Securitization and Insider Tradingbear.warrington.ufl.edu/tucker/2016_securitization_paper.pdf · trading. In a typical securitization, the issuer (assumed to be a bank) transfers

THE ACCOUNTING REVIEW American Accounting AssociationVol. 91, No. 2 DOI: 10.2308/accr-51230March 2016pp. 649–675

Securitization and Insider Trading

Stephen G. RyanNew York University

Jennifer Wu TuckerUniversity of Florida

Ying ZhouUniversity of Connecticut

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.

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

We thank Stephen Asare, Gauri Bhat (discussant), Stephen V. Brown, Michael Donohoe, Leslie D. Hodder (editor), Kathy Rupar, Stanley Veliotis, JamesVincent, Dushyant Vyas, two anonymous reviewers, and participants at the 2011 American Accounting Association Annual Meeting and accountingworkshops at the University of Florida, Fordham University, University of International Business and Economics, Shanghai University of Finance andEconomics, and Yale University.

Supplemental materials can be accessed by clicking the link in Appendix B.

Editor’s note: Accepted by Leslie D. Hodder.

Submitted: May 2013Accepted: July 2015

Published Online: July 2015

649

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 ofABS in the U.S. equaled $8.9 trillion, compared to $5.4 trillion of corporate bonds and $4.5 trillion of U.S. Treasuries (Cheng,Dhaliwal, and Neamtiu 2011). Although securitization volume for most asset classes other than agency mortgages felldramatically during the financial crisis, volume has begun to rebound as the U.S. economy and financial markets recover.1

Second, banks’ securitization-based business models worked well before, but poorly during the crisis, allowing us to examinelarge realizations of downside risks on banks’ existing exposures to these transactions. Third, banks’ public quarterly regulatoryreports contain detailed and standardized data about their securitizations.

Securitization is a contractually and economically complex transaction for which the issuing banks may be exposed tothree types of risk. First, banks always retain some risk of providing recourse on previously securitized assets (‘‘recourserisks’’). Securitizations of credit-risky assets typically create risk-layered tranches of securities and other contractual asset andliability interests in the securitized assets. Issuing banks often retain risky and illiquid residual securities to ‘‘credit enhance’’ thesenior securities sold to outside investors. Alternatively, banks may provide non-contractual (‘‘implicit’’) recourse. Inessentially all securitizations, banks provide contractual representations and warranties that the securitized assets have thecharacteristics specified in the securitization prospectus. Banks violating representations and warranties are required to buyback the assets at par or other specified amounts if requested by the purchaser. Although historically viewed as distinct fromrecourse, buybacks of impaired securitized assets at prices above fair value due to actual or alleged violations effectivelyamount to recourse. Second, banks are exposed to uncertainty about their securitization income for the current period. Thisincome depends on the prices that investors are willing to pay for the sold ABS and the estimated fair values of banks’ retainedinterests. Banks typically do not publicly disclose securitization income for a quarter until they file their financial and regulatoryreports midway through the subsequent quarter. Third, banks for which securitization is a business model are exposed touncertainty about the sustainability of this model, which requires continuing access to financing on acceptable terms that canevaporate quickly in credit crunches.

Our study is motivated by and contributes to both the securitization and insider trading literatures. The former literaturefinds 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-dresstheir balance sheets and manipulate reported earnings (Dechow and Shakespeare 2009; Dechow, Myers, and Shakespeare2010).2 Due to their complex economics and accounting, securitizations contribute to banks’ opacity (Cheng et al. 2011). Weextend this literature, especially Cheng et al. (2011), by examining how bank insiders exploit their securitization-relatedinformation advantages through trading. The latter literature finds that insiders trade before significant price movements and thepublic disclosure of earnings (Lakonishok and Lee 2001; Ke, Huddart, and Petroni 2003). However, few studies in thisliterature examine specific sources of insiders’ information advantage. A notable exception is Aboody and Lev (2000), whoidentify 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 andtest for direct links between that information and insiders’ trading. Such tests help policymakers identify gaps in requireddisclosures and thereby limit insider trading. We extend this literature by examining securitizations as a source of bank insiders’private information.

To mitigate self-selection issues, our full sample includes bank holding companies only if they report securitized assetsoutstanding or non-zero securitization income in at least one quarter during our full-sample period of 2001Q2–2007Q2. Weview securitization as a feasible choice for these ‘‘Securitization Banks.’’ We identify subsamples that correspond to the threetypes 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 subsamplehave private information about the banks’ recourse risks. The ‘‘Securitization Income’’ subsample includes all bank-quarterswith non-zero securitization income reported for the current quarter or the previous quarter; insiders in this subsample haveprivate information about the banks’ securitization income. The ‘‘Crisis’’ subsample includes all banks with non-zerosecuritized assets outstanding or securitization income in 2006Q4 on the verge of the financial crisis;3 insiders in this subsamplehave private information about the (non)sustainability of the banks’ securitization-based business models.

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

2 Barth and Taylor (2010) question whether Dechow et al.’s (2010) findings are attributable to earnings management.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).

650 Ryan, Tucker, and Zhou

The Accounting ReviewMarch 2016

http://www.sifma.org/research/statistics.aspx

We develop three hypotheses about the association between bank insiders’ securitization-related private information and

their 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-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-based business

model during the crisis,5 and find that this is the case. Hence, bank insiders appear to trade on 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 percent

(�3.9 percent) 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 percent during 2007–2008,

31.7 percent more negative than the market and 4.7 percent more negative than similarly sized banks. Net 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 Securities and Exchange Commission (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 percent of plan sales

for our full sample occur in securitization quarters when we expect bank insiders to possess securitization-related private

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 difficultto develop and employ effectively, 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, andthe attrition of the 33 Crisis subsample observations as the crisis unfolds.

Securitization and Insider Trading 651


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 percent of sales by banks’ CEOs and CFOs,

but only 23 percent 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.

We believe our findings have implications for policymakers 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 the Financial Accounting Standards Board (FASB) should consider requiring

management discussion and analysis (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 Statement of Financial Accounting Standards (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

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.

Effective in 2001, SFAS 140 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 interest at the end of each reporting period; and (3) the sensitivity of these valuations to changes in key estimates.

Effective in 2010, SFAS 166 and SFAS 167 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,

6 For simplicity, we assume that banks do not consolidate securitization SPEs, as is usually the case, although the issuance of SFAS 167 madeconsolidation of certain types of SPEs (e.g., credit card master trusts and asset-backed commercial paper conduits) more common starting in 2010. Saleaccounting with consolidation of securitization SPEs effectively yields secured-borrowing accounting for the consolidated entity.

7 The types of retained interests that generally accepted accounting principles (GAAP) require to be recognized at fair value at the time of sale haveexpanded over time. SFAS 125 and SFAS 140 required initial fair value measurement only for retained liability interests. SFAS 156 added servicingrights and SFAS 166 added all other asset interests. Before SFAS 166, GAAP required retained asset interests not required to be fair valued to beinitially recognized at relative fair value-based allocations of the amortized cost basis of the securitized assets at the time of sale.

8 The information banks provide about risk-layering may have improved recently due to the SEC’s revision of Regulation AB in August 2014, whichrequires extensive additional disclosures about securitization pools in securitization prospectuses and on an ongoing basis. See Acharya and Ryan(2016) for details about these requirements.



banks’ financial reports contain very little 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). Assuming 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 larger at R&D-intensive firms than at other firms, consistent with R&D activities providing insiders

with information advantages. Aboody and Lev (2000) 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 (the link to this and the other online

appendices is provided in Appendix B).

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 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 hypothesis in terms of net insider sales (insider sales minus insider

purchases):

H2: Net insider sales during a quarter are negatively associated with banks’ not-yet-reported securitization-related

accounting performance for that quarter 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.

Last, 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 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 percent of the market capitalization of all banks, on

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

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 Crisissubsamples.

9 See: http://www.newyorkfed.org/research/banking_research/datasets.html



http://www.newyorkfed.org/research/banking_research/datasets.html

average, during our sample period. We exclude 472 observations with missing CUSIP (Committee on Uniform Security

Identification Procedures), 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 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 1 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 2 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 2.5 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 29 in 2001Q4 to a minimum of 15 in 2006Q3 (untabulated). Of the Securitization Income subsample

FIGURE 1Aggregate Securitized Assets and Securitization Income for the Full Sample Each Quarter

This figure depicts aggregate securitized assets and aggregation securitization income for the full sample of 2,000 bank-quarters during each quarter of the2001Q2–2007Q2 sample period. Panel A of Table 1 reports the construction of the full sample.

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 ofcompensation.



TABLE 2

Descriptive Statistics

Panel A: Insider Trading Summary Statistics for the Full Sample

n Mean Median Std. Dev. 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 RiskSubsample

Securitization IncomeSubsample

CrisisSubsample

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

jUSIj 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

jDROAj 1,118 0.001 0.000 535 0.001 0.000

jPastRetj 1,126 0.080 0.059 535 0.076 0.054

jFutureRetj 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,tþ3 1,127 0.004 0.003 535 0.003 0.005

Returnt,tþ6 1,127 0.006 0.004 535 0.013 0.011

Returncrisis 33 �0.140 �0.216

Total 1,127 535 33

(continued on next page)



observations, 89.3 percent 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 H1

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 ¼ a0 þ a1Securitizationt þ a2logðMVEtÞ þ a3logðTurnovertÞ þ a4MBt þ a5jDROAtj þ a6jPastRettjþ a7jFutureRettj þ a8StdRett þ a9StockCompt þ a10ExecOwnershipt þ a11StockCompt � ExecOwnershipt

þ et:

ð1Þ

TABLE 2 (continued)

Panel C: Pairwise Correlations of Main Variables for the Full Sample

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 0.132. 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. jUSIj �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 �0.308. 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 �0.239. 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. jDROAj 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. jPastRetj 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 0.0812. jFutureRetj 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 0.0913. 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 0.1514. 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 �0.2315. 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

Table 1 reports the construction of the full sample and subsamples. In Panel A, insider sales and insider purchases for a bank in a quarter are calculated asthe 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 percent level are in bold. The means and mediansof Returnt,tþ3, Returnt,tþ6, and Returncrisis reported in Panel B are for the bank-quarter variables used in the bank-quarter-level regression analysis reportedin Online Appendix B; analogously defined trade-level Returnt,tþ3 and Returnt,tþ6 are used in the trade-level analysis reported in Table 7.Appendix A contains the variable definitions.

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. Forexample, some banks have positive securitized assets in a quarter from securitizations in prior quarters, but conduct no securitizations and, thus, earn nosecuritization income in the current quarter. Some banks record zero securitization income despite conducting securitizations in a quarter. Some banksconduct securitizations during a quarter for which they do not retain servicing rights or provide credit enhancement and so do not disclose thesecuritized assets on Schedule HC-S.



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, which 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:

SIt ¼ a0 þ a1SIt�1 þ a2SBt�1 þ a3Q1t þ a4Q2t þ a5Q3t þ year fixed effects þ et: ð2Þ

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, jUSIj, as the proxy for

FIGURE 2Aggregate Insider Sales and Purchases for the Full Sample Each Quarter

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 the2001Q2–2007Q2 sample period. Panel A of Table 1 reports the construction of the full sample.

12 Piotroski and Roulstone (2004) scale their measure by trading volume. We scale instead by beginning-of-quarter market value of equity, becausetrading volume trends upward strongly over time with the growth in high-frequency trading after their sample period ends in 2000.



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-quarternumber 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, jDROAj, because we expectinsiders of banks with higher growth and more variable profitability to have greater 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, jPastRetj,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, jFutureRetj, to capture insiders’ private information about banks’ future performance unrelated tosecuritization (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 andStockComp 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

percent 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 percent of total assets. The other recourse risk proxies have considerably smaller

values. In the Securitization Income subsample, securitization income (undeflated SI) equals 34.3 percent of net income, on average

(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, wediscuss only the Spearman correlations. NetTrade is negatively and insignificantly correlated with SB and jUSIj, 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 jUSIj.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 issuperior 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, Minutti-Meza, and Wasley 2012). The first four

columns of the panel use the Recourse Risk subsample with the four proxies for bank insiders’ securitization-related privateinformation 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 (t¼ 2.18), 0.094 (t¼ 2.84), 1.841 (t¼ 4.44), and 0.320 (t¼ 3.15), respectively. The

fifth column uses the Securitization Income subsample with jUSIj as the proxy for uncertainty about current-quartersecuritization income. Again consistent with H1, the coefficient on jUSIj is significantly positive at 0.848 (t ¼ 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. Thecoefficients on the other control variables are less consistently significant with the predicted sign.13 Overall, 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

13 We expect the coefficient on jDROAj to be positive. Results in the Recourse Risk subsample analysis are largely consistent with this expectation, butthis coefficient is insignificant in the Securitization Income subsample analysis. The coefficient on jPastRetj is weakly significantly negative in most ofthe models. We expect the coefficient on jFutureRetj to be positive and find a weakly positive coefficient in the Securitization Income subsampleanalysis, but a negative coefficient in the Recourse Risk subsample analysis. The coefficient on StdRet is insignificant. The coefficient onExecOwnership 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.



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 that

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.

TABLE 3

Contemporaneous Association between Bank Insiders’ Securitization-Related Private Information and their TradingVolume

NetTradet ¼ a0 þ a1ðSecuritizationtÞ þ a2logðMVEtÞ þ a3logðTurnovertÞ þ a4MBt þ a5jDROAtj þ a6jPastRettjþa7jFutureRettj þ a8StdRett þ a9StockCompt þ a10ExecOwnershipt þ a11StockCompt � ExecOwnershipt

þ et

ð1Þ

Panel A: Tests of H1 Using the Recourse and Securitization Income Subsamples

Recourse RiskSubsample

Securitization IncomeSubsample

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)

jUSIj 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)

jDROAj 0.995** 0.988** 0.773 0.919* �0.371

(2.01) (1.98) (1.53) (1.83) (�0.80)

jPastRetj �0.023* �0.024* �0.027* �0.022 �0.020

(�1.65) (�1.67) (�1.78) (�1.52) (�1.22)

jFutureRetj �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 � ExecOwnership 0.028 0.031 0.166 0.028 0.524***

(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




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

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

TABLE 3 (continued)

Panel B: Decomposing SB into Types of Securitized Assets that Exhibit Differential Implicit Recourse Using theRecourse 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)

jDROAj 0.783

(1.56)

jPastRetj �0.020

(�1.39)

jFutureRetj �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 ¼ SBCON F-statistic 17.27***

Test SBM ¼ SBCOM F-statistic 1.29

Test SBCON ¼ SBCOM F-statistic 2.54

***, **, * Indicate significance at the 1 percent, 5 percent, and 10 percent levels, respectively.Table 1 reports the construction of the full sample and subsamples. SBM is securitized residential mortgages; SBCON is securitized consumer loans (homeequity lines of credit, credit card receivables, automobile loans, and other consumer loans); and SBCOM is securitized commercial loans (commercial andindustrial loans and all other loans and leases), all divided by beginning-of-quarter total assets. Regressions are estimated using robust regression (RREGin Stata). Heteroscedasticity-robust t-statistics are reported in parentheses.Appendix A contains all other variable definitions.

14 Residential mortgages and other consumer loans generally are homogeneous, whereas commercial loans generally are heterogeneous. All else beingequal, bank insiders’ information advantage should be greater for heterogeneous loans than for homogeneous loans. Because commercial loansecuritizations are relatively small, we do not focus on this source of insiders’ information advantage.



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 percent (74.0 percent),

SBCON averages 4.7 percent (21.9 percent), and SBCOM averages only 0.9 percent (4.2 percent). 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 (F¼17.27). Likely due to SBCOM’s small

magnitude, the coefficient on SBCOM has a large standard error that renders all differences of coefficients involving SBCOMinsignificant. 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 H2

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 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 ¼ a0 þ a1NPSLt�1 þ a2SBt�1 þ a3Q1t þ a4Q2t þ a5Q3t þ year fixed effectsþ et: ð3Þ

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 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 NPSLadequately 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 ¼ b0 þ b1NetSalet þ et: ð4Þ

USIt ¼ b0 þ b1NetSalet þ et: ð5Þ

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.

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 dependentvariable as the change in NPSL from the beginning of quarter t to the end of quarter tþ2 plus the sum of loan charge-offs during quarters t, tþ1, and tþ2.If charge-offs during a period equal zero, then the change in NPSL equals the amount of loans that become delinquent, net of any cure of priordelinquencies, during the period. Charge-offs decrease NPSL by the book value of the charged-off loans. Thus, the change in NPSL plus charge-offsduring a period reflect loans that either become delinquent, net of cure, or that migrate from delinquency to charge-offs during the period.



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 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

TABLE 4

Ability of Insider Trading to Predict Unexpected Not-Yet-Reported Non-Performing Securitized Loans: Recourse RiskSubsample

Panel A: Estimation of Unexpected Non-Performing Securitized Loans

NPSLt ¼ a0 þ a1NPSLt�1 þ a2SBt�1 þ a3Q1t þ a4Q2t þ a5Q3t þ year fixed effectsþ et ð3Þ

a0 a1 a2 a3 a4 a5

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. ¼ 1,054

Adjusted R2 ¼ 85.6%

Panel B: Tests of H2 Predicting Unexpected Not-Yet-Reported Non-Performing Securitized Loans for the QuarterUsing Insider Trading Measures and the Recourse Risk Subsample

UNPSLt ¼ a0 þ a1NetSalet þ et ð4Þ

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

***, **, * Indicate significance at the 1 percent, 5 percent, and 10 percent levels, respectively.Table 1, Panel B reports the construction of the Recourse Risk subsample. Regressions are estimated using robust regression (RREG in Stata).Heteroscedasticity-robust t-statistics are reported in parentheses.Appendix A contains the variable definitions.



TABLE 5

Ability of Insider Trading to Predict Unexpected Not-Yet-Reported Securitization Income: Securitization IncomeSubsample

Panel A: Estimation of Unexpected Subsequent Securitization Income

SIt ¼ a0 þ a1SIt�1 þ a2SBt�1 þ a3Q1t þ a4Q2t þ a5Q3t þ year fixed effectsþ et ð2Þ

a0 a1 a2 a3 a4 a5

�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. ¼ 504


Panel B: Tests of H2 Predicting Unexpected Not-Yet Reported Securitization Income Using Insider Trading Measuresand the Securitization Income Subsample

USIt ¼ a0 þ a1NetSalet þ et ð5Þ

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

Panel C: Estimation of Unexpected Subsequent Non-Securitization Income

NSIt ¼ a0 þ a1NSIt�1 þ a2Securitiest�1 þ a3Loanst�1 þ a4Depositt�1 þ a5Q1t þ a6Q2t þ a7Q3t þ year fixed effectsþ et

a0 a1 a2 a3 a4 a5 a6 a7

�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)

Obs. ¼ 504





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.

TABLE 5 (continued)

Panel D: Tests Predicting Unexpected Not-Yet Reported Non-Securitization Income Using Insider Trading Measuresand the Securitization Income Subsample

UNSIt ¼ a0 þ a1NetSalet þ 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

***, **, * Indicate significance at the 1 percent, 5 percent, and 10 percent levels, respectively.Table 1, Panel C reports the construction of the Securitization Income subsample. Regressions are estimated using robust regression (RREG in Stata).Heteroscedasticity-robust t-statistics are reported in parentheses.Appendix A contains the variable definitions.

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 asthe 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 thequarter. To test the sensitivity of our results to this choice and to better align the measure of insider trades with the subsequent arrival of securitizationnews, we alternatively measure insider trades from one day after the earnings announcement for the previous quarter to one day before the earningsannouncement for the current quarter. The use of this alternative insider trading window does not alter the results, reflecting the fact that 95 percent ofthe trades in this alternative window occur during the fiscal quarter involved.



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 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 Salecomponents, 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 Write-Down and Capital Infusion (WDCI) 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 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 the balance

sheet:

Writedown S07Q3�08Q4 ¼ c0 þ c1NetSale06Q4 þ c2logðTA06Q4Þ þ c3Loan06Q4 þ e: ð6Þ

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).

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 (t¼ 2.43) on NetSale06Q4, and Column (2) reports a

significantly positive coefficient on Sale06Q4 of 0.140 (t¼ 2.10) and a significantly negative coefficient on Buy06Q4 of�0.233 (t

¼�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.

17 For two sample banks that were acquired during 2007Q3–2008Q4, we calculate write-downs only up to the quarter before the acquisition to excludediscretionary ‘‘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.19 The WDCI help function indicates that its write-downs for loans 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 percent), subprime residential mortgage-backed

securities (16 percent), collateralized loan obligations (11 percent), and commercial mortgage-backed securities (8 percent).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.22 Untabulated robust-regression (linear) estimation of Equation (6) yields the same inferences.



Tests of H3

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 (2009) one-month

return window because it often excludes 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 all 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

TABLE 6

Net Insider Sales in 2006Q4 and Write-Downs of Securitized Assets During 2007Q3–2008Q4: Crisis Subsample

Writedown S07Q3�08Q4 ¼ a0 þ a1NetSale06Q4 þ a2logðTA06Q4Þ þ a3Loan06Q4 þ e ð6Þ

Writedown_S07Q3–08Q4

(Write-Downs of Securitization-Related Assets)

Writedown_NS07Q3–08Q4

(Excess Loss Provisions on LoansHeld for Investment)

(1) (2) (3) (4)

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

***, ** Indicate significance at the 1 percent and 5 percent levels, respectively.Table 1, Panel D reports the construction of the Crisis subsample. Asset write-downs are cumulated from 2007Q3 to 2008Q4. For a bank acquired in2008Q3 and another acquired in 2008Q4, we cumulate asset write-down until the quarter before the acquisition. The model is estimated using Tobitbecause the dependent variable is non-negative. Heteroscedasticity-robust t-statistics are reported in parentheses.Appendix A contains the variable definitions.

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.24 Aboody and Lev (2000) also examine a 12-month window 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.



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 bold. 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, then we expect median abnormal

returns to be zero. If insiders instead trade on private information, then we expect median abnormal returns to be negative

(positive) after insider sales (purchases).

In the top part of Table 7, Panel A, examining trades by all insiders, abnormal stock returns are significantly negative after

insider sales and significantly positive after insider purchases in both 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 Table 7, 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 percent for sales by CEOs and CFOs compared to �1.3 percent for sales by other insiders, and 4.3 percent for

purchases by CEOs and CFOs compared to 0.0 percent 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 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 three-month and six-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 percent level in a one-tailed (two-tailed) test, some

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) 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.



TABLE 7

Insider Trading ProfitabilityTrade-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

Three-month abnormal return �1.1% 0.7% �1.7% �0.1%

Between-group test statistic 0.25 3.75***

Six-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


Between-group test statistic �0.77 4.10***

Six-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

Three-month abnormal return �0.6% 0.6% �1.6% 0.0%

Between-group test statistic 2.33** 2.63***


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

SecuritizationIncome Subsample Control Subsample

Sale Purchase Sale Purchase

All Insiders

Number of transactions 9,423 891 4,122 3,159


Between-group test statistic �4.54*** 1.75*


Between-group test statistic �10.21*** 4.86***

CEO/CFO

Number of transactions 3,401 109 520 342

Three-month abnormal return �7.2% �2.5% �3.1% �1.3%

Between-group test statistic �12.13*** �1.72*

Six-month abnormal return �14.6% �0.3% 0.5% �1.7%

Between-group test statistic �20.38*** 1.58

Other Insiders

Number of transactions 6,022 782 3,602 2,817

Three-month abnormal return 0.0% 1.0% �1.6% 0.0%

Between-group test statistic 8.63*** 2.80***

Six-month abnormal return 0.1% 1.8% �2.3% 0.1%

Between-group test statistic 8.39*** 4.65***




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 the J3

Services Group (J3SG) database,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 percent of sales

by CEOs and CFOs, but only 23 percent of sales by other insiders. Over 98 percent 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 percent of the banks’ quarters. These statistics suggest that CEOs and CFOs are more

concerned than other insiders about litigation risk, 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?

The details of this analysis are reported in Online Appendix C. Briefly, we expand the future earnings response coefficient

(FERC) framework (Collins et al. 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.

TABLE 7 (continued)

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

Three-month abnormal return �2.9% �1.0%

Six-month abnormal return �6.8% �2.2%

***, **, * Indicate z-statistics significant at the 1 percent, 5 percent, and 10 percent levels, respectively.Abnormal return is the buy-and-hold return from the insider trade date to three or six months afterward minus the buy-and-hold return of a size-stratifiedbanking-industry index over the same window. The index is constructed by sorting banks into above- versus below-median size groups in each quarterbased on the banks’ beginning-of-quarter market capitalization and calculating mean returns for each group for each day during the quarter. The indexreturn 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 percent level are inbold. In Panels A and B, the between-group test statistic reports the Wilcoxon-Mann-Whitney test comparing the Recourse Risk and Securitization Incomesubsamples, respectively, and the Control subsample.

26 The J3SG database (see: http://j3sg.com) is less comprehensive than the Thomson Reuters database used in our earlier analyses. For example, for theperiod over which the two databases overlap, the number of bank-quarters (dollar amount of insider trades) in the J3SG database is 63.7 percent (55.8percent) of that in the Thomson Reuters database.

27 Jagolinzer (2009) observes that 18 percent of plan trades are not disclosed as such and that insiders predominantly use 10b5-1 plans for sales, notpurchases.



http://j3sg.com

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 banks’

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 andSecuritization Income Subsamples

All Insiders CEO/CFO Other Insiders

PlanSales

Non-PlanSales

PlanSales

Non-PlanSales

PlanSales

Non-PlanSales

Number of transactions 909 1,554 354 371 555 1,183

Sales in $ million $1,219 $2,251 $777 $866 $442 $1,385

Three-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***

Six-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***

*** Indicates z-statistics significant at the 1 percent level.SEC Rule 10b5-1 plan and non-plan sale data are obtained from J3SG at: http://j3sg.com. Dollar amounts of insider sales are calculated as the number ofshares traded multiplied by the trading price. See the notes to Table 7 for insider trading profitability calculations. Table 1 reports the construction of thesubsamples. The Wilcoxon signed-rank test is used to determine whether abnormal returns are statistically significantly different from zero. z-statisticssignificant at the 5 percent level are in bold. The between-group test statistic reports the Wilcoxon-Mann-Whitney test comparing plan and non-plan sales.Appendix A contains the variable definitions.



http://j3sg.com

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.

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APPENDIX A

Variable Definitions

Variable Definition

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.

jDROAj Absolute value of ROA for the current quarter minus ROA for the previous quarter.

jPastRetj 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).

jFutureRetj 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.




APPENDIX BLink to Online Appendices A, B, and C

Online_Appendices: http://www.dx.doi.org/10.2308/accr-51230.s01

APPENDIX A (continued)

Variable Definition

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,tþj Buy-and-hold return from the insider transaction date to j ¼ 3 or 6 months afterward 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.

All balance sheet and other stock variables are for the end of the current (fiscal) quarter unless stated otherwise. All income statement and other flowvariables 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. Averagereturns 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 groupto which the bank belongs at the beginning of the quarter. As defined above, the bank-quarter level variables Returnt,tþj and Returncrisis are used in theregression analysis reported in the Online Appendix B; analogously defined trade-level Returnt,tþj is used in the analysis reported in Table 7.



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Securitization and Insider Tradingbear.warrington.ufl.edu/tucker/2016_securitization_paper.pdf · trading. In a typical securitization, the issuer (assumed to be a bank) transfers