Asset Securitization, Information Asymmetry,
and Informed Trading
Jennifer Wu Tucker
Ying Zhou
University of Florida
June 2011
Very preliminary
(Please do not quote)
Asset Securitization, Information Asymmetry, and Insider Trading
ABSTRACT
Prior research finds that the difficulty in assessing the true extent of risk transfer
in banks’ asset securitization leads to large information uncertainty for investors.
Managers are in the best position to understand their firms’ explicit or implicit
obligations from securitizations, the terms of which are often non-standard and complex.
In this study, we examine whether managers take advantage of the information
asymmetry between them and outside investors. We find that insider trading activities are
stronger for firms with more securitizations, thus documenting another consequence of
asset securitizations.
Keywords: securitization, information asymmetry, insider trading, recourse.
1. Introduction
In this study, we examine another consequence of banks’ asset securitizations: insiders
take advantage of the information complexity regarding such securitizations. Asset
securitization is the selling of financial assets to a special purpose entity (SPE), which raises the
cash for the purchase through selling debt securities backed by the securitized assets to
investors. Such debt securities are generally referred to as “asset-backed securities” (ABS). As
of the end of the first quarter of 2007, ABS have an outstanding principal amount of 8.9 trillion
in the U.S, compared to $5.4 trillion of corporate bonds and $4.5 trillion of U.S. treasury bills,
and 70% of the securitizing firms are banks (Cheng, Dhaliwal, and Neamtiu 2011).
Securitization involves some form of recourse—guarantees provided by the originating bank to
ABS investors if the performance of the underlying assets deteriorate. As securitizations vary in
the pool of securitized assets, the types of debt securities, and the form of recourse, it is rather
difficult for outsiders to determine the extent of risk transfer in a securitization. As a result,
investors of the originating banks face increased information uncertainty. Cheng et al. (2011)
find that banks’ information asymmetry measures are positively associated with their
securitization activities. We examine another consequence of asset securitization – whether
insiders take advantage of this information problem and increase trading in heavily securitizing
banks.
Asset securitization has properties of a sale in that the assets are then under control of
another entity other than the originating firm. Asset securitization also has properties of a
secured borrowing if one considers the SPE and the originating bank as a whole. When asset
securitization is treated as a sale for accounting purposes, the securitized assets are off the
bank’s books and the borrowing from the SPE is not shown on the bank’s balance sheet unless
1
the SPE is consolidated. An accounting gain is often recorded to balance the journal entries.
Given the improved balance sheet and income statement, it is not surprising that in practice
“sale accounting” rather than “securitized-borrowing accounting” is used for most asset
securitizations (Cheng et al. 2011, p.546). The complexity of asset securitization is not only in
the amount of off-the-balance-sheet debts and accounting gain, but more importantly in the
extent of risk transfer from the bank to the ABS investors. There is much uncertainty of the risk
transfer because of explicit or implicit recourse. Explicit recourse is the contractual interest
retained by the bank in the form of the most subordinated ABS. In case of poor performance of
the securitized assets, any loss will be first absorbed by the bank. Implicit recourse is any credit
enhancement beyond the explicit recourse that shields the ABS investors from underperformance of the securitized assets. These means of recourse are similar to derivatives in that
banks’ cash outflows related to these guarantees can be very small or very large depending on
the underlying performance of the securitized assets. Because the recourse terms as well as the
pool of securitized assets vary from securitization to securitization even within the same bank, it
is rather difficult for bank investors to gauge the extent of the bank’s risk exposure. As the risk
exposure changes over time with the performance of the securitized loans, large information
asymmetry exists between managers and outside investors of the bank regarding the potential
impact of asset securitizations on the company’s financial condition and bottom line.
Even though an asset securitization resembles a transaction with mixed sale and secured
borrowing features, the accounting rules under the U.S. Generally Accepted Accounting
Principles (GAAP) require a dichotomous treatment. As a result, neither sale accounting nor
securitized-borrowing accounting fully represents the economic substance of the transaction.
Additional disclosures are needed for investors to understand the implications of the transaction.
2
The accounting disclosure provided by securitizing firms under SFAS 140 has been considered
grossly inadequate (FASB 2002; FASB2008; CFA Institute 2008). Investors face much
information uncertainty due to the complexity of asset securitization and lack of corporate
disclosure. Consistent with this concern, Cheng et al. (2011) find that banks with more active
securitization activities experience larger bid-ask spreads and more disagreement among
financial analysts. As prior research finds that insiders strategically use their discretion in
financial reporting and disclosure to benefit their own trading (Aboody and Kasznik 2000;
Cheng and Lo 2006), we conjecture that insiders are also likely to take advantage of their
informational advantage and trade more actively in highly securitizing firms.
We collect bank holding companies’ financial information from the Regulatory Y-9C
reports for the sample period of 2001-2007. Using three sets of securitization variables and
three sets of insider trading variables, we find increased insider trading activities in banks with
more active securitization transactions. Given the finding of Cheng et al. (2011) that heavy
securitizing banks are more opaque, our results suggest that insiders in opaque banks are more
likely to trade.
This study contributes to the financial instrument literature by documenting another
consequence of complex financial instrument. Although financial instruments facilitate risk
transfers and resource allocations in the capital markets, their complexity together with opaque
disclosure, out-of-date regulation, and human greed has led to the recent global financial crisis.
Along with credit default swaps, collateralized debt obligations (CDO), CDO2, and CDO3
played an important role in the overheating of the economy before the bubble burst. Asset
securitization is the vehicle through which CDOs and their derivative products are created. Our
study furthers the understanding of the consequences of using such vehicles. Our study also
3
contributes to the insider trading literature by documenting another scenario in which insiders
appear to use their information advantage or financial reporting and disclosure discretion to
their own advantage.
Section 2 provides background information about asset securitization. Section 3
describes the sample, Section 4 presents the empirical results, and Section 5 concludes.
2. Background
Two approaches are used by standard setters in determining whether a securitization
resembles primarily a sale or a secured borrowing. Under the risk and reward approach, a
securitization is treated as a sale if the originating bank has transferred substantially all risks and
rewards related to the securitized assets. This approach is used by the International Accounting
Standards Board in its IAS No. 39. Under the control and component approach, sale accounting
is applied as long as the control of the securitized assets has been transferred from the
originating firm (“issuer”) to the SPE. For control over the financial assets to be considered
surrendered, all three conditions have to be met (Chen, Liu, and Ryan 2008):
1. The assets are legally isolated from the issuer.
2. The SPE is able to pledge or exchange the asset.
3. The issuer does not effective control over the assets through the right and obligation
to repurchase the assets or the unilateral ability to obtain specific assets.
The term “component” in this approach means the total assets are separated into two
components: those assets for which the issuer receives consideration other than the asset-backed
securities (ABS) in the securitization and those assets for which the issuer receives a portion of
the ABS (which is typically the lowest tranche of the ABS and is referred to as “retained
interest,” a form of explicit recourse). Under this approach, the first component is considered a
4
sale and thus its book value is off the balance sheet, and the portion (determined by ratio of the
fair value of the retained interest over the fair value of both components) of the book value of
the original asset that is retained is recorded as retained interest on the issuer’s balance sheet.
Afterwards, retained interest is treated as either “trading” or “held for sale” securities by the
issuer and corresponding accounting rules apply. The accounting standard SFAS No. 140, as
amended by SFAS No. 156, by the U.S. Financial Accounting Standards Board adopts the
control and component approach since 1997.1 Managers prefer sale accounting to securitizedborrowing accounting in recording securitizations because the former avoids ABS to be
included as liabilities in the firm’s balance sheet and because often an accounting gain is
included in the income statement from a securitization. Despite the fact that sale accounting is
used in most securitizations, credit-rating analysts and investors view most securitizations as, in
substance, secured borrowings (Liu and Richardson 2006).
The financial assets used by banks in securitizations are loans. Banks securitize three
types of loans: mortgages, consumer loans, and commercial loans. Mortgage loans can be prime
or subprime, but commercial banks--which are our sample--typically do not involve
significantly in making subprime mortgages (Carson and Weinbach 2007). Mortgage loans are
more standardized than other types of loans such that banks engage in less cherry-picking
regarding what securities to sell and what to retain. Mortgage loans are also well collateralized
and guaranteed against default. Other consumer loans, referred to as “consumer loans,” are also
standardized, but their credit losses are typically higher than those of mortgage loans.
Commercial loans are the least standardized loan type and banks often cherry-pick which loans
1
Under SFAS No. 140, sale accounting treatment is used if the originator surrenders control over the securitized
assets to the SPEs. See Appendix A for a detailed discussion of sale versus secured borrowing accounting. In 2009,
the FASB issued two new standards (SFAS 166/167 or ASC 860/810) that require more disclosure and impose
more stringent criteria on the use of sales accounting. The new standards are effective after the first quarter of
2010.
5
to securitize. Therefore, among the types of assets for securitization, the credit risk of mortgage
loans is the smallest and is most verifiable than other types of loans and that of commercial
loans is the largest (Chen, Liu, and Ryan 2008). This means that the risks that have not been
transferred from the bank to the ABS investors is the smallest in securitization of mortgage
loans and the largest in securitization of commercial loans. In our analysis, we will examine
both total securitized loans and each of the three types of loans.
3. Empirical Design
We hypothesize that insider trading is more active at banks with more asset
securitizations. We use three sets of variables to capture the intensity of securitization, all
measured from quarterly data. The first set is the amount of securitized assets of the bank at the
end of the fiscal quarter, scaled by the total assets, and the variable is referred to as SA. The
second set is a dummy variable, SAD, that takes the value of 1 if SA is positive and 0 otherwise.
The third set is the securitization volume of three types of bank loans: SMA for securitized
mortgage loans, SCONA for securitized consumer loans, and SCOMA for securitized
commercial loans, all scaled by the end-of-quarter total assets of the bank. All the three sets of
variables are referred to as SECURITIZE in our econometric model.
To test for unusual insider trading activities, we follow Piotroski and Roulstone (2004).
We use three measures to capture insider trading and distinguish insider purchases from insider
sales. The first measure, ISTRADE, is the absolute difference of the total number of shares
purchased by insiders and the total number of shares sold by insiders in the quarter, scaled by
the total trading volume of the quarter. Prior research argues that insider purchases are more
likely to be driven by private information, whereas insider sales are likely to be due to liquidity
needs. Thus, our second measure, ISPURCH, considers only insider purchases. In the third
6
measure, we consider all insider activities and treat purchases the same as sales. ISALLTRADE
is the sum of insider purchases and sales. Among the three measures, we view ISTRADE as the
primary measure because of its popular use in the literature. All the three variables are
positively valued, so we use the natural logarithm to approximate a normal distribution of the
error terms in the regression. We expect the securitization variables to have a positive
coefficient in the regression of insider trading activities on SECURITIZE.
We control for firm size, stock turnover, change in profitability, the market-to-book
ratio, and stock performances before and after the current quarter. We expect firm size to be
negatively associated with the intensity of insider trading activities because small firms tend to
have weak corporate governance and less monitoring from the media. Firm size, SIZE, is the
logarithm of the market value of common equity at the beginning of the fiscal quarter. We
control for share turnover because it is the scalar for our dependent variable and we want to
ensure that our results are not driven by scaling effects. TURNOVER, is the logarithm of the
quarter-average daily trading volume scaled by the end-of-day market capitalization. Following
Piotroski and Roulstone (2004), we control for the absolute change in accounting return of
assets from the previous quarter to the current quarter, |ΔROA|, and the market-to-book ratio
measured at the end of the fiscal quarter, MB, expecting more insider trading activities at growth
firms and firms of large earnings changes. Prior research also find that insiders are more likely
to trade after large price movements, so we include PASTRET, measured as the absolute marketadjusted return for the prior quarter, where the market return is the value-weighted index return.
In the cross-sectional regression, we also control for the cross-section differences in firms’
future performance arguably privately observed only by insiders. FUTURERET is the absolute
7
market-adjusted return for the future year, where the market return is the value-weighted index
return. All variable definitions are provided in the appendix and our regression is as follows:
Log(ISTRADE) = a0 + a1SECURITIZE + a2SIZE + a3TURNOVER + a4|ΔROA| + a5logM
+ a6PASTRET + a7FUTURERET + e
4. Data and results
The sample selection starts with all bank holding companies (BHC). 2 We collect
securitization variable SA and its components (SMA, SCONA, and SCOMA) well as the
accounting control variables from the regulatory Y-9C reports that BHCs file quarterly with the
Federal Reserve.3 Returns variables are collected from CRSP and the insider trading variables
are calculated from data in the Thomson Reuters Insiders Filing Data Feed database. The
sample period starts from the second quarter of 2001, the first time when Schedule HC-S
became available. The sample period ends in the second quarter of 2007 to avoid the effects of
the financial crisis. The test sample includes 6,352 firm-quarter observations from about 260
banks that have available data in this 25-quarter sample period.
Table 1 provides the descriptive statistics for the dependent, explanatory, and control
variables. The sample banks are large firms with average total assets around $2 billion.
Approximately 16.06% of the firm-quarters have securitized assets. The average securitization
volume, SA, is 1.77% of the bank’s total assets, with securitized mortgage loans being the
largest component. The mean and median of insider trading is 0.023% and 0.005%,
2
A bank holding company is any firm that owns or controls U.S. bank(s).
FR Y-9C reports financial information from a domestic bank holding company, including off-balance sheet items.
Effective in March 2006, the asset-size threshold for filing Y-9C increases from $150 million to $500 million. The
information about securitized assets is collected from Schedule HC-S, Servicing, Securitization and Asset Sale
Activities.
3
8
respectively. The descriptive statistics of informed trading variables are comparable to those
reported in Piotroski and Roulstone (2004).
Table 2 presents the correlation matrix. The securitization variables are negatively
correlated with the insider trading variables. The univariate results seem to contradict our
hypothesis. However, these correlations are univariate. Please note that the securitization
variables are positively correlated with firm size at 0.58 for the SA variable, whereas firm size
is highly negatively correlated with the insider trading variables at -0.28 for ISTRADE. The
negative univariate correlation between securitization and insider trading could be due to their
partial correlations with firm size. None of the control variables have a correlation with other
variables higher than 0.60, suggesting that multicollinearity should not be a serious concern. In
our regression test, we monitor multicollinearity by calculating the Variance Inflation Factor
score and none of them is higher than 20, suggesting that multicollinearity is not a concern.
Table 3 presents the primary results. In Estimation 1, to be consistent with Piotroski and
Roulstone (2004), we exclude
future performance (FUTURERET). Consistent with our
prediction, ISTRADE is positively associated with the securitization indicator. The coefficient is
0.0038 and t-statistic is 3.83, suggesting that on average securitizing firms’ insider trading
activities are 0.004 higher than those at non-securitizing firms’. This difference is close to the
median level of insider trading of the whole test sample and is economically significant. In
Estimation 2 with the future return variable, the coefficient on the securitization indicator
remains economically significant and statistically significantly positive. In Estimation 3, we use
the securitization volume and find that insider trading activities are positively associated with
SA with a coefficient of 0.0181 and t statistic of 3.22, consistent with the previous result. In
Estimation 4, we disaggregate securitized assets into mortgage loans, consumer loans, and
9
commercial loans, and expect more active insider trading to be associated with securitized loans
with higher credit risk. Consistent with this expectation, the coefficients for SCONA and
SCOMA, that is, consumer and commercial loans, are significantly higher than that for SMA –
mortgage loans. Meanwhile all the control variables are as expected, except for no statistical
significance for |ΔROA|.
In Table 4, we report the test results using the insider purchase variable and the sum of
insider purchase and sale variable. The results are very similar to the primary results.
5. Conclusion
In this paper we investigate one consequence of banks’ asset securitization activities and
find more active insider trading to be associated with more intense securitizations. Although
securitized assets themselves are often only a small percentage of a bank’s total assets, explicit
and more importantly implicit recourse could expose the bank to much credit risk associated
with the securitized assets. Such recourse terms act like derivatives: although they are initially
valued very low, these guarantees could bring large losses to the bank in case of credit problems
of the original loanees. Moreover, the vehicle of securitization could be used multiple times and
create a derivative product of a derivative. Many debt obligations could be issued backed by the
same asset. When the original loans go sour, a bank’s losses could double, triple, quadruplicate,
etc. due to the existence of recourse. Thus, securitizations are complex financial engineering
transactions in which the extent of risk transfer and risk exposure to the bank are very uncertain.
Existing financial reporting and disclosure requirements oversimplify such complex
transactions. As a result, significant information asymmetry exist between a bank’s insiders,
including its managers and directors, and the outside investors, including some so called
sophisticated institutional investors. The finding of unusually high insider trading activities at
10
heavily securitizing banks suggests that complex transactions and intrinsically insufficient
corporate disclosure provide opportunities for insider to benefit from their unique private
information. This consequence of complex financial products should be of interest to investors
and regulators.
11
References
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FIN46(R)-e, Disclosure about transfers of financial assets and interests in variable
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13
APPENDIX
Variable Definition
Variable
Definition
Informed Trading Proxies
ISTRADE
= the net direction of insider trades, calculated as absolute value of the total
shares purchased by insiders less the total shares sold by insiders, scaled by
total quarterly trading volume;
ISPURCH
= the total shares purchased by insiders, scaled by quarterly total trading
volume;
ISALLTRADE
= the total shares purchased by insiders plus the total shares sold by
insiders, scaled by quarterly total trading volume;
Bank’s Securitization Variables
SA
= total securitized assets, scaled by total assets at the end of quarter t;
SAD
= the indicator variable that equals one if a firm has non-zero ABS at the
end of quarter t, and zero otherwise;
SMA
=securitized 1-4 family residential mortgages scaled by total assets at the
end of quarter t;
SCONA
=securitized consumer loans (home equity lines of credit, credit card
receivables; automobile loans, and other consumer loans) scaled by total
assets at the end of quarter t;
SCOMA
=securitized commercial loans (commercial and industrial loans and all
other loans and leases) scaled by total assets at the end of quarter t;
Control Variables
SIZE
= the logarithm of the market value of common equity at the beginning of
the quarter t;
TURNOVER
= the logarithm of the quarter-average daily trading volume scaled by the
end-of-day market capitalization the average daily turnover during quarter
t;
|ΔROA|
= the absolute change in accounting return of assets from the previous
quarter to the current quarter;
MB
= the market value of equity divided by book value of equity at the end of
quarter t;
PASTRET
= the absolute market-adjusted return for the prior quarter, where the
market return is the value-weighted index return; and
FUTURERET
= the absolute market-adjusted return for the future year, where the market
return is the value-weighted index return.
14
TABLE 1
Descriptive Statistics
N
MEAN
MEDIAN
Insider Trading Variables
ISTRADE
6352
ISPURCH
6352
ISALLTRADE
6352
0.0231
0.0287
0.0113
0.0052
0.0067
0.0003
Securitization Variables
SAD
6352
SA
6352
SMA
6352
SCONA
6352
SCOMA
6352
0.1606
0.0177
0.0113
0.0021
0.0010
Control Variables
SIZE
6352 5.6379
TURNOVER
6352 -1.1974
|∆ROA|
6352 0.0006
LogMB
6352 0.2849
PASTRET
6352 0.0940
FUTURERET
6352 0.2008
See variable definitions in the appendix.
Q1
Q3
0.0559
0.0692
0.0386
0.0011
0.0014
0.0000
0.0191
0.0236
0.0049
0.0000
0.0000
0.0000
0.0000
0.0000
0.3672
0.0743
0.0567
0.0101
0.0051
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
5.4795
-1.1819
0.0002
0.2869
0.0703
0.1474
0.7632
0.3796
0.0012
0.1454
0.0871
0.1863
5.0846
-1.4421
0.0001
0.1926
0.0324
0.0683
6.0521
-0.9518
0.0005
0.3772
0.1277
0.2675
15
STDEV
TABLE 2
Correlation Table
Variables
1 log (ISTRADE)
2 SAD
3 SA
4 SMA
5 SCONA
6 SCOMA
7 SIZE
8 TURNOVER
9 |∆ROA|
10 logMB
11 PASTRET
12 FUTURERET
1
1.00
-0.27
-0.27
-0.24
-0.24
-0.21
-0.50
-0.33
-0.03
-0.09
0.05
0.06
2
-0.12
1.00
0.99
0.83
0.68
0.57
0.47
0.18
0.04
0.09
-0.04
-0.05
3
-0.05
0.54
1.00
0.84
0.70
0.59
0.47
0.18
0.05
0.08
-0.04
-0.05
4
-0.03
0.46
0.88
1.00
0.48
0.42
0.38
0.15
0.01
0.04
-0.03
-0.02
5
-0.08
0.49
0.52
0.23
1.00
0.46
0.41
0.12
0.08
0.05
-0.05
-0.06
6
-0.07
0.45
0.33
0.16
0.25
1.00
0.32
0.07
0.03
0.06
-0.07
-0.09
7
-0.28
0.58
0.38
0.27
0.44
0.35
1.00
0.31
-0.02
0.44
-0.15
-0.14
8
-0.20
0.17
0.11
0.07
0.11
0.04
0.27
1.00
0.06
0.01
0.06
0.09
9
-0.03
0.09
0.10
0.05
0.15
0.05
0.05
0.13
1.00
-0.07
0.01
0.04
10
-0.09
0.08
0.01
-0.02
0.03
0.03
0.39
-0.01
-0.06
1.00
-0.04
-0.13
11
0.06
-0.05
-0.01
0.01
-0.04
-0.06
-0.17
0.05
0.01
-0.05
1.00
0.14
12
0.06
-0.04
0.01
0.03
-0.04
-0.07
-0.16
0.10
0.07
-0.19
0.19
1.00
This table reports correlation matrix, where upper (lower) triangle presents the Pearson (Spearman) correlations. Correlations with
significance at the 0.01 level or lower are in bold. See the appendix for variable definitions.
16
TABLE 3
Primary Analysis
Log(ISTRADE) = a0 + a1SECURITIZE + a2SIZE + a3TURNOVER + a4|ΔROA| + a5logM
+ a6PASTRET + a7FUTURERET + e
Estimation 1
SAD
SA
SMA
SCONA
SCOMA
SIZE
TURNOVER
|∆ROA|
LogMB
PASTRET
FUTURERET
INTERCEPT
Adj-Rsquared
N
Estimation 2
Estimation 3
Estimation 4
Coef.
t-statistics
Coef.
0.0181
(3.22)***
Coef.
t-statistics
Coef.
t-statistics
0.0038
(3.83)***
0.0037
(3.79)***
t-statistics
0.0423 (10.02)***
-0.0077 (-11.77)***
-0.0078 (-6.48)***
0.0234 (0.13)^^^
0.0033 (1.11)^^^
0.0050 (1.67)*^^
0.0037 (2.42)**^
0.0406 (9.44)***
-0.0073 (-11.82)***
-0.0078 (-6.41)***
-0.0081 (-0.04)^^^
0.0032 (1.11)^^^
0.0051 (1.72)*^^
0.0035 (2.29)**^
0.0390 (9.24)***
0.0156 (2.11)**^
0.0986 (4.37)***
0.0768 (1.91)*^^
-0.0078 (-11.96)***
-0.0076 (-6.30)***
-0.0544 (-0.30)^^^
0.0041 (1.40)^^^
0.0049 (1.66)*^^
0.0036 (2.35)**^
0.0416 (9.57)***
9.74%
6,352
9.83%
6,352
9.90%
6,352
9.94%
6,352
-0.0078 (-11.97)***
-0.0076 (-6.26)***
0.0525 (0.30)^^^
0.0027 (0.92)^^^
0.0063 (1.80)*^^
See the appendix for variable definitions. t-statistics are calculated using firm and time clustered standard errors
(Petersen 2009). ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
17
TABLE 4
Using Alternative Insider Trading Measures
Panel A: Insider purchases only
Log(ISPURCH) = a0 + a1SECURITIZE + a2SIZE + a3TURNOVER + a4|ΔROA| + a5logM
+ a6PASTRET + a7FUTURERET + e
Estimation 1
SAD
SA
SMA
SCONA
SCOMA
SIZE
TURNOVER
|∆ROA|
LogMB
PASTRET
FUTURERET
INTERCEPT
Adj-Rsquared
N
Estimation 2
Estimation 3
Estimation 4
Coef.
t-statistics
Coef.
0.0211
(3.59)***
Coef.
t-statistics
Coef.
t-statistics
0.0048
(3.95)***
0.0048
(3.93)***
t-statistics
0.0520 (10.04)***
-0.0098 (-11.67)***
-0.0109 (-7.78)***
0.0109 (0.05)^^^
0.0054 (1.61)^^^
0.0065 (1.89)*^^
0.0040 (2.26)**^
0.0501 (9.46)***
-0.0093 (-11.87)***
-0.0109 (-7.69)***
-0.0205 (-0.10)^^^
0.0051 (1.55)^^^
0.0066 (1.99)**^
0.0038 (2.12)**^
0.0475 (9.27)***
0.0166 (2.16)**^
0.1352 (4.83)***
0.1068 (2.02)**^
-0.0100 (-12.15)***
-0.0106 (-7.60)***
-0.0937 (-0.46)^^^
0.0063 (1.94)*^^
0.0064 (1.91)*^^
0.0040 (2.19)**^
0.0513 (9.72)***
11.43%
6,352
11.50%
6,352
11.51%
6,352
11.61%
6,352
-0.0100 (-11.85)***
-0.0107 (-7.65)***
0.0427 (0.22)^^^
0.0047 (1.46)^^^
0.0078 (2.05)**^
See the appendix for variable definitions. t-statistics are calculated using firm and time clustered standard errors
(Petersen 2009). ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
18
TABLE 4
(Continued)
Panel B: Sum of insider purchases and insider sales
Log(ISALLTRADE) = a0 + a1SECURITIZE + a2SIZE + a3TURNOVER + a4|ΔROA| + a5logM
+ a6PASTRET + a7FUTURERET + e
Estimation 1
SAD
SA
SMA
SCONA
SCOMA
SIZE
TURNOVER
|∆ROA|
LogMB
PASTRET
FUTURERET
INTERCEPT
Estimation 2
Estimation 3
Estimation 4
Coef.
t-statistics
Coef.
t-statistics
0.0114
(3.23)***
0.0093
0.0764
0.0944
-0.0053
-0.0040
0.0684
-0.0026
0.0002
0.0015
0.0300
(1.93)*^^
(4.12)***
(2.95)***
(-8.59)***
(-5.42)***
(0.56)^^^
(-1.38)^^^
(0.14)^^^
(1.19)^^^
(7.89)***
Coef.
t-statistics
Coef.
t-statistics
0.0033
(4.40)***
0.0032
(4.34)***
-0.0054
-0.0041
0.1364
-0.0033
0.0006
(-8.88)***
(-5.70)***
(1.15)^^^
(-1.79)*^^
(0.47)^^^
0.0303
(8.59)***
-0.0053
-0.0041
0.1248
-0.0031
0.0001
0.0015
0.0296
(-8.71)***
(-5.66)***
(1.03)^^^
(-1.60)^^^
(0.11)^^^
(1.21)^^^
(8.01)***
-0.0048
-0.0041
0.1158
-0.0035
0.0003
0.0014
0.0272
(-8.49)***
(-5.56)***
(0.94)^^^
(-1.85)*^^
(0.27)^^^
(1.12)^^^
(7.65)***
Adj-Rsquared
8.59%
8.61%
8.47%
8.65%
N
6,352
6,352
6,352
6,352
See the appendix for variable definitions. t-statistics are calculated using firm and time clustered standard errors
(Petersen 2009). ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
19
20