which
they cede control over the securitized assets as sales rather than as secured borrowings.6 Although
the FASB tightened the requirements for sale accounting with SFAS 140 and SFAS 166, in
practice banks continue to account for most securitizations as sales (Dechow et al. 2010). Under
sale accounting, banks initially recognize retained interests at fair value.7 For illiquid interests,
banks must estimate fair value using internally developed or vendor models that banks do not (and
6 For simplicity, we assume that banks do not consolidate securitization SPEs, as is usually the case, although the issuance of SFAS 167 made consolidation of certain types of SPEs (e.g., credit card master trusts and asset-backed commercial paper conduits) more common starting in 2010. Sale accounting with consolidation of securitization SPEs effectively yields secured-borrowing accounting for the consolidated entity. 7 The types of retained interests that GAAP requires to be recognized at fair value at the time of sale have expanded over time. SFAS 125 and SFAS 140 required initial fair value measurement only for retained liability interests. SFAS 156 added servicing rights and SFAS 166 added all other asset interests. Before SFAS 166, GAAP required retained asset interests not required to be fair valued to be initially recognized at relative-fair-value-based allocations of the amortized cost basis of the securitized assets at the time of sale.
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likely cannot feasibly) fully describe in their financial reports. Holding a transaction constant, the
valuation of retained interests directly and fully determines securitization income for that
transaction: a dollar higher fair value assigned to a retained asset (liability) yields a dollar higher
(lower) securitization gain on sale. For example, underestimation of default rates at the time of
securitization increases the fair value of any retained junior securities and decreases the fair value
of any recourse liability, thereby increasing the gain on sale.
SFAS 140, effective in 2001, requires banks to disclose considerable information about
their securitizations including: (1) the cash received and gains on sale recognized from securitizing
assets by major asset type; (2) the fair value of each type of retained interests at the end of each
reporting period; and (3) the sensitivity of these valuations to changes in key estimates. SFAS 166
and SFAS 167, effective in 2010, expanded these required disclosures, especially about continuing
involvements with securitized assets. Oz (2013) finds that these additional disclosures have
improved the information available to investors.
Despite these findings, significant aspects of securitization remain opaque to investors due
to four limitations of securitization disclosures. First, banks describe securitized assets in
aggregated and incomplete fashions in their financial reports and even in their securitization
prospectuses. For example, securitization prospectuses often include statistics about the
underwriting criteria (e.g., credit scores and loan-to-value ratios) that banks used in originating the
securitized assets but rarely provide information about risk layering (e.g., the combination of low
credit scores with high loan-to-value ratios).8 Second, banks’ financial reports contain very little
8 The SEC revised Regulation AB in August 2014, effective October 2014, to require extensive additional standardized asset-level disclosures about securitization pools in securitization prospectuses and on an ongoing basis. These additional disclosures pertain to contractual features of the securitization that affect the payment waterfall; credit-risk-relevant attributes of the securitized assets such as geography, property value, and loan-to-value ratio; the post-securitization performance of the securitized assets; and post-securitization loss mitigation efforts. These disclosures clearly improve the information available to investors. Due to their recency, however, the effects of these enhanced disclosure requirements have not yet been empirically evaluated.
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information about recourse risks associated with contractual representations and warranties and
non-contractual implicit recourse (Niu and Richardson 2006; Landsman et al. 2008; Chen et al.
2008; Dou, Liu, Richardson, and Vyas 2014). Third, the volume and profitability of current-period
securitizations are uncertain because they depend on (1) economic conditions such as the
receptivity of financial markets to securitization; (2) banks’ choices about whether to conduct and
how to structure securitizations, given their financing needs and risk tolerance; and (3) banks’
exercise of discretion in fair valuing retained interests and recording securitization income. Fourth,
financial reports provide little information about the sustainability of banks’ securitization-based
business models. Bank insiders likely have information advantages about all of these aspects of
securitization, but the extant literature provides no empirical evidence about whether insiders
exploit these advantages by trading for personal gain.
Prior research finds that insiders exploit their information advantages by trading. For
example, insider net purchases predict future stock returns (Lakonishok and Lee 2001) and insider
sales predict breaks in strings of consecutive quarterly earnings increases three to nine quarters
before the breaks occur (Ke et al. 2003). Because a firm’s stock price impounds all information
about the firm and its earnings measure the firm’s overall operating performance, these studies are
largely silent about the specific types of private information on which insiders trade. Identifying
these types of information is important for two reasons. First, it helps researchers determine the
control variables (e.g., risk factors or alternative sources of information) to include in empirical
models to alleviate concerns that reported results are attributable to omitted correlated variables.
Second, this identification clarifies the determinants of insider trading and thus the policy
implications of the research. For example, Aboody and Lev (2000) identify R&D as a specific
source of insider information and provide evidence that insiders’ trading profits are substantially
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larger at R&D-intensive firms than at other firms, consistent with R&D activities providing
insiders with information advantages. Aboody and Lev conclude that requiring additional
disclosures of firms’ ongoing and planned R&D activities would mitigate those advantages.
We propose three hypotheses about the associations between bank insiders’ securitization-
related private information and their trading. Each of these hypotheses reflects our expectation that
insiders receive securitization-related information before its public release and that they exploit
this private information by profitably trading. Although insiders’ private information is inherently
unobservable, our study takes a step further than extant research by testing these hypotheses using
subsamples and proxies intended to capture the private information that bank insiders possess
about three types of securitization-related risks: (1) recourse risks, (2) uncertainty about current-
period securitization income, and (3) uncertainty about securitization-based business model
sustainability. We state all hypotheses as alternatives. Online Appendix A summarizes the
hypotheses and indicates the tables that report the corresponding test results and supplemental
analyses.
We first hypothesize a positive contemporaneous association between the amount of bank
insiders’ securitization-related private information and their trading volume:
H1: Bank insiders’ securitization-related private information is positively associated
with their trading volume during the quarter.
This hypothesis is similar to Cheng et al.’s (2011) hypothesis of a positive contemporaneous
association between a bank’s securitization activity and measures of its information asymmetry.
We next hypothesize that the direction and magnitude of insider trading during a quarter
predict not-yet-reported securitization-related accounting performance measures (e.g.,
securitization income) for that quarter or subsequent quarters. Although we examine both insider
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sales and insider purchases, we expect the association to be stronger for insider sales because the
major concerns raised by securitization pertain to downside risks: Will recourse be triggered? Will
securitizations occur in the current quarter and be profitable? Is the securitization-based business
model sustainable? Because most insider trades are sales, for simplicity we state this and our third
hypotheses in terms of net insider sales (insider sales minus insider purchases).
H2: Net insider sales during quarters are negatively associated with banks’ not-yet-
reported securitization-related accounting performance for those quarters or subsequent quarters.
Tests of this hypothesis can provide more direct evidence of whether insiders trade on valuable,
private securitization-related information than do tests of our first hypothesis.
Lastly, we hypothesize that insider sales (purchases) are followed by stock price decreases
(increases), more so in banks’ securitization quarters, when insiders likely possess more
securitization-related private information, than in other quarters.
H3. Net insider sales in banks’ quarters with securitization activity are more strongly
negatively associated with the banks’ subsequent abnormal stock returns than are net insider sales in the banks’ other quarters.
III. DATA
Although securitization occurs in many industries, as in most prior research our sample
includes only bank holding companies (“banks”). Our sample period begins in 2001Q2 when the
Federal Reserve Board included Schedule HC-S, “Servicing, Securitization and Asset Sale
Activities,” in banks’ quarterly regulatory Y-9C reports. Banks with more than $150 million ($500
million) in total assets before (after) March 2006 must file these reports. Schedule HC-S requires
banks to report detailed and standardized data about their securitizations accounted for as sales in
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which they retain servicing rights or provide credit enhancement. As in Cheng et al. (2011), we
end the sample period in 2007Q2 for the Recourse Risk and Securitization Income subsample
analyses that examine insider trading during periods when banks’ securitization-based business
models appeared to perform well. We extend the sample period for our Crisis subsample analysis
of the breakdown of those business models during the financial crisis.
Panel A of Table 1 summarizes the sample selection for the full sample. We require bank-
quarter observations to have available Y-9C filings and non-missing PERMCO in the Federal
Reserve Bank of New York’s file that matches Y-9C filings to CRSP,9 yielding 11,513 initial
observations. To mitigate self-selection, we restrict the full sample to banks with securitized assets
outstanding or non-zero securitization income in at least one quarter of our sample period.
Although this restriction excludes 8,402 observations of banks for which securitization does not
appear to be a feasible choice, the remaining 3,111 observations for “Securitization Banks”
constitute 85.8% of the market capitalization of all banks on average during our sample period.
We exclude 472 observations with missing CUSIP, which is necessary to merge Y-9C data with
insider trading data from Thomson Reuters’ Insider Filing Data Feed. Following Seyhun (1992),
Rozeff and Zaman (1998), Piotroski and Roulstone (2005), and Cheng and Lo (2006), we limit
insider trades to open market purchases and sales, which are most likely to reflect insiders’ private
information.10 The Thomson Reuters database does not contain data for 639 bank-quarters; it is
unclear whether this is attributable to absence of insider trading or database incompleteness. We
exclude these observations following Lakonishok and Lee (2001), Frankel and Li (2004), and
Piotroski and Roulstone (2005). The resulting full sample includes 2,000 bank-quarters for 130
9 http://www.newyorkfed.org/research/banking_research/datasets.html. 10 Most other insider trades are between the insiders and their firms. These trades tend to be driven by stock option grants and other stock-based forms of compensation.
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unique banks, with the number of banks in a quarter varying from a maximum of 98 in 2004Q1 to
a minimum of 67 in 2006Q3 and 2007Q1-2 (untabulated).
Figure 1A depicts aggregate securitized assets outstanding and securitization income for
the full sample for each quarter of the 2001Q2-2007Q2 sample period. Aggregate securitized
assets generally increase over this period, peaking at $1.9 trillion in 2006Q4. Aggregate
securitization income fluctuates considerably over time, with the maximum being $5.5 billion in
2004Q3. Panel A of Table 2 reports summary statistics for insider sales and purchases calculated
as the number of shares traded multiplied by the trade price. Consistent with prior research, insider
sales are a large multiple of insider purchases (about 13 [33] based on the mean [median] of the
variables). Figure 1B depicts the total dollar amount of insider sales and purchases for the full
sample during each quarter of the sample period. Insider trading varies moderately over time
except for 2006Q4, when the amount is about two and half times higher than in any other quarter,
indicating unusually active insider trading on the verge of the financial crisis.
We identify three subsamples of the full sample in which bank insiders likely possess
specific types of securitization-related private information. Panels B-D of Table 1 summarize the
selection processes for these “securitization-treatment” subsamples. The Recourse Risk subsample
includes the 1,127 bank-quarters reporting a positive balance of securitized assets at the end of the
quarter, with the number of banks in a quarter varying from a maximum of 58 in 2001Q3 to a
minimum of 33 in 2007Q1 (untabulated). Because Schedule HC-S includes only securitizations
accounted for as sales in which banks retain servicing rights or provide credit enhancement, banks
reporting securitized assets generally are exposed to recourse risks. The Securitization Income
subsample includes the 535 bank-quarters with non-zero securitization income in either the current
quarter or the previous quarter, with the number of banks in a quarter varying from a maximum of
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29 in 2001Q4 to a minimum of 15 in 2006Q3 (untabulated). Of the Securitization Income
subsample observations, 89.3% also belong to the Recourse Risk subsample. 11 The Crisis
subsample includes the 33 banks reporting either a positive balance of securitized assets or non-
zero securitization income in the fourth quarter of 2006 on the verge of the financial crisis; all of
these observations appear in one or both of the Recourse Risk and Securitization Income
subsamples.
We refer to the bank-quarter observations in the full sample that do not belong to either the
Recourse Risk or Securitization Income subsamples as the Control subsample. We contrast the
Recourse Risk and Securitization Income subsamples to the Control subsample in the analyses of
the profitability of insider trading. We do not contrast the Crisis subsample to the Control
subsample, however, because we do not expect the time-distributed Control sample observations
to be comparable to banks with securitization exposures in a single quarter on the verge of the
financial crisis.
IV. PRIMARY ANALYSES
Tests of Hypothesis 1
H1 predicts that the amount of bank insiders’ securitization-related private information is
positively associated with insider trading volume during the quarter. We test this hypothesis by
estimating this equation using the Recourse Risk and Securitization Income subsamples:
NetTradet = a0 + a1Securitizationt + a2 log(MVEt) + a3 log(Turnovert) + a4MBt
+ a5|ΔROAt| + a6|PastRett| + a7|FutureRett| + a8 StdRett + a9StockCompt
11 Securitization income in a quarter may be zero for banks with non-zero securitized assets in the quarter, and vice versa, for various reasons. For example, some banks have positive securitized assets in a quarter from securitizations in prior quarters but conduct no securitizations and thus earn no securitization income in the current quarter. Some banks record zero securitization income despite conducting securitizations in a quarter. Some banks conduct securitizations during a quarter for which they do not retain servicing rights or provide credit enhancement and so do not disclose the securitized assets on Schedule HC-S.
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+ a10ExecOwnershipt + a11StockCompt*ExecOwnershipt + et. (1) Following Piotroski and Roulstone (2004), the dependent variable is NetTrade, the absolute dollar
amount of sales minus purchases by all of the bank’s insiders during the quarter, multiplied by 100
for presentation purposes, and divided by beginning-of-quarter market value of equity.12 NetTrade
is unaffected by equal increments to insider sales and purchases for a bank in a quarter, and thus
treats such increments as having perfectly offsetting implications.
The primary explanatory variable, Securitization, stands in for our proxies for bank
insiders’ securitization-related private information, which differ across the subsample analyses. In
the Recourse Risk subsample analysis, we proxy for this information using four variables
identified by Cheng et al. (2011): (1) quarter-end securitized assets, SB, which captures the size of
the exposure to recourse risks; (2) quarter-end non-performing securitized loans, NPSL, and (3)
net charge-offs of securitized loans during the quarter, Chgoff_Sec, two variables that capture the
recent performance of the securitized assets; and (4) quarter-end retained securities from
securitizations, Retained, which captures the extent of the primary form of credit enhancement.
We expect banks’ recourse risks to increase with each of these proxies. Bank insiders’ private
information involves knowing the values of these variables before their public disclosure as well
as the implications of the variables for the likelihood that the bank provides recourse on the
securitized assets and the amount of losses resulting from that provision.
In the Securitization Income subsample analysis, to develop the proxy for the amount of
bank insiders’ securitization-related private information, we first estimate this equation predicting
securitization income, SI:
12 Piotroski and Roulstone (2004) scale their measure by trading volume. We scale instead by beginning-of-quarter market value of equity because trading volume trends upward strongly over time with the growth in high-frequency trading after their sample period ends in 2000.
17
SIt = a0 + a1SIt-1 + a2SBt-1 + a3Q1t +a4Q2t + a5Q3t + year fixed effects + et. (2)
On the right hand side of Equation (2), we include securitization income for the previous quarter,
which we expect to be the best predictor of current-quarter securitization income. Following
Dechow et al. (2010), we divide securitization income by beginning-of-quarter book value of
equity. We include beginning-of-quarter securitized assets to capture the strong tendency for banks
that have previously conducted securitizations to continue to do so. Finally, we include fiscal
quarter dummies to control for seasonality and year fixed effects to control for macroeconomic
and financial market factors. We measure unexpected securitization income, USI, as the estimated
residual in Equation (2) and use the absolute value of unexpected securitization income, |USI|, as
the proxy for the amount of bank insiders’ securitization-related private information. Because this
proxy plays a more central role in the test of H2 than in the test of H1, we discuss the estimation
of Equation (2) in the section devoted to the test of H2.
Following Piotroski and Roulstone (2004) and Cheng and Lo (2006), we control for the
following variables in Equation (1). We control for the natural logarithm of banks’ beginning-of-
quarter market value of equity, log(MVE), which we expect to be negatively associated with insider
trading because larger firms tend to have stronger corporate governance and higher media scrutiny.
We control for banks’ stock liquidity using the natural logarithm of trading volume divided by
beginning-of-quarter number of shares outstanding, log(Turnover), because liquidity affects
insiders’ ability to trade without moving the price. We control for beginning-of-quarter market-to-
book ratio, MB, and for the absolute value of the change in quarterly net income from the previous
quarter to the current quarter divided by beginning-of-quarter total assets, |ΔROA|, because we
expect insiders of banks with higher growth and more variable profitability to have greater
18
information advantages. We control for the absolute value of the bank’s stock return in the previous
quarter minus the index return of banks of similar size, |PastRet|, because prior research finds that
insiders are more likely to trade after larger price movements.
We also control for the absolute value of the bank’s return minus the average return of
banks of similar size in the 12 months following the current quarter, |FutureRet|, to capture
insiders’ private information about banks’ future performance unrelated to securitization (Ke et al.
2003). We control for the standard deviation of daily stock returns in the 12 months before the
current quarter begins, StdRet, because managers may trade to reduce their exposure to bank risks
other than recourse risks. We control for the fraction of the bank’s shares owned by its reportable
executives at the end of the most recent fiscal year, ExecOwnership, and for the dollar value of
restricted stock and option grants divided by total compensation in the most recent fiscal year
averaged across these executives, StockComp, because managers with high stock ownership or
recent stock-based compensation tend to trade to reduce this exposure (Cziraki 2015; Ofek and
Yermack 2000). We further include the interaction of ExecOwnership and StockComp to capture
the effect of the combination of stock ownership and stock-based compensation on insiders’
tendency to trade. We are able to calculate ExecOwnership and StockComp using data from
ExecuComp for 995 observations (about 50% of the full sample); we hand-collect data for the
remaining observations from the banks’ proxy statements.
Panel B of Table 2 reports descriptive statistics for the variables in Equation (1). In the
Recourse Risk subsample, securitized assets (undeflated SB) on average equal a sizeable 21.5% of
total assets. The other recourse-risk proxies have considerably smaller values. In the Securitization
Income subsample, securitization income (undeflated SI) equals 34.3% of net income on average
19
(untabulated). Many of the variables have skewed distributions, as evidenced by the large
differences between their means and medians.
Panel C of Table 2 reports pairwise Pearson and Spearman correlations for the full sample.
Because of data skewness, we discuss only the Spearman correlations. NetTrade is negatively and
insignificantly correlated with SB and |USI|, respectively, apparently inconsistent with H1. These
correlations are likely driven by bank size: log(MVE) is highly negatively correlated with NetTrade
but highly positively correlated with SB and |USI|.
Panel A of Table 3 reports the estimation of Equation (1) using robust regression to mitigate
the effects of the data skewness. Robust regression iteratively reweights observations until the
estimated coefficients converge. This method is superior to traditional methods of dealing with
outliers, such as winsorization and truncation, because it identifies and assigns weights to outliers
in a multivariate distribution (Anderson 2008; Leone et al. 2012). The first four columns of the
panel use the Recourse Risk subsample with the four proxies for bank insiders’ securitization-
related private information about recourse risks included one at a time. Consistent with H1, the
coefficients on SB, NPSL, Chgoff_Sec, and Retained are significantly positive at 0.004 (t=2.18),
0.094 (t=2.84), 1.841 (t=4.44), and 0.320 (t=3.15), respectively. The fifth column uses the
Securitization Income subsample with |USI| as the proxy for uncertainty about current-quarter
securitization income. Again consistent with H1, the coefficient on |USI| is significantly positive
at 0.848 (t=14.43). The control variables log(MVE), log(Turnover), and MB have consistently
significant coefficients of the predicted signs across the models, except for the positive but
insignificant coefficient on MB in the Securitization Income subsample analysis. The coefficients
on the other control variables are less consistently significant with the predicted sign.13 Overall,
13 We expect the coefficient on |ΔROA| to be positive. Results in the Recourse Risk subsample analysis are largely consistent with this expectation, but this coefficient is insignificant in the Securitization Income subsample analysis.
20
the results reported in Panel A of Table 3 indicate that insiders trade more when they possess better
securitization-related private information.
We refine the above analyses in two ways to probe our interpretation of the positive
estimated coefficients on the Securitization proxies in Equation (1) as attributable to bank insiders
trading on securitization-related private information. First, we decompose insider trades into those
by CEOs and CFOs versus by other insiders, because prior research provides evidence that CEOs
and CFOs possess better private information than other insiders. For example, Cheng and Lo
(2006) find that the number of bad-news earnings forecasts is more strongly positively associated
with planned purchases by CEOs than by other insiders. Shon and Veliotis (2013) find a significant
positive association between firms meeting or beating analyst earnings expectations and planned
sales after earnings announcements only by CEOs and CFOs. Based on this research, we expect
H1 holds more strongly for trades by CEOs and CFOs than by other insiders. In untabulated tests,
we jointly estimate two regressions, one with trades by CEOs and CFOs as the dependent variable
and the other with trades by other insiders as the dependent variable, using seemingly unrelated
regression. We find that the coefficients on the Securitization proxies are significantly more
positive in the CEO/CFO regression than in the other-insider regression.
Second, we decompose securitized assets into types that we expect differentially expose
banks to implicit recourse. Chen et al. (2008) argue, based on structural differences across
securitizations of different types of loans and empirical research examining banks’ provision of
implicit recourse, that banks provide implicit recourse in securitizations of revolving consumer
The coefficient on |PastRet| is weakly significantly negative in most of the models. We expect the coefficient on |FutureRet| to be positive and find a weakly positive coefficient in the Securitization Income subsample analysis but a negative coefficient in the Recourse Risk subsample analysis. The coefficient on StdRet is insignificant. The coefficient on ExecOwnership is significantly positive as expected in the Recourse Risk subsample analysis, but not in the Securitization Income subsample analysis. The interaction term of StockComp and ExecOwnership is significantly positive only in the Securitization Income subsample analysis.
21
loans (e.g., credit card receivables and home equity lines of credit) due to the use of master trusts
and early amortization provisions, but not in securitizations of mortgages and most other types of
loans.14 We expect bank insiders’ information advantages to be stronger about implicit recourse
than about contractual recourse. Accordingly, we expand Equation (1) by decomposing SB into
securitized residential mortgages, SBM, consumer loans, SBCON, and commercial loans, SBCOM.
We expect more positive coefficients on SBCON than on SBM or SBCOM.
Panel B of Table 2 reports that, as a percentage of banks’ total assets (SB), SBM averages
15.9% (74.0%), SBCON averages 4.7% (21.9%), and SBCOM averages only 0.9% (4.2%). Panel
B of Table 3 reports the estimation of the expansion of Equation (1) replacing SB with SBM,
SBCON, and SBCOM. As expected, the coefficient on SBCON is significantly positive and
significantly higher than the coefficient on SBM (F=17.27). Likely due to SBCOM’s small
magnitude, the coefficient on SBCOM has a large standard error that renders all differences of
coefficients involving SBCOM insignificant. The results of these refined analyses provide further
support for our interpretation of the positive coefficients on the Securitization proxies in the
estimation of Equation (1) as attributable to bank insiders trading on their securitization-related
private information.
Tests of Hypothesis 2
H2 predicts that higher net insider sales in a quarter indicate that securitization-related
accounting performance for that quarter or subsequent quarters will, when subsequently reported,
be found to be worse on average. We test this hypothesis by examining the association of net
14 Residential mortgages and other consumer loans generally are homogeneous, whereas commercial loans generally are heterogeneous. All else being equal, bank insiders’ information advantage should be greater for heterogeneous loans than for homogeneous loans. Because commercial loan securitizations are relatively small, we do not focus on this source of insiders’ information advantage.
22
insider sales with three securitization-related performance measures using the most relevant
subsamples. First, we examine the unexpected component of non-performing securitized loans
(UNPSL), a timely measure of the performance of securitized loans and thus of the likelihood and
expected cost of banks providing recourse, using the Recourse Risk subsample. We model
expected NPSL below. Second, we examine the unexpected component of securitization income
(USI, the residual in Equation (2)) using the Securitization Income subsample. Finally, we examine
write-downs of securitization-related assets during the financial crisis, our measure of the
breakdown of banks’ securitization-based business models during the crisis, using the Crisis
subsample. Unlike for the first two performance measures, we do not model the expected
component of these write-downs because they should be largely unpredictable if banks accurately
measure securitization-related assets each quarter and because the infrequency of write-downs
before the crisis makes it difficult to implement an expectation model. We include additional
control variables in this test, however, to compensate for the absence of an expectation model for
write-downs.
The expectation model for NPSL is:15
NPSLt = a0 + a1NPSLt-1 + a2SBt-1 + a3Q1t + a4Q2t + a5Q3t + year fixed effects + et. (3)
We include lagged NPSL on the right hand side of Equation (3) to capture serial correlation in the
performance of securitized loans, which we expect to arise for various reasons including: (1)
different loan types exhibit different levels of delinquencies (Ryan 2007, Exhibit 5.6); (2) different
15 We obtain similar results testing H2 using the residual from the estimation of a modified version of Equation (3) in which we define the dependent variable as the change in NPSL from the beginning of quarter t to the end of quarter t+2 plus the sum of loan charge-offs during quarters t, t+1, and t+2. If charge-offs during a period equal zero, the change in NPSL equals the amount of loans that become delinquent, net of any cure of prior delinquencies, during the period. Charge-offs decrease NPSL by the book value of the charged-off loans. Thus, the change in NPSL plus charge-offs during a period reflect loans that either become delinquent, net of cure, or that migrate from delinquency to charge-offs during the period.
23
banks exhibit different underwriting quality; and (3) different pools of securitized loans exhibit
different risk attributes such as geographical and industry concentrations. We include lagged SB
to capture the mathematical fact that, holding delinquency rates constant, banks with higher
securitized assets have higher NPSL. We again include fiscal quarter dummies to control for
seasonality and year fixed effects to control for macroeconomic and financial market factors.
Panel A of Table 4 reports the estimation of Equation (3) using the Recourse Risk
subsample. As expected, the coefficient on lagged NPSL is significantly positive. The coefficient
on SB is insignificant, however, suggesting that lagged NPSL adequately captures both the
delinquency rate and the securitized assets subject to that rate. UNPSL is the estimated residual in
this equation.
Panel A of Table 5 reports the estimation of Equation (2) using the Securitization Income
subsample. As expected, the coefficients on SI and SB are both significantly positive.
We test H2 by regressing the securitization-related performance measures UNPSL and USI
on NetSale:
UNPSLt = b0 + b1 NetSalet + et. (4) USIt = b0 + b1 NetSalet + et. (5)
We also estimate expanded versions of Equations (4) and (5) in which NetSale is decomposed into
one or both of Sale versus Buy and trades by CEOs and CFOs versus by other insiders.
Panel B of Table 4 reports the robust-regression estimation of Equation (4) using the
Recourse Risk subsample. NetSale is the sole explanatory variable in Column 1 and is decomposed
in Columns 2-4. In Columns 1 and 2, NetSale and its Sale and Buy components have insignificant
coefficients, inconsistent with H2. Decomposing NetSale, Sale, and Buy into trades by CEOs and
24
CFOs (NetSale_Exec, Sale_Exec, and Buy_Exec) versus by other insiders (NetSale_Other,
Sale_Other, and Buy_Other) in Columns 3 and 4, we find significantly positive coefficients on
NetSale_Exec and Sale_Exec but insignificant coefficients on the other variables. These results
indicate that trades by CEOs and CFOs, especially sales, predict the unexpected component of
subsequently reported NPSL for the quarter, consistent with H2 and with top executives having
and trading on private information about the performance of securitized loans.
Panel B of Table 5 presents the robust-regression estimation of Equation (5) using the
Securitization Income subsample and the same decompositions of NetSale and column structure
as in Panel B of Table 4.16 In Column 1, the coefficient on NetSale is significantly negative,
indicating that NetSale predicts unexpectedly low securitization income for the quarter, consistent
with H2. Decomposing NetSale into Sale and Buy in Column 2 shows that the significant
association reported in Column 1 is driven by insider sales, not purchases. In Columns 3 and 4, we
find significantly negative coefficients on NetSale_Exec and Sale_Exec and a significantly positive
coefficient on Buy_Exec, but insignificant coefficients on the trades by other insiders, indicating
that the significant association reported in Column 1 is driven by trades of CEOs and CFOs. These
results are consistent with H2 and with top executives having and trading on private information
about current-quarter securitization income.
To rule out the possibility that the ability of NetSale to predict the unexpected component
of securitization income simply reflects bank insiders trading on their private information about
16 The timing of the arrival of securitization news for a quarter varies across banks, coming from as early as the earnings announcement date to as late as the Y-9C filing date, which is required to be no later than 45 days after the quarter end. In our primary tests, we measure insider trades within the quarter. To test the sensitivity of our results to this choice and to better align the measure of insider trades with the subsequent arrival of securitization news, we alternatively measure insider trades from one day after the earnings announcement for the previous quarter to one day before the earnings announcement for the current quarter. The use of this alternative insider trading window does not alter the results, reflecting the fact that 95% of the trades in this alternative window occur during the fiscal quarter involved.
25
the bank’s bottom-line net income (Ke et al. 2003), we conduct parallel analysis using non-
securitization income, NSI, defined as net income minus securitization income divided by
beginning-of-quarter book value of equity (Dechow et al. 2010). Panel C of Table 5 reports the
estimation of an expectation model for NSI that parallels Equation (2) for SI. We measure the
unexpected component of NSI as the residual from this estimation, UNSI. Panel D of Table 5
reports the robust-regression estimation of Equation (6) replacing USI with UNSI. Columns 1-3
report that this estimation yields insignificant associations of UNSI with NetSale, its Buy and Sale
components, and its NetSale_Exec and NetSale_Other components. Column 4 reports a significant
negative coefficient on Sale_Other, however, indicating that other insiders sell shares before the
release of negative news about NSI. The contrasting results for USI and UNSI suggest that
securitization is an especially significant source of information advantage for bank insiders,
particularly top executives.
We also test H2 using write-downs of securitization-related assets during 2007Q3-2008Q4
by the Crisis subsample of 33 banks. We collect data on these write-downs from Bloomberg’s
WDCI (Write-Down and Capital Infusion) database, which contains material quarterly write-
downs by type reported by financial institutions during the crisis. For banks not covered by WDCI,
we hand collect these write-downs from their Form 10-K filings. In aggregate, Crisis subsample
banks reported total asset write-downs of $291 billion during the 2007Q3-2008Q4 period we
examine.17 WDCI distinguishes 18 types of write-downs, all of which largely or entirely involve
financial instruments.18 We classify these write-down types as securitization-related versus non-
securitization-related as follows. The only apparent non-securitization-related asset is loans held
17 For two sample banks that were acquired during 2007Q3-2008Q4, we calculate write-downs only up to the quarter before the acquisition to exclude discretionary “cleaning the decks” write-downs that often are recorded upon the arrival of new management. 18 For example, the WDCI help function indicates that write-downs of goodwill are not included.
26
for investment; this classification implies that banks do not currently intend to securitize the loans.
We define write-downs of non-securitization-related assets, Writedown_NS07Q3-08Q4, as cumulative
excess loss provisions for these loans divided by loans held for investment at quarter-end
2006Q4.19 WDCI includes an unspecified category that we do not treat as either securitization
related or non-securitization related; our results are not affected by this choice. We define write-
downs of securitization-related assets, Writedown_S07Q3-08Q4, as the sum of WDCI’s 16 other types
of write-downs divided by the sum of trading securities, available-for-sale securities, held-to-
maturity securities, and loans held for sale at quarter-end 2006Q4.20
We regress Writedown_S07Q3-08Q4 on net insider sales during 2006Q4, NetSale06Q4. We
include two control variables: (1) the logarithm of total assets at quarter-end 2006Q4, TA06Q4, to
capture size-related effects; and (2) loans held for investment divided by total assets at quarter-end
2006Q4, Loan06Q4, to capture banks’ willingness and ability to hold loans on balance sheet.
Writedown_S07Q3-08Q4 = c0 + c1NetSale06Q4 + c2log(TA06Q4) + c3Loan06Q4 + e. (6)
We also estimate an expanded version of Equation (6) in which NetSale06Q4 is decomposed into
Sale06Q4 and Buy06Q4.21 The mean of NetSale06Q4 is $36 million, which equals Sale06Q4 of $43
million minus Buy06Q4 of $7 million (untabulated). For comparison purposes, we estimate Equation
(6) with Writedown_NS07Q3-08Q4 as the dependent variable. The means of Writedown_S07Q3-08Q4 and
Writedown_NS07Q3-08Q4 are $3.4 billion and $3.9 billion, respectively (untabulated).
19 The WDCI help function indicates that its write-downs for loan held for investment equal the excess of the bank’s provision for loan losses during the quarter over its provision for loan losses in 2006Q4. 20 The asset types that contribute most to Writedown_S07Q3-08Q4 are collateralized debt obligations (28%), subprime residential mortgage-backed securities (16%), collateralized loan obligations (11%), and commercial mortgage-backed securities (8%). 21 Because of the small size of the Crisis subsample, we do not decompose insider trades into those by CEOs and CFOs versus other insiders.
27
Columns 1 and 2 (3 and 4) of Table 6 report Tobit estimation of Equation (6) with
Writedown_S07Q3-08Q4 (Writedown_NS07Q3-08Q4) as the dependent variable. 22 Even though the
sample includes only 33 observations, yielding low-power tests, Column 1 reports a significantly
positive coefficient of 0.159 (t=2.43) on NetSale06Q4, and Column 2 reports a significantly positive
coefficient on Sale06Q4 of 0.140 (t=2.10) and a significantly negative coefficient on Buy06Q4
of -0.233 (t=-2.31). These results are consistent with H2 that insider sales (purchases) on the verge
of the crisis predict larger (smaller) write-downs of securitization-related assets during the crisis.
In contrast, the coefficients on the insider trading variables in Columns 3 and 4 are insignificant,
inconsistent with trading by bank insiders on the verge of the financial crisis pertaining to the
deterioration of banks’ traditional banking activities such as lending.
Tests of Hypothesis 3
H3 predicts that net insider sales in Securitization Banks’ quarters with securitization
activity indicate subsequent negative abnormal stock returns more strongly than do net insider
sales in these banks’ other quarters. We test this hypothesis using two approaches: (1) calculating
median abnormal stock returns after individual insider trades following Aboody and Lev (2000)
and (2) estimating multivariate regression models at the bank-quarter level following Lakonishok
and Lee (2001).23 In each approach, we compare the abnormal returns for the Recourse Risk and
Securitization Income subsamples to those for the Control subsample.
In the individual trade-level approach, following Jagolinzer (2009) we calculate buy-and-
hold abnormal stock returns for the three and six months following each insider trade during a
quarter. We do not examine Jagolinzer’s one-month return window because it often excludes
22 Untabulated robust-regression (linear) estimation of Equation (6) yields the same inferences. 23 Aboody and Lev (2000) also use a rolling calendar-time portfolio approach that is inappropriate for our study due to our relatively short sample period.
28
banks’ first disclosure of securitization information in a quarter, which may occur as late as the
required filing of regulatory Y-9C reports by 45 days after quarter end.24 We calculate abnormal
stock returns as a bank’s buy-and-hold return minus the buy-and-hold return for a size-stratified
banking-industry index over the same window. Specifically, we sort banks into above- versus
below-median size groups in each quarter based on the banks’ beginning-of-quarter market
capitalization. We calculate average returns for each group for each day during the quarter. The
index return for a bank in a quarter is the return during the quarter for the group to which the bank
belongs at the beginning of the quarter. This index return removes returns attributable to common
factors affecting comparable banks, an improvement over prior research that typically uses a
market index return (Aboody and Lev 2000; Ke et al. 2003; Jagolinzer 2009).25
Panel A (Panel B) of Table 7 reports median abnormal stock returns for the three- and six-
month windows after sales and purchases by all insiders, CEOs and CFOs only, and other insiders
for the Recourse Risk (Securitization Income) and corresponding Control subsample. Statistically
significant median abnormal returns are in boldface. The “between-group test statistic” indicates
the significance of differences between the median abnormal returns for the securitization-
treatment and Control subsamples. If insiders trade solely for liquidity or portfolio-rebalancing
purposes, we expect median abnormal returns to be zero. If insiders instead trade on private
information, we expect median abnormal returns to be negative (positive) after insider sales
(purchases).
In the top part of Panel A examining trades by all insiders, abnormal stock returns are
significantly negative after insider sales and significantly positive after insider purchases in both
24 Aboody and Lev (2000) also examine a 12-month windows for which we find insignificant results, likely due to the noise resulting from extending the window. 25 Our results are similar or stronger using a market index or overall bank index.
29
the three- and six-month return windows in the Recourse Risk subsample. In the Control
subsample, abnormal stock returns are significantly negative after insider sales but insignificant
after insider purchases in both return windows. Insider trading is significantly more profitable in
the Recourse Risk subsample than in the Control subsample for the three- and six-month windows
after purchases and the six-month window after sales, consistent with H3, although the difference
in returns between the Recourse Risk and Control subsamples in the three-month window after
sales is insignificant. Overall, these results suggest that trades by insiders of Securitization Banks
are more likely to be based on private information in quarters with securitization activity than in
other quarters.
The middle (bottom) portion of Panel A reports the same analyses for trades by CEOs and
CFOs (other insiders). While sales and purchases by CEOs and CFOs and other insiders are all
profitable, trades by CEOs and CFOs are considerably more profitable than trades by other
insiders. For example, the abnormal stock return in the six-month window is -4.6% for sales by
CEOs and CFOs compared to -1.3% for sales by other insiders, and 4.3% for purchases by CEOs
and CFOs compared to 0.0% for purchases by other insiders. As for trades by all insiders, trades
by CEOs and CFOs are more profitable in the Recourse Risk subsample than in the Control
subsample for the three- and six-month windows after purchases and the six-month window after
sales. In contrast, trades by other insiders do not exhibit consistently different profitability across
the Recourse Risk and Control subsamples. These results are consistent with our expectation that
top executives such as CEOs and CFOs have better securitization-related private information and
thus trade more profitably than other insiders.
Panel B (Panel C) of Table 7 reports similar analyses of median abnormal stock returns
following trades by bank insiders for the Securitization Income and Control subsamples (the Crisis
30
subsample). To conserve space, we do not discuss these results, which yield similar inferences as
the Panel A results. The significance of the results for the Crisis subsample is striking given the
small size of this sample.
Online Appendix B reports the details of our bank-quarter-level multivariate regression
models, which yield similar inferences as the individual trade-level approach. Briefly, for the
Recourse Risk, Securitization Income, and Control subsamples, we regress 3-month and 6-month
abnormal stock returns on either NetSale or Sale and Buy as well as control variables. For both of
these securitization-treatment subsamples, we find that the coefficients on NetSale and Sale are
significantly negative and the coefficient on Buy is significantly positive, consistent with insider
trading in securitization quarters being profitable. In contrast, for the Control subsample the
coefficients on these insider trading variables are all insignificant. For the Crisis subsample, we
regress abnormal stock returns during the financial crisis period from January 1, 2007 to April 30,
2009 on the same sets of insider trading variables in 2006Q4. Again despite the small sample size,
we find that the coefficient on NetSale (Sale) is significantly negative at the 10% level in a one-
tailed (two-tailed) test, weak evidence of profitable insider trading on the verge of the crisis.
V. SUPPLEMENTAL ANALYSES
Litigation Risk and 10b5-1 Plans
Although a matter of ongoing interpretation and dispute, insider trading is in principle
prohibited, being subject to SEC enforcement and litigation under SEC Rule 10b-5 issued in 1942
pursuant to the SEC’s authority under Section 10(b) of the Securities Exchange Act of 1934. Ke
et al. (2003) provide evidence that insiders avoid litigation by reverting their stock sales to normal
levels two quarters before informational events. SEC Rule 10b5-1, issued in August 2000, defines
illegal insider trading as insiders possessing non-public information when they trade. Section (c)
31
of this rule provides an affirmative defense for transactions under pre-arranged trading plans,
referred to as “10b5-1 plans.” Jagolinzer (2009) finds that insiders sell their shares under these
plans from one to six months before firms report unexpectedly poor performance and that plan
sales avoid larger losses than do non-plan sales. Shon and Veliotis (2013) find that CEOs and
CFOs manipulate earnings to meet or beat market expectations before plan sales. These studies
suggest that 10b5-1 plans are often used by insiders to reduce their litigation risk by providing
cover for their private-information-based trading.
We examine the extent to which insiders in our sample trade under 10b5-1 plans and
whether plan sales avoid larger losses than do non-plan sales. As do Shon and Veliotis (2013), we
obtain data on insiders’ trades under 10b5-1 plans from j3sg.com,26 which begins coverage on July
1, 2003, when insiders were first required to file Form 4 electronically after trading. Insiders are
not required to disclose whether their trades are under a 10b5-1 plan, however, or the details of
any plan.27 All plan trades by insiders of our sample banks are sales.
Panel A of Table 8 reports the dollar amount of plan sales by all insiders, by CEOs and
CFOs, and by other insiders of Securitization Banks in each quarter of 2003Q3-2007Q2 and in
total across this period. Plan sales constitute 47% of sales by CEOs and CFOs but only 23% of
sales by other insiders. Over 98% of plan sales by insiders occur in banks’ quarters with
securitization activity (untabulated), i.e., in the Recourse Risk or Securitization Income
subsamples, even though these quarters comprise fewer than 60% of the banks’ quarters. These
statistics suggest that CEOs and CFOs are more concerned than other insiders about litigation risk
26 The j3sg.com database is less comprehensive than the Thomson Reuters database used in our earlier analyses. For example, for the period over which the two databases overlap, the number of bank-quarters (dollar amount of insider trades) in the j3sg.com database is 63.7% (55.8%) of that in the Thomson Reuters database. 27 Jagolinzer (2009) observes that 18% of plan trades are not disclosed as such and that insiders predominantly use 10b5-1 plans for sales, not purchases.
32
but that both types of insiders use 10b5-1 plans to provide cover for private-information-based
sales.
To investigate whether insiders use 10b5-1 plans to mitigate litigation risk, we exploit the
fact that a large stock price drop following an insider trade increases the insider’s exposure to
litigation (Lev and de Villiers 1994). In Panel B of Table 8, we report the median abnormal stock
returns in the three and six months after plan sales and non-plan sales in the same quarters by all
insiders, by CEOs and CFOs only, and by other insiders. The panel reports significantly negative
median abnormal stock returns in both return windows after plan sales, but insignificant (in one
case significantly positive) returns after non-plan sales, by all three groups of insiders. The
abnormal returns after plan sales are significantly more negative than those after non-plan sales.
Moreover, the stock returns after plan sales by CEOs and CFOs are considerably more negative
than those after plan sales by other insiders (untabulated). These results suggest that insiders,
especially CEOs and CFOs, use 10b5-1 plans to provide cover for their trading on securitization-
related private information.
Does Insider Trading Benefit Investors through Enhanced Price Informativeness?
This details of analysis are reported in Online Appendix C. Briefly, we expand the future
earnings response coefficient (FERC) framework (Collins, Kothari, Shanken and Sloan 1994;
Tucker and Zarowin 2006) to include interactions of the explanatory variables with securitized
assets, SB, an indicator for high insider trading volume, and the interaction of these two variables.
We find that securitization and insider trading each separately reduce the informativeness of price
with respect to future earnings and that insider trading does not alter the effect of securitization on
this price informativeness. Hence, we find no evidence that securitization-related insider trading
benefits investors.
33
VI. CONCLUSION
Securitizations are complex and opaque transactions about which bank insiders have
significant information advantages. We investigate whether and if so how insiders exploit specific
types of securitization-related private information by trading for personal gain. For our sample of
Securitization Banks, we find that the amount of securitization-related insider information is
positively associated with insider trading volume during the quarter, more so for trades by CEOs
and CFOs than by other insiders and for the type of securitized assets most subject to implicit
recourse. Net sales by CEOs and CFOs predict subsequent unexpected high levels of non-
performing securitized loans and low levels of securitization income subsequently reported for the
quarter, as well as large write-downs of securitization-related assets during the financial crisis,
indicating that CEOs and CFOs trade on their securitization-related private information. We find
that insiders profit more from trading in their bank’s securitization quarters than in other quarters
and this finding is driven by trades by CEOs and CFOs instead of other insiders. Moreover, insiders
tend to use Rule 10b5-1 plan sales in securitization quarters to shield themselves from the litigation
risk, and they are able to avoid substantially larger negative stock returns under plan sales than
under non-plan sales. We find no evidence that insider trading improves the informativeness of
price with respect to future earnings.
Our results suggest that securitization is an especially significant source of information
advantage for bank insiders, especially top executives such as CEOs and CFOs. Securitization-
related insider trading appears to benefit bank insiders, particularly top executives, but not
investors. Our findings should be of interest to investors and policymakers involved in the
regulation of securitizations and other complex financial transactions.
34
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36
APPENDIX Variable Definitions
_____________________________________________________________________________ Securitization Variables: SB Securitized assets (outstanding principal balance of assets sold and securitized with
servicing retained or with recourse or other seller-provided credit enhancements) divided by beginning-of-quarter total assets
NPSL Past due securitized loans divided by beginning-of-quarter total assets UNPSL Unexpected non-performing securitized loans estimated as the residual of Equation (3) Chgoff_Sec Net charge-offs of securitized loans divided by beginning-of-quarter total assets Retained Total retained interests from all asset securitizations (credit-enhancing interest-only
strips, subordinated asset-backed securities, and other residual interests) divided by beginning-of-quarter total assets
SI Securitization income divided by beginning-of-quarter book value of equity. Because income statement items in Y-9C filings are reported year to date, securitization income is set as “missing” for fiscal quarters other than the first for which securitization income for the bank in the previous quarter is unavailable
USI Unexpected securitization income estimated as the residual of Equation (2) Insider Trading Variables (multiplied by 100): NetTrade Absolute value of the dollar amount of sales minus purchases by all of the bank’s
insiders, divided by beginning-of-quarter market value of equity NetSale Sale minus Buy Sale Dollar amount of sales by all of the bank’s insiders, divided by beginning-of quarter
market value of equity Buy Dollar amount of purchases by all of the bank’s insiders, divided by beginning-of-
quarter market value of equity NetTrade_Exec NetTrade component attributable to the bank’s CEO or CFO NetSale_Exec NetSale component attributable to the bank’s CEO or CFO Sale_Exec Sale component attributable to the bank’s CEO or CFO Buy_Exec Buy component attributable to the bank’s CEO or CFO NetTrade_Other NetTrade component attributable to the bank’s other insiders NetSale_Other NetSale component attributable to the bank’s other insiders Sale_Other Sale component attributable to the bank’s other insiders Buy_Other Buy component attributable to the bank’s other insiders NetSale06Q4 Dollar amount of sales minus insider purchases by all of the bank’s insiders during
2006Q4, divided by beginning-of-quarter market value of equity Sale06Q4 Dollar amount of sales by all of the bank’s insiders during 2006Q4, divided by
beginning-of-quarter market value of equity Buy06Q4 Dollar amount of purchases by all of the bank’s insiders during 2006Q4, divided by
beginning-of-quarter market value of equity Other Variables: MVE Beginning-of-quarter market value of equity Turnover Trading volume divided by beginning-of-quarter shares outstanding MB Beginning-of-quarter market value of equity divided by beginning-of-quarter book value
of equity ROA Net income divided by beginning-of-quarter total assets |ΔROA| Absolute value of ROA for the current quarter minus ROA for the previous quarter
37
|PastRet| Absolute value of buy-and-hold share return for the previous quarter minus the value-weighted index return for banks of similar size over the same window (see notes)
|FutureRet| Absolute value of buy-and-hold share return for the one-year period after the current quarter minus the value-weighted index return for banks of similar size over the same window (see notes)
StdRet Standard deviation of daily stock returns in the 12 months before the beginning of the quarter
StockComp Dollar value of an executive’s restricted stock and option grants divided by the executive’s total compensation in the most recent year, averaged across the bank’s reportable executives. This variable takes the same value for a bank in each quarter of a year
ExecOwnership Fraction of the bank’s shares owned by all of the bank’s reportable executives at the end of the previous year. This variable has the same value for a bank in each quarter of a year.
NSI Non-securitization income, i.e., net income minus securitization income, divided by beginning-of-quarter book value of equity
UNSI Unexpected non-securitization income estimated as the residual of the regression reported in Panel C of Table 5
Securities Investment securities divided by beginning-of-quarter book value of assets Loans Loans (including financing leases) held for investment divided by beginning-of-quarter
book value of assets Deposit Deposit liabilities divided by beginning-of-quarter total liabilities Writedown_S07Q3-08Q4 Total asset write-downs minus the loss provision for loans (including financing
leases) held for investment minus write-downs of unspecified assets from 2007Q3 to 2008Q4, divided by the sum of trading securities, available-for-sale securities, held-to-maturity securities, and loans (including financing leases) held for sale at the end of 2006Q4
Writedown_NS07Q3-08Q4 Excess loss provision for loans (including financing leases) held for investment (above the loss provision for 2006Q4) from 2007Q3 to 2008Q4, divided by the loans (including financing leases) held for investment at the end of 2006Q4
Returnt,t+j Buy-and-hold return from the insider transaction date to j=3 or 6 months afterwards minus the buy-and-hold return of the value-weighted index return for banks of similar size over the same window (see notes), weight-averaged across all insider trades occurring in the bank-quarter with weights based on the relative dollar amounts of these trades.
Returncrisis Buy-and-hold return from January 1, 2007 to April 30, 2009 minus the buy-and-hold return for the bank-size index over the same period (see notes).
TA06Q4 Total assets at the end of 2006Q4 Loan06Q4 Loans (including financing leases) held for investment at the end of 2006Q4, divided by
TA06Q4
Notes: All balance sheet and other stock variables are for the end of the current (fiscal) quarter unless stated otherwise. All income statement and other flow variables are for the current (fiscal) quarter unless stated otherwise. Abnormal stock returns are calculated using a size-stratified banking-industry index. Banks are sorted into above- versus below-median size groups in each quarter based on the banks’ beginning-of-quarter market capitalization. Average returns are calculated for each group for each day during the quarter. The index return for a bank in a quarter is the return during the quarter for the group to which the bank belongs at the beginning of the quarter. As defined above, the bank-quarter level variables Returnt,t+j and Returncrisis are used in the regression analysis reported in Online Appendix B; analogously defined trade-level Returnt,t+j is used in the analysis reported in Table 7.
38
FIGURE 1A Aggregate Securitized Assets and Securitization Income for the Full Sample each Quarter
Note: This figure depicts aggregate securitized assets and aggregation securitization income for the full sample of 2,000 bank-quarters during each quarter of the 2001Q2-2007Q2 sample period. Panel A of Table 1 reports the construction of the full sample.
FIGURE 1B Aggregate Insider Sales and Purchases for the Full Sample each Quarter
Note: This figure depicts the aggregate dollar amounts of insider sales and purchases for the full sample of 2,000 bank-quarters during each quarter of the 2001Q2-2007Q2 sample period. Panel A of Table 1 reports the construction of the full sample.
Sec
urit
ized
Ass
ets
($m
illi
on)
Sec
urit
izat
ion
Inco
me
($m
illi
on)
Insi
der
trad
es (
$mil
lion
)
39
TABLE 1 Sample Selection
Panel A: Full Sample (all Securitization Bank-quarters)
Bank-quarters with Y-9C filings during 2001Q2-2007Q2 and PERMCO 11,513
Exclude banks reporting zero securitized assets and securitization income each quarter of the sample period
(8,402)
Exclude bank-quarters with missing CUSIP (472)
Exclude bank-quarters with missing insider trading data (639)
Full sample 2,000
Panel B: Recourse Risk Subsample (with risk of providing recourse on securitized assets)
Full sample 2,000
Exclude bank-quarters with zero securitized assets at the end of the quarter (873)
Recourse Risk subsample 1,127
Panel C: Securitization Income Subsample (with uncertainty about current-quarter securitization income)
Full sample 2,000
Exclude bank-quarters with missing securitization income (19) Exclude bank-quarters with zero securitization income in both the current quarter (t)
and the previous quarter (t-1) (1,446)
Securitization Income subsample 535
Panel D: Crisis Subsample (with uncertainty about securitization-related write-downs)
Full sample 2,000
Cross-section of full sample banks in fourth quarter of 2006 72 Exclude banks with zero securitized assets and securitization income (38) Exclude banks acquired in the first two quarters of 2007 (1)
Crisis subsample 33
Note: The Control subsample includes the 816 bank-quarters of the full sample that do not belong to any of the Recourse Risk, Securitization Income, or Crisis subsamples.
40
TABLE 2 Descriptive Statistics
Panel A: Insider trading summary statistics for the full sample
N Mean Median Stddev 25% 75% Insider sales ($000) 2,000 3,957 262 21,154 0 1,743 Insider purchases ($000) 2,000 311 8 5,093 0 69
Panel B: Variable means and medians for the subsamples Recourse Risk
Subsample Securitization Income
Subsample
Crisis Subsample
N Mean Median N Mean Median N Mean Median NetTrade 1,127 0.155 0.023 535 0.138 0.015 SB 1,124 0.215 0.041 SBM 1,124 0.159 0.006 SBCON 1,124 0.047 0.000 SBCOM 1,124 0.009 0.000 NPSL 1,124 0.010 0.000 Chgoff_Sec 1,064 0.001 0.000 Retained 1,124 0.003 0.000 |USI| 504 0.007 0.002 MVE ($million) 1,127 20,610 4,610 535 33,982 12,378 Turnover 1,127 0.244 0.207 535 0.270 0.228 MB 1,124 2.138 2.041 535 2.186 2.068 |∆ROA| 1,118 0.001 0.000 535 0.001 0.000 |PastRet| 1,126 0.080 0.059 535 0.076 0.054 |FutureRet| 1,074 0.160 0.114 508 0.158 0.106 StdRet 1,121 0.018 0.017 535 0.018 0.017 StockComp 1,119 0.297 0.315 533 0.369 0.396 ExecOwnership 1,119 0.027 0.000 533 0.029 0.000 NetSale 1,127 0.102 0.009 535 0.115 0.009 Sale 1,127 0.136 0.011 535 0.131 0.011 Buy 1,127 0.034 0.000 535 0.016 0.000 NetSale_Exec 1,127 0.045 0.000 535 0.074 0.000 Sale_Exec 1,127 0.047 0.000 535 0.075 0.000 Buy_Exec 1,127 0.002 0.000 535 0.001 0.000 NetSale_Other 1,127 0.057 0.005 535 0.041 0.005 Sale_Other 1,127 0.089 0.007 535 0.056 0.007 Buy_Other 1,127 0.032 0.000 535 0.015 0.000 Writedown_S07Q3-08Q4 33 0.031 0.012 Writedown_NS07Q3-08Q4 33 0.027 0.022 NetSale06Q4 33 0.067 0.053 Sale06Q4 33 0.091 0.063 Buy06Q4 33 0.024 0.000 Log(TA06Q4 ) 33 17.712 17.674 Loan06Q4 33 0.573 0.640 Returnt,t+3 1,127 0.004 0.003 535 0.003 0.005 Returnt,t+6 1,127 0.006 0.004 535 0.013 0.011 Returncrisis 33 -0.140 -0.216
Total 1,127 535 33
41
TABLE 2 (continued) Panel C: Pairwise correlations of main variables for the full sample Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 NetTrade 0.04 0.06 -0.01 -0.01 0.04 -0.15 0.02 -0.12 0.02 0.09 0.04 0.08 -0.07 0.13
2 SB -0.14 0.90 0.32 0.37 0.59 0.16 0.19 -0.02 0.13 0.06 0.06 0.16 0.10 0.05
3 NPSL -0.16 0.83 0.29 0.36 0.60 0.17 0.22 -0.03 0.08 0.06 0.10 0.13 0.12 0.03
4 Chgoff_Sec -0.17 0.58 0.69 0.70 0.24 0.18 0.11 0.11 0.17 -0.01 -0.03 0.14 0.19 -0.06
5 Retained -0.15 0.70 0.78 0.70 0.27 0.19 0.17 0.04 0.13 0.03 0.10 0.17 0.15 -0.03
6 |USI| -0.02 0.25 0.24 0.14 0.17 0.12 0.18 -0.01 0.09 0.07 0.04 0.11 0.07 0.03
7 log(MVE) -0.36 0.48 0.56 0.47 0.57 0.10 0.58 0.45 0.08 -0.13 -0.10 -0.18 0.57 -0.30
8 log(Turnover) -0.05 0.31 0.37 0.25 0.38 0.11 0.60 0.32 0.10 0.03 0.08 -0.03 0.41 -0.23
9 MB -0.12 0.04 0.07 0.05 0.09 0.00 0.48 0.35 0.08 0.00 -0.12 -0.02 0.35 -0.31
10 |∆ROA| 0.00 0.09 0.06 0.12 0.13 0.02 0.01 0.02 -0.03 0.04 0.12 0.11 0.04 -0.02
11 |PastRet| 0.04 0.01 0.01 0.04 -0.02 0.02 -0.11 0.01 0.01 0.05 0.16 0.37 -0.07 0.08
12 |FutureRet| 0.07 -0.05 -0.05 -0.05 -0.04 0.03 -0.11 0.08 -0.12 0.10 0.12 0.21 -0.20 0.09
13 StdRet 0.06 0.06 0.01 0.03 -0.01 0.02 -0.20 -0.05 -0.05 0.07 0.33 0.17 -0.05 0.15
14 StockComp -0.18 0.20 0.29 0.28 0.31 -0.01 0.57 0.39 0.37 -0.01 -0.03 -0.18 -0.03 -0.23
15 ExecOwnership 0.25 -0.28 -0.34 -0.30 -0.33 -0.01 -0.62 -0.43 -0.35 -0.01 0.07 0.14 0.15 -0.47
Notes: Table 1 reports the construction of the full sample and subsamples. The appendix contains the variable definitions. In Panel A, insider sales and insider purchases for a bank in a quarter are calculated as the number of shares traded multiplied by the trade price, summed across all insiders of the bank in the quarter. In Panel C, Pearson (Spearman) correlations are reported above (below) the diagonal. Correlations that are statistically significant at the 1% level are in boldface. The means and medians of Returnt,t+3, Returnt,t+6, and Returncrisis reported in Panel B are for the bank-quarter variables used in the bank-quarter level regression analysis reported in Online Appendix B; analogously defined trade-level Returnt,t+3 and Returnt,t+6 are used in the trade-level analysis reported in Table 7.
42
TABLE 3 Contemporaneous Association between Bank Insiders’ Securitization-Related
Private Information and their Trading Volume
NetTradet = a0+a1(Securitization t)+a2 log(MVEt)+a3 log(Turnovert)+a4MBt
+a5|ΔROAt|+a6|PastRett | +a7|FutureRett|+ a8 StdRett + a9StockCompt (1) +a10ExecOwnershipt+a11StockCompt*ExecOwnershipt+et
Panel A: Tests of H1 using the Recourse and Securitization Income subsamples
Recourse Risk Subsample Securitization Income
Subsample Constant 0.125*** 0.128*** 0.131*** 0.129*** 0.106*** (8.24) (8.34) (8.12) (8.33) (6.49) SB 0.004** (2.18) NPSL 0.094*** (2.84) Chgoff_Sec 1.841*** (4.44) Retained 0.320*** (3.15) |USI| 0.848*** (14.43) log(MVE) -0.005*** -0.005*** -0.005*** -0.005*** -0.004*** (-7.14) (-7.22) (-6.97) (-7.26) (-5.74) log(Turnover) 0.004*** 0.004*** 0.004*** 0.004*** 0.007*** (2.66) (2.62) (3.09) (2.61) (3.87) MB 0.004*** 0.004*** 0.004*** 0.004*** 0.002 (2.85) (2.86) (2.85) (3.01) (1.22) |∆ROA| 0.995** 0.988** 0.773 0.919* -0.371 (2.01) (1.98) (1.53) (1.83) (-0.80) |PastRet| -0.023* -0.024* -0.027* -0.022 -0.020 (-1.65) (-1.67) (-1.78) (-1.52) (-1.22) |FutureRet| -0.012** -0.013** -0.013* -0.014** 0.014* (-2.01) (-2.09) (-1.95) (-2.22) (1.87) StdRet -0.151 -0.166 -0.226 -0.186 -0.154 (-0.92) (-1.00) (-1.29) (-1.10) (-0.86) StockComp -0.001 -0.001 -0.003 -0.002 0.007 (-0.21) (-0.25) (-0.48) (-0.27) (1.16) ExecOwnership 0.033*** 0.032** 0.035*** 0.031** -0.016 (2.69) (2.57) (2.77) (2.52) (-1.14) StockComp 0.028 0.031 0.166 0.028 0.524*** *ExecOwnership (0.25) (0.27) (0.93) (0.24) (4.31) Model F-statistic 9.11*** 9.10*** 9.82*** 9.14*** 32.36*** N 1,061 1,061 1,007 1,061 479
43
TABLE 3 (Continued)
Panel B: Decomposing SB into types of securitized assets that exhibit differential implicit recourse using the Recourse Risk subsample
Coefficient Intercept 0.128*** (8.22) SBM 0.001 (0.56) SBCON 0.024*** (4.60) SBCOM -0.054 (-1.11) log(MVE) -0.005*** (-6.95) log(Turnover) 0.004*** (2.59) MB 0.004*** (2.75) |∆ROA| 0.783 (1.56) |PastRet| -0.020 (-1.39) |FutureRet| -0.012* (-1.88) StdRet -0.223 (-1.32) StockComp -0.003 (-0.56) ExecOwnership 0.032** (2.56) StockComp*ExecOwnership 0.035 (0.30) Model F-statistic 8.69*** N 1,061 Test SBM=SBCON F-statistic 17.27*** Test SBM=SBCOM F-statistic 1.29 Test SBCON=SBCOM F-statistic 2.54
Notes: Table 1 reports the construction of the full sample and subsamples. The appendix contains all variable definitions except: SBM is securitized residential mortgages; SBCON is securitized consumer loans (home equity lines of credit, credit card receivables, automobile loans, and other consumer loans); and SBCOM is securitized commercial loans (commercial and industrial loans and all other loans and leases), all divided by beginning-of-quarter total assets. Regressions are estimated using robust regression (RREG in Stata). Heteroskedasticity-robust t-statistics are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
44
TABLE 4 Ability of Insider Trading to Predict Unexpected Not-yet-reported
Non-performing Securitized Loans: Recourse Risk Subsample Panel A: Estimation of unexpected non-performing securitized loans NPSLt = a0 + a1 NPSLt-1 + a2 SBt-1 + a3Q1t + a4Q2t + a5Q3t + year fixed effects + et (3) 0.001 0.885*** 0.001 -0.002* 0.000 0.000 (0.86) (9.54) (0.25) (-1.74) (0.48) (0.42)
Obs = 1,054 Adjusted R2 = 85.6%
Panel B: Tests of H2 predicting unexpected not-yet-reported non-performing securitized loans for the quarter using insider trading measures and the Recourse Risk subsample
UNPSL t = a0 + a1 NetSale t + et (4)
Unexpected Non-performing Securitized Loans (UNPSL) (1) (2) (3) (4)
Intercept -0.001*** -0.001*** -0.001*** -0.001*** (-10.23) (-10.08) (-10.47) (-10.25) NetSale 0.000 (0.94) Sale 0.000 (0.98) Buy -0.000 (-0.25) NetSale_Exec 0.001** (2.14) NetSale_Other 0.000 (0.83) Sale_Exec 0.001** (2.13) Buy_Exec -0.002 (-0.69) Sale_Other 0.000 (0.84) Buy_Other -0.000 (-0.20) Model F statistic 0.87 0.51 2.65* 1.36 N 1,054 1,054 1,054 1,054
Notes: Table 1, Panel B reports the construction of the Recourse Risk subsample. The appendix contains the variable definitions. Regressions are estimated using robust regression (RREG in Stata). Heteroskedasticity-robust t-statistics are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
45
TABLE 5 Ability of Insider Trading to Predict Unexpected Not-yet-reported Securitization Income:
Securitization Income Subsample
Panel A: Estimation of unexpected subsequent securitization income SI t = a0 + a1SI t-1 + a2SB t-1 + a3Q1t + a4Q2 t + a5Q3t + year fixed effects + et (2) -0.003 0.623*** 0.011** 0.001 -0.001 0.004* (-0.99) (4.96) (2.36) (0.33) (-0.23) (1.68)
Obs = 504 Adjusted R2 = 65.6%
Panel B: Tests of H2 predicting unexpected not-yet reported securitization income using insider trading measures and the Securitization Income subsample
USI t = a0 + a1 NetSale t + et (5)
Unexpected Securitization Income (USI) (1) (2) (3) (4)
Intercept -0.000*** -0.000** -0.000*** -0.000*** (-2.71) (-2.21) (-2.97) (-2.66) NetSale -0.002*** (-2.69) Sale -0.002*** (-2.85) Buy -0.001 (-0.51) NetSale_Exec -0.002*** (-5.38) NetSale_Other -0.001 (-1.04) Sale_Exec -0.002*** (-5.40) Buy_Exec 0.058*** (2.99) Sale_Other -0.001 (-1.25) Buy_Other -0.001 (-0.71) Model F-statistic 7.21*** 4.24** 15.03*** 9.96*** N 504 504 504 504
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TABLE 5 (Continued)
Panel C: Estimation of unexpected subsequent non-securitization income NSI t = a0 + a1NSIt-1 + a2Securitiest-1 + a2Loans t-1 + a2Deposit t-1 + a3Q1 t+ a4Q2 t + a5Q3t
-0.024** 0.501*** 0.054*** 0.019 0.024 0.005 0.002 0.001 (-2.27) (4.33) (3.04) (1.36) (1.35) (1.61) (0.68) (0.19)
+ year fixed effects + et Obs = 504 Adjusted R2 = 40.2% Panel D: Tests predicting unexpected not-yet reported non-securitization income using insider trading measures and the Securitization Income subsample
UNSIt = a0 + a1 NetSalet + et
Unexpected Non-securitization Income (UNSI) (1) (2) (3) (4)
Intercept 0.003*** 0.003*** 0.003*** 0.003*** (4.33) (4.39) (4.29) (4.73) NetSale -0.003 (-0.98) Sale -0.004 (-1.28) Buy -0.001 (-0.19) NetSale_Exec -0.002 (-0.32) NetSale_Other -0.003 (-0.96) Sale_Exec -0.003 (-0.54) Buy_Exec -0.006 (-1.40) Sale_Other -0.218*** (-2.95) Buy_Other 0.000 (0.04) Model F-statistic 0.96 0.84 0.52 2.67** N 504 504 504 504
Notes: Table 1, Panel C reports the construction of the Securitization Income subsample. The appendix contains the variable definitions. Regressions are estimated using robust regression (RREG in Stata). Heteroskedasticity-robust t-statistics are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
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TABLE 6 Net Insider Sales in 2006Q4 and Write-downs of Securitized Assets during 2007Q3-2008Q4
Crisis Subsample
Writedown_S07Q3-08Q4 = a0 + a1 NetSale06Q4 + a2log(TA06Q4) + a3Loan06Q4 + e (6)
Writedown_S07Q3-08Q4
(Write-downs of securitization-related assets)
Writedown_NS07Q3-08Q4
(Excess loss provisions on loans held for investment)
Intercept -0.193*** -0.193*** -0.049 -0.050 (-3.00) (-3.00) (-1.41) (-1.47) NetSale06Q4 0.159** 0.006 (2.43) (0.16) Sale06Q4 0.140** 0.020 (2.10) (0.52) Buy06Q4 -0.233** 0.031 (-2.31) (0.68) log(TA06Q4) 0.010*** 0.010*** 0.004** 0.004** (3.01) (2.99) (2.24) (2.19) Loan06Q4 0.040 0.041 0.022 0.021 (0.93) (0.97) (0.91) (0.90)
Model F-statistic 5.36*** 4.14*** 1.79 1.69 N 33 33 33 33
Notes: Table 1, Panel D reports the construction of the Crisis subsample. The appendix contains the variable definitions. Asset write-downs are cumulated from 2007Q3 to 2008Q4. For a bank acquired in 2008Q3 and another acquired in 2008Q4, we cumulate asset write-down until the quarter before the acquisition. The model is estimated using Tobit because the dependent variable is non-negative. Heteroskedasticity-robust t-statistics are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
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TABLE 7 Insider Trading Profitability–Trade-Level Analysis
Panel A: Tests of H3 using median abnormal stock returns for the Recourse Risk and Control subsamples Recourse Risk Subsample Control Subsample Sale Purchase Sale Purchase All Insiders Number of transactions 20,557 3,122 4,122 3,159 3-month abnormal return -1.1% 0.7% -1.7% -0.1% Between-group test statistic 0.25 3.75*** 6-month abnormal return -2.4% 0.1% -2.0% 0.1% Between-group test statistic -2.26** 2.65***
CEO/CFO Number of transactions 7,035 226 520 342 3-month abnormal return -1.8% 1.4% -3.1% -1.3% Between-group test statistic -0.77 4.10*** 6-month abnormal return -4.6% 4.3% 0.5% -1.7% Between-group test statistic -6.68*** 4.29***
Other Insiders Number of transactions 13,522 2,896 3,602 2,817 3-month abnormal return -0.6% 0.6% -1.6% 0.0% Between-group test statistic 2.33** 2.63*** 6-month abnormal return -1.3% 0.0% -2.3% 0.1% Between-group test statistic 4.06*** 1.36 Panel B: Tests of H3 using median abnormal stock returns for the Securitization Income and Control subsamples Securitization Income Subsample Control Subsample Sale Purchase Sale Purchase All Insiders Number of transactions 9,423 891 4,122 3,159 3-month abnormal return -1.8% 0.7% -1.7% -0.1% Between-group test statistic -4.54*** 1.75* 6-month abnormal return -3.9% 1.6% -2.0% 0.1% Between-group test statistic -10.21*** 4.86***
CEO/CFO Number of transactions 3,401 109 520 342 3-month abnormal return -7.2% -2.5% -3.1% -1.3% Between-group test statistic -12.13*** -1.72* 6-month abnormal return -14.6% -0.3% 0.5% -1.7% Between-group test statistic -20.38*** 1.58
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Other Insiders Number of transactions 6,022 782 3,602 2,817 3-month abnormal return 0.0% 1.0% -1.6% 0.0% Between-group test statistic 8.63*** 2.80*** 6-month abnormal return 0.1% 1.8% -2.3% 0.1% Between-group test statistic 8.39*** 4.65*** Panel C: Tests of H3 using median abnormal stock returns for the Crisis subsample Crisis Subsample Sale Purchase All Insiders Number of transactions 1,613 123 3-month abnormal return -2.9% -1.0% 6-month abnormal return -6.8% -2.2%
Notes: Abnormal return is the buy-and-hold return from the insider trade date to three or six months afterwards minus the buy-and-hold return of a size-stratified banking-industry index over the same window. The index is constructed by sorting banks into above- versus below-median size groups in each quarter based on the banks’ beginning-of-quarter market capitalization and calculating mean returns for each group for each day during the quarter. The index return for a bank in a quarter is the return during the quarter for the group to which the bank belongs at the beginning of the quarter. The Wilcoxon signed-rank test is used to determine whether abnormal returns are statistically significantly different from zero. z-statistics significant at the 5% level are in boldface. In Panels A and B, the between-group test statistic reports the Wilcoxon-Mann-Whitney test comparing the Recourse Risk and Securitization Income subsamples, respectively, and the Control subsample. z-statistics significant at the 1%, 5%, and 10% levels are indicated by ***, **, and *, respectively.
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TABLE 8
SEC Rule 10b5-1 Plan Sales versus Non-plan Sales Panel A: Insider sales (in $ millions) by type and quarter
All Sales
CEO/CFO Other Insiders
Plan Non-plan Plan Non-plan
2003Q3 183 5 34 18 125 2003Q4 193 37 59 28 69 2004Q1 153 12 32 60 49 2004Q2 89 13 19 28 29 2004Q3 176 24 64 17 71 2004Q4 165 36 21 30 77 2005Q1 361 199 38 12 112 2005Q2 179 41 32 32 74 2005Q3 172 38 52 29 53 2005Q4 195 39 26 32 97 2006Q1 315 33 139 15 128 2006Q2 128 28 27 23 50 2006Q3 388 78 71 81 159 2006Q4 393 155 62 28 149 2007Q1 277 14 97 5 160 2007Q2 264 42 132 6 84
Total $ 3,631 $ 795 $ 904 $ 445 $ 1,486 (47%) (53%) (23%) (77%)
Panel B: Median return comparison after insider plan and non-plan sales using the union of the Recourse and Securitization Income subsamples
All Insiders CEO/CFO Other Insiders
Plan Sales
Non-Plan Sales
Plan Sales
Non-Plan Sales
Plan Sales
Non-Plan Sales
Number of transactions 909 1,554 354 371 555 1,183 Sales in $ million $1,219 $2,251 $777 $866 $442 $1,385 3-month abnormal return -3.2% 0.0% -4.4% 0.3% -1.9% -0.1% Between-group test statistic -7.97*** -8.24*** -3.46*** 6-month abnormal return -5.2% -0.6% -6.8% -0.4% -4.4% -0.6% Between-group test statistic -9.23*** -7.83*** -4.98***
Notes: SEC Rule 10b5-1 plan and non-plan sale data are obtained from j3sg.com. Dollar amounts of insider sales are calculated as the number of shares traded multiplied by the trading price. See the notes to Table 7 for insider trading profitability calculations. Table 1 reports the construction of the subsamples. The appendix contains the variable definitions. The Wilcoxon signed-rank test is used to determine whether abnormal returns are statistically significantly different from zero. z-statistics significant at the 5% level are in boldface. The between-group test statistic reports the Wilcoxon-Mann-Whitney test comparing plan and non-plan sales. z-statistics significant at the 1%, 5%, and 10% levels are indicated by ***, **, and *, respectively.