Insider trading around bank failures.

Jalbert, Terrance J. ; Rao, Ramesh P. ; Bathala, Chenchuramariah T. 等

INTRODUCTION

In this paper we examine insider trading around the failure of banks. Between 1980 and 1994, 1,617 banks failed in the United States. This number constitutes 9.14 percent of all U.S. chartered banks. On a total asset basis, the failed banks held 8.98 percent of the total banking system assets (FDIC, 1997). While no geographic area of the United States was immune from the failures, some were hit harder than others. Nearly 60 percent of the failures occurred in 5 states: Texas, Oklahoma, California, Kansas and Louisiana (FDIC, 1997). Despite the economic significance of these bank failures, to date no paper has examined insider trading behavior around bank failures. We examine insider trading around four largest commercial bank failures that took place in the 1980's. The four failures that are examined are Continental Illinois National Bank and Trust Company, First City Bancorp., First Republic Bank and Mbank.

Given the lack of evidence regarding insider trading for banks and other financial institutions, we will discuss the empirical literature on insider trading behavior in non-financial firms. These studies have attempted to answer three important questions: (1) Is insider trading beneficial to capital markets? (2) Do insiders earn excess returns?, and (3) What kind of trading patterns do insiders exhibit relative to significant corporate news announcements? While all of these issues are interesting, our focus is on answering the third question for the banking industry.

Elliott, Morse and Richardson (1984) found evidence that insiders with private information purchased stock in their firms before value increasing announcements and sold stock before value decreasing information releases. Oppenheimer and Dielman (1988) examined insider trading patterns during the twelve months prior to announcements of dividend resumption (omission) and possible abnormal returns associated with such insider trading. They found evidence suggesting that insiders engaged in extensive net buying (selling) activity prior to dividend resumption announcements (omission). However, these pre-announcement insiders purchases failed to consistently earn excess returns, while pre-announcement insider sales enabled the insider to avoid negative abnormal returns. Furthermore, officers (high-information insiders) were found to earn larger profits than all insiders considered as a broad group.

The literature relating to non-financial firms indicates that the announcements of voluntary liquidations tend to benefit stockholders, whereas the announcement of filing for bankruptcy reduces equity prices. On that basis, Eysell (1991) hypothesized that the announcement of voluntary liquidations would be associated with an increase in insider purchases and that announcements concerning bankruptcy filings would lead to increased insider sales. As expected, when he empirically tested these hypotheses he found that corporate insiders were in fact heavy purchasers prior to liquidation announcements and during the period when the value of firms' stock kept rising. Furthermore, as expected, insiders of firms that eventually filed for bankruptcy were heavy net sellers. Additionally, those with the greatest access to information (high-information insiders) were found to be the heaviest purchasers before liquidation announcements and the heaviest sellers before bankruptcy announcements. These findings indicate that insider trading is prevalent in a manner consistent with the exploitation of private information. In a recent article, Hu and Noe (2001) derive the conditions which allow insiders to trade on their on their own behalf and in a manner which increases shareholder wealth.

Karpoff and Lee (1991) examined the insider trading patterns before new equity offerings. The authors found that insiders are net sellers of their firms' common stock prior to the announcements of new common stock and convertible debt issues. On that basis, they concluded that penalties for illegal insider trading did not deter insiders from using their superior information. Teal (1993) examined insider holdings, as opposed to insider trading, around the failure of savings and loan associations (S&Ls) during the period from June 1977 to March 1991. Teal argues that managers maintain a dual role. In the management role, the manager strives to maximize the value of his salary. In the shareholder role the manager strives to maximize his equity value. He suggests that these objectives often conflict, and consequently, we should expect managers with higher shareholdings to act on behalf of shareholders in general by assuming greater risk in order to increase the value of the deposit insurance option. Consistent with this, he found evidence that the insiders of S&Ls which subsequently failed owned 23.1% of the outstanding equity, compared to only 11.0% for S&Ls that did not fail. Bank failure announcements are generally found to have a negative effect on the stock prices of the failing banks as well as other commercial banks. For example, using weekly data Swary (1986) found that all banks experienced a 3% decline in the value of their common stock in reaction to the Continental Illinois failure. In a similar fashion, Lamy and Thompson (1986) show that all banks experienced a 1% decline in stock value in reaction to the Penn Central failure.

In this paper we examine insider trading around the failure of financial institutions. We examine insider trading around four major bank failures that took place in the 1980's. We begin by discussing the theory related to bank contagion and insider trading. We continue by proposing testable hypotheses and discussing the data and methodology. Next, the test results are presented followed by concluding comments.

THEORY RELATED TO BANK CONTAGION AND INSIDER TRADING

Banks are unique in that a significant proportion of their liabilities are payable at par on demand or at very short maturities. To meet these deposit withdrawals the bank may have to quickly liquidate assets at current market value. While banks are required to hold a small percentage of reserves against transaction or demand deposits, these reserves are not sufficient to meet a massive withdrawal of deposits. To help protect again such a "bank run" the FDIC insures bank deposits up to $100,000. This deposit guarantee, plus the fact that historically the FDIC charged a flat-rate insurance premium regardless of bank risk, encouraged banks to assume increased levels of risk. This moral hazard condition has been well documented in the literature (Kane, 1986). In effect, bank management has been encouraged to take excessive risks which, if successful, benefits bank shareholders as well as bank management. If the risky investments are unsuccessful and the solvency of the bank should be in jeopardy, the FDIC and tax payers will ultimately bare the financial burden. In addition, to prevent a massive run on the banking system, the Federal Reserve and the FDIC instituted a "too-big-to-fail" doctrine which lead to the protection of all liability holders at Continental Illinois banks regardless of amount or type of deposit or indebtedness. The argument put forth at that time was that, should Continental Bank fail, hundreds of other banks with correspondent banking relationship with Continental might also fail. This could then lead to widespread panic or contagion within the banking system that could eventually migrate to non-bank financial institutions. The size of the FDIC insurance fund was felt to be inadequate to deal with such a massive bank run.

Aharony and Swary (1983) make a distinction between pure or industry-specific contagion and firm or bank specific contagion. With industry-specific contagion the market has difficulty differentiating between individual banks and views the banking industry as more or less homogeneous. Given the difficulty that the market has in evaluating the quality of bank assets, in particular private loan contracts, problems at one bank are viewed as a likely problem for all banks and may illicit an irrational response toward the entire industry. With bank-specific contagion an adverse event at one bank is seen as being indicative of problems at other banks which share common characteristics, such as similar types of loan exposures or common product lines. In this case the market reaction is rational in the sense that bank performance is correlated to a common set of industry or macro economic variables. Industry-specific contagion is viewed as being more damaging than bank-specific contagion as it potentially impacts all banks, rather than shifting deposits and loans within the system from weak to healthy institutions. Furthermore, as Kaufman (1994) points out, the news may not be all bad since the failure of a major bank may significantly benefit its competitors. This was certainly the case for First Chicago Bank, the primary competitor of Continental Bank. In reviewing the evidence surrounding a number of major bank failures, Kaufman concludes that bank-specific rather than industry-specific contagion is more likely.

In a recent paper, Bougheas (1999) develops a model of bank contagion and concludes that bank failure alone is not sufficient to induce a "bank run" but must be accompanied by periods of economic instability. In terms of international bank failures, Jayanti, Whyte, and Do (1996) examine several bank failures in Britain and Canada and find no evidence of industry-specific contagion in Britain, while Canadian banks reacted adversely to several domestic bank failures. The authors conclude that the Canadian stock market primarily may have reacted to new bank regulations proposed immediately following the failures and that deposit insurance by itself may not be sufficient to prevent contagion effects.

Overall, the majority of evidence suggests that bank managers have little to worry about industry-wide contagion effects and may have an incentive to act on insider information relating to an impending bank failure. This inside information may encourage them to sells shares of their own stock prior to a bank failure if they perceive that bank-specific contagion will adversely impact their bank. Conversely, the impending failure may provide an incentive to purchase shares based upon inside information if the failed bank is a major a competitor. As in the case of First Chicago and Continental Bank, it seems likely that in certain cases both effects could be operative. Which effect dominates and leads to either insider net sales or purchases is an empirical question and is the central focus of this research.

TESTABLE HYPOTHESES

We argue that insiders have better information than other market participants regarding both the timing and likely effect of bank failures. Inter-bank payments and credit transactions, plus informal contacts allow insiders in one bank to be aware of problems occurring in other institutions. This is private information that generally would not be available to other market participants during the same time frame. Furthermore, insiders in healthy banks are in a better position to evaluate both the extent and ultimate consequences of the problems at troubled institutions and determine more accurately how their bank will be affected. Thus, its seems likely that insiders of non-failed banks might view the consequences of another bank failing quite differently than the general market. For example, if the general market has trouble differentiating between banks, a major bank failure might be perceived as a negative announcement impacting all commercial banks. On the other hand, insiders at the non-failed bank may view the failure of a competitor as an opportunity to obtain additional business. Based on this private information, in comparison to less informed investors, we expect that bank insiders will have an incentive to trade their own shares based on their perception of the impact of the impending failure of other another bank. An increase in insider purchases indicates that insiders view the failure as having a net positive impact. Insiders would treat the reduction in price caused by other market participants trading as an opportunity to purchase shares at a bargain. A reduced level of purchases or net selling by insiders would suggest that insiders are less optimistic about the impact of the failure on their bank than are other market participants. In this case, insiders would view the lower than expected decline in price as an opportunity to sell their shares at a high price.

THE DATA AND METHODOLOGY

The four largest bank failures that took place between 1982 and 1989 are identified from the annual reports of the FDIC on failed institutions. We selected these four failures because they were markedly larger than other failures that took place during that time period. First City, the smallest of the four banks examined, was nearly twice as large as the next largest failed commercial bank (FDIC, 2002). Because of their size, a more pronounced insider reaction is expected within the failed bank as well as in non-failed banks around the time of the failure. The four banks and selected information about the banks are provided in Table 1 (FDIC, 2002). In order to analyze the insider trading in other banks, a listing of national commercial banks is obtained from Standard and Poor's Compustat Data Files. All banks denoted as a national commercial bank, commercial bank, or commercial bank - not chartered are selected. Data regarding the open market trades of insiders in these banks is obtained from the Ownership Reporting System Master File compiled by the US Securities and Exchange Commission (SEC). Insider trading data is collected for the three years prior and the one year subsequent to each failure.

More specifically, we examine insider trading in the time period surrounding the failure. In any event study, it is necessary to define and event period. Loh and Rathinasamy (1995) select a six-month event period in their study of insider trading around dual class recapitalizations. Gosnell, Keown, and Pinkerton (1992) find that the level of insider selling increases over the final 5 months leading to the first public bankruptcy announcement of OTC firms. Sanders and Zdanowicz (1992) examine takeovers and find that target firm insiders know of impending takeovers as much as 542 days and on average 79.1 days prior to the first public announcement. In this paper, the "pre-event" period is defined as the four quarters prior to the failure (Quarters -1 to -4). The "post-event" period is defined as the four quarters subsequent to the event period (Quarters +1 to +4). In order to estimate how the failure has impacted the direction and extent of insider trading, an eight-quarter insider trading "comparison period" is identified which includes the two years just prior to the beginning of the pre-event period. These timeframes are selected to be consistent with the previous literature as well as to best utilize a limited amount of data available. While we use these timeframes, tests are conducted in a quarterly fashion, thereby permitting the identification of anomalies that occur at other time intervals. Figure 1 illustrates these definitions:

[FIGURE 1 OMITTED]

In order to differentiate the behavior of large volume insiders from small volume insiders, two measures of insider behavior are incorporated into the analysis. The "number of transactions" approach for measuring insider behavior is based on the number of trades that insiders made, regardless of size. Of interest here is the proportion of insider trades that were sales. On the other hand, the "shares traded" approach is based on the actual number of shares traded. Of interest is the net number of shares traded by insiders that were sales. This combination of measures is similar to that of Loh and Rathinasamy (1995) and Hirschey and Zaima (1989). Other measures have been suggested by Penman (1985), however, he finds no appreciable difference in the predictive ability of the measures.

THE EMPIRICAL RESULTS

The first step in the analysis is to compare the number of individual insider sales transactions as well as the total number of shares traded in each of the eight quarters surrounding the bank failure date to the number of insider sales transactions in the comparison period. In order to perform the analysis a dummy variable is assigned to each trade indicating whether the transaction represents a purchase or a sale. Trades that involve a sale of shares are assigned a value of 1,while trades that involved a purchase of shares are assigned a value of 0. A t-test is used to determine if the proportion of sales transactions for each of the eight event quarters is significantly different from the proportion of sales transactions during the comparison period. Given the small number of transactions in certain quarters, a similar test is performed for both one-year pre- and post- event periods.

Consistent with the literature which indicates that geographic proximity and differences (or similarities) in bank regulation may play an important role in insider behavior, each of the above mentioned tests are performed on the following two test samples and reported in Table 2. Panel A in Table 2 reports the results of a sample of non-failed banks located outside the state in which the failed bank is located. Panel B reports the findings for the sample of non-failed banks located within the same state as the failed bank.

The series of t-tests discussed above are then estimated for each of the two samples and for each of the four bank failures. Thus a total of 8 sets of statistical tests are made. The first two of the following four analyses compare the proportion of insider sales transactions and the average net size of these transactions between various groups of non-failed banks during various time periods. The final two analyses compare the proportion of insider sales transactions and the average net size of these transactions between the set of failed and non-failed banks during the same time periods.

Proportion of Sales Transactions: Non-failed Bank Comparisons

As mentioned above, the results of the first set of tests based upon the proportion of insider sales transactions or trades are presented in Table 2. In each of the two panels, insider trading at non-failed banks in various geographic categories (e.g., out-of-state and within-state) is being compared at different points in time as measured by the four bank failure event dates. In Panel A of Table 2, the results of the test on the out-of-state sample of non-failed banks are presented. To illustrate, examining the Continental Illinois bank failure, during the comparison period there were a total of 4,408 insider trades reported nationwide and 27.86% of them were insider sales. On the other hand, during quarter +1 of the post-event period there were a total of 749 insider trades of which approximately 20.96% were sales. This reduction in the proportion of sales suggests that on an out-of-state basis, insiders had become more optimistic about the effects of the Continental failure than other market participants, hence the reduction in share sales. A t-test on the difference in these proportions is significant at the one percent level. This procedure is then repeated for each of the remaining seven pre- and post- event quarters as well as for the one-year pre- and post-failure periods. The latter results indicate a reduction in the proportion of sales in both periods relative to the comparison period but a slight recovery during the post-event period.

In general, similar reductions in the proportion of insider sales are found in other event quarters surrounding the Continental Bank failure. Comparable results are presented for First Republic and First City banks. However, during two quarters both before and following the failure of Mbank, bank insiders nationwide increased the proportion of times they sold shares, suggesting that insiders were less optimistic about the effects of the Mbank failure on their own bank than was true for the other three bank failures. Overall, the evidence suggests that insiders in non-failing banks changed their trading behavior around bank failures, although the direction of the change is not identical for each failure.

In Panel B, the in-state non-failed banks test results are presented. Given that the number of insider trades is quite small for certain quarters, the aggregate one-year pre- and post- event comparisons are undoubtedly more reliable. The results for Continental Bank indicate a significant increase in the proportion of net sales during the one-year post-event period. For First Republic Bank the results are similar during both the pre- and post-event period, with a modest decrease in the proportion of net sales. On the other hand, Mbank reported a substantial decline during the post-failure period. First City Bank reported a dramatic drop in the proportion of net sales during the pre-failure period, followed by a significant recovery during the post-failure period. Thus, once again there is evidence of a change in trading behavior around the time of a bank failure.

Number of Share Transacted: Non-Failed Bank Comparisons

In this analysis we test for differences in the average number of shares traded. We do this by creating an index of purchases/sales. The index is created by multiplying the number of shares traded by +1 if the transaction is a purchase and by -1 if the transaction is a sale. Thus a purchase of 100 shares is given an index value of +100 and a sale of shares is given a index value of -100. A positive index value indicates that in the aggregate share purchases exceeded share sales. A negative total indicates the opposite. As before, a t-test is performed to determine if there is a significant difference between the average size of net sales or purchases between the comparison period and the each individual event quarter, as well as over the one-year pre- and post-event periods.

The test results for the average net number of shares traded are presented in Table 3. Generally, the tests show little change in insider trading around the time of the failures for the out-of-state sample of non-failed banks. However, there is evidence to suggest a change in insider trading patterns among in-state non-failed banks. This result is perhaps not surprising, since one would expect that in-state banks would be affected more by a failure than out-of state banks. For example, in the case of Continental Illinois the average number of shares for in-state bank trades was +1,404, indicating that the number of purchases exceeded the number of sales. During the four pre-event quarters (-1 to - 4) the balance starts to shift in the direction of either an increase in insider sales or a large reduction in the size of net purchases. During the four post-event quarters (+1 to + 4) the average net number of shares traded is now negative for quarters 2,3 and 4, indicating a significant shift towards insider selling. A comparison of the one-year pre- and post-failure periods dramatically illustrates these results as of the number of net shares purchased declines from approximately 519 to 91. An even more drastic shift takes place for First Republic and First City as large net purchase positions during the comparison period shift towards sizeable net sale positions during the pre- and post-periods. At the same time, there is a noticeable reduction in net sales during the post-event period relative to the pre-event period.

The reader will notice a marked difference between the results from the proportion of insider sales and the average number shares traded approaches. Recall that the proportion of insider sales treats each trade equally, regardless of the number of shares involved. The average net number of shares traded assigns those transactions that involve a larger number of shares more importance in the analysis. Thus, the differing results are an indication that insiders behave differently based upon the volume of shares that they trade.

Proportion of Transactions Approach: Failed Bank vs. Non-Failed Bank Comparisons

The comparisons presented in Table 4 employs the proportion of sales transactions approach in a manner analogous to the tests performed in Table 2 except that we are now comparing failed banks with non-failed banks. We perform significance tests for each of the four failing banks. Due to the limited amount of available data and the close time proximity of the three Texas bank failures, we combine the Texas bank failures into a single sample (labeled ALLTX). The event day for the combined Texas sample is the time period from April 20, 1988 (the date that First City Bank failed) to March 29, 1989 (the date that Mbank failed). The pre-event quarters and comparison period are defined relative to April 20,1988, while the post-event quarters are defined relative to March 29,1989. (Note: because of the sampling procedures employed the total number of observations in the three Texas failures may not equal the total number of observations in the combined test).

The values for N1, N2, PS1, and PS2 are found in Table 2. Table 4 simply tests for a statistically significant difference between PS1 and PS2, using N1 and N2 degrees of freedom. In Panel A, insider trading in the failed bank is compared to insider trading in non-failed banks nationwide. In the comparison period and in both the pre- and post- event periods, Continental Illinois consistently had a higher proportion of insider sales compared to the national proportion (the once exception being quarter - 4). For example, in the comparison period, the proportions were 50.7% and 27.7% for Continental and the sample of nationwide banks, respectively. The proportions of sales for failed and non-failed banks for the one-year pre-event period are 34.9% and 22.9%, respectively. For the post-event period the proportions are 47.8% and 23.5, respectively. In general, insiders at the failed banks were more frequent sellers than insiders at non-failed institutions.

The trading differences have a greater degree of statistical significance during the post- event period, suggesting that the dominance of insider selling increased somewhat after the failure event. In Panel B, trading in the failed bank is compared to trading in non-failed banks in the same state. The results are somewhat similar indicating that insiders at Continental were more likely to be selling share compared to insiders at in-state non-failed banks, although the results are not as statistically consistent. A similar set of results for the combined set of failed Texas banks is observed but here the trading differences are statistically significant.

Average Number of Shares Approach: Failed Bank vs. Non-Failed Bank Comparisons

The final comparison presented in Table 5 is based on the average number of shares traded by insiders in a manner analogous to the tests performed for Table 3. In the case of Continental Bank for both the comparison period and quarter -4 the average number of shares sold per trade was positive indicating that the number of purchases exceeded the number of sales. The reverse was true for the nationwide sample of non-failed banks for the comparison period. During the remaining three pre- event periods the average number of shares sold turns negative for Continental indicating that insider sales were growing. The same was true of Continental during the post- event period although the results were generally not statistically significant. For the entire pre-event period, the average net number of shares (66) is close to zero. Compared to the post-event period where the average net number of shares traded (-605) is decidedly negative. For the Texas banks and general and Mbank in particular, the average number of net sales increases dramatically between the one-year pre- and post-event periods.

CONCLUSIONS

In this paper we examine insider trading around the failure of financial institutions. We examine insider trading around four major bank failures that took place in the 1980's. The four failures that are examined are Continental Illinois National Bank and Trust Company, First City Bankcorp., First Republic Bank, and Mbank. We find that bank insiders changed their trading behavior in the time period surrounding these large bank failure announcements relative to a comparison time period. Insider trading patterns suggest that bank insiders are generally more optimistic than other insiders around the time of large bank failures. We find that large volume insiders behave differently than small volume insiders. Small volume traders appear to react more strongly to the bank failure than large volume insiders. We find that insiders in banks which are located in the same state as the failing bank behave differently than insiders in out-of-state banks around the time of a failure. Finally, we find that insiders in failing banks trade differently than insiders in non-failed banks. Insiders in the failed bank are larger sellers of their banks stock around the time of the failure than insiders in other banks.

REFERENCES

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Terrance J. Jalbert, University of Hawaii at Hilo

Ramesh P. Rao, Oklahoma State University

Chenchuramariah T. Bathala, Cleveland State University

Alan Reichert, Cleveland State University

Table 1: Bank Failure Summary Information

Bank State Data of Failure

Continental Illinois IL July 26, 1984
First City Bankcorp TX April 20, 1988
First RepublicBank TX July 29, 1988
Mbank TX March 29, 1989

Bank Total Deposits Total Assets

Continental Illinois 28,580,214,000 39,956,956,000
First City Bankcorp 9,275,170,000 12,374,490,000
First RepublicBank 19,946,416,000 31,277,123,000
Mbank 11,937,990,000 15,640,651,000

Bank Failure Type

Continental Illinois Assistance Transaction
First City Bankcorp Purchase and Assumption
First RepublicBank Purchase and Assumption
Mbank Assistance Transaction

This table shows the four largest bank failures that occurred
between 1982 and 1989 along with summary statistics
about the failed bank.

Table 2: Analysis of Insider Trading in Event Quarters relative to
Insider Trading in the Comparison Period Using the Transactions
Methodology

 Panel A: Out of State Non-Failed banks

 Q C. I. F. R. M. B. F. C.

 CP N 4408 5556 7676 4633
 PS 0.2786 0.2527 0.1998 0.2614

 -4 N 648 1226 881 915
 PS 0.2531 0.1631 0.2111 0.1956
 T 1.39 7.43 *** -0.78 4.50 ***

 -3 N 659 1365 757 926
 PS 0.2352 0.1055 0.2483 0.2192
 T 2.43 ** 14.49 *** -2.96 *** 2.80 ***

 -2 N 754 961 914 1525
 PS 0.2599 0.2206 0.2495 0.0852
 T 1.07 2.2 ** 3.30 *** 18.28 ***

 -1 N 728 851 962 1006
 PS 0.1676 0.2009 0.1954 0.2018
 T 7.20 *** 3.47 *** 0.33 4.19 ***

-1to-4 2790 4409 0.356 4443
 PS 0.2283 0.1649 0.2242 0.1636
 T 4.82 *** 10.87 *** 2.91 *** 11.48 ***

1 to 4 2936 3525 4302 3521
 PS 0.2333 0.2267 0.1934 0.2247
 T 4.39 *** 1.28 0.85 3.85 ***

 1 N 749 762 884 902
 PS 0.2096 0.2848 0.276 0.1929
 T 4.22 *** -1.85 * -4.85 *** 4.68 ***

 2 N 640 1055 870 735
 PS 0.2109 0.2 0.246 0.2667
 T 3.87 *** 3.87 *** -3.01*** -0.3

 3 N 868 823 1069 999
 PS 0.2465 0.2272 0.1637 0.2272
 T 1.99 ** 1.62 2.96 *** 2.32 **

 4 N 679 885 1479 885
 PS 0.2636 0.2644 0.1346 0.2192
 T 0.82 -0.73 6.54 *** 2.75 ***

 Panel B: In-State Non Failed Banks

 Q C. I. F. R. M. B. F. C.

 CP N 78 184 175 173
 PS 0.1795 0.4325 0.4286 0.5087

 -4 N 13 17 14 8
 PS 0.6923 0.2353 0.5714 0.5
 T -3.66 *** 2.41 ** -1.00 0.04

 -3 N 11 31 15 19
 PS 0 0..3226 0.4667 0.2632
 T 4.10 *** 1.97 * -0.28 2.22 **

 -2 N 8 14 27 32
 PS 0.125 0.3571 0.5185 0.2813
 T 0.41 1.08 -0.86 2.55 **

 -1 N 12 12 15 16
 PS 0.1667 0.6667 0.2667 0.375
 T 0.11 -1.10 1.31 1.02

-1to-4 44 74 71 76
 PS 0.2727 0.3649 0.4648 0.3158
 T -1.15 2.09 ** -0.51 2.93 ***

1 to 4 53 74 61 72
 PS 0.3208 0.3784 0.2951 0.4306
 T -1.81* 1.88 * 1.91 * 1.12

 1 N 18 19 16 9
 PS 0.2222 0.5789 0.375 0.5556
 T -0.39 -0.6 0.41 -0.26

 2 N 7 21 14 22
 PS 0.4286 0.381 0.2143 0.6364
 T -1.2 1.09 1.79 * -1.14

 3 N 21 26 20 19
 PS 0.2857 0.2692 0.45 0.3684
 T -0.97 2.46 ** -0.18 1.17

 4 N 7 8 11 22
 PS 0.5714 0.25 0 0.2273
 T -1.9 1.52 11.42 *** 2.84 ***

Table 3: Analysis of Insider Trading in Event Quarters relative
to Insider Trading in the Comparison Period
Using the Shares Traded Methodology

 Panel A: Out-of-State Non-Failed Banks

 Q C. I. F. R. M. B. F. C.

 CP N 4408 5556 7676 4633
 SH -2337 1427.2 60.19 1671.8

 -4 N 648 1226 881 915
 SH 265.15 97.4 157.46 219.11
 T -0.31 1.03 -1.38 0.94

 -3 N 659 1365 757 926
 SH 541.04 293.63 99.64 56.4
 T -0.34 0.88 -0.5 1.04

 -2 N 754 961 914 1525
 SH 527.96 95.06 61.6 230.24
 T -0.34 1.03 -0.04 0.93

 -1 N 728 851 962 1006
 SH 927.7 119.08 255.35 191.12
 T -0.38 1.01 -0.94 0.95

-1to-4 2790 4409 3560 4443
 SH 574.15 161.74 145.52 180.72
 T -0.34 0.98 -1.32 0.96

 1to4 2936 3525 4302 3521
 SH 3527.2 513.11 662.48 249.17
 T -0.68 0.69 -2.33 ** 0.92

 1 N 749 762 884 902
 SH 2818.2 70.12 1633.6 130.31
 T -0.59 1.05 -1.34 0.99

 2 N 640 1055 870 735
 SH 2205.8 234.35 106.99 83.47
 T -0.52 0.91 -0.45 1.02

 3 N 868 823 1069 999
 SH 7632.4 1778.4 722.92 48.46
 T -1.08 -0.19 -1.95 * 1.05

 4 N 679 885 1479 885
 SH 306.95 50.15 365.15 734.49
 T -0.31 1.06 -3.75 *** 0.59

 Panel B: In-State Non-Failed Banks

 Q C. I. F. R. M. B. F. C.

 CP N 78 184 175 173
 SH 1403.8 -749.3 191.57 -648

 -4 N 13 17 14 8
 SH 372.5 660.71 -8.5 8
 T 2.71 *** -2.70 *** 1.05 -1.59

 -3 N 11 31 15 19
 SH 2545 465.74 -156.9 564.84
 T -0.6 -2.77 *** 1.82 -2.30 **

 -2 N 8 14 27 32
 SH -179.3 7.79 -65.81 575.06
 T 2.13 ** -1.88 * 1.26 -2.65 ***

 -1 N 12 12 15 16
 SH 92.5 -4.67 867 -170.3
 T 2.05 * -2.03 ** -1.41 -1.02

-1to-4 44 74 71 76
 SH 518.84 347.61 123.32 348.99
 T 1.13 -2.96 *** 0.35 -2.40 **

 1to4 53 74 61 72
 SH 90.7 223.08 145.93 152.56
 T 1.49 -2.73 *** 0.26 -1.98 **

 1 N 18 19 16 9
 SH 720.28 -41.16 252.81 129.78
 T 0.35 -2.04 ** -0.24 -1.89 *

 2 N 7 21 14 22
 SH -348.9 -106.1 383.07 -91.18
 T 2.38 ** -1.73 * -0.89 -1.4

 3 N 21 26 20 19
 SH -87.62 640.08 -181.1 -122.13
 T 2.33 ** -3.15 *** 1.76 * -1.24

 4 N 7 8 11 22
 SH -533.7 359.5 283.18 643
 T 2.66 ** -2.52 ** -0.54 -2.59 **

In this table trading in the event quarters is compared to trading
in the comparison period using the shares traded methodology. N is
the number of insider trades. SH is the mean number of shares traded;
CP indicates the comparison period. T is the test statistic for the
two-sample T-test. *** indicates significance at the 1 percent level,
** indicates significance at the 5 percent level and * indicates
significance at the 10 percent level. I.D. indicates that there was
insufficient data to perform the test.

Table 4: Comparison of Insider Trading in the Failed Bank to
Insider Trading in Non-Failed Banks using the Transactions
Methodology

 Panel A: Nationwide Non-Failed Banks

 Q C. I. F. R. M. B. F. C. ALLTX

 CP N1 69 I.D. 41 3 5
 PS1 0.5072 0.4146 0.6667 0.20
 N2 4486 7851 4806 4809
 PS2 0.2769 0.2049 0.2703 0.2705
 T 3.78 *** 2.69 ** 1.19 -0.35

 -4 N1 17 I.D. I.D. I.D. 6
 PS1 0.1176 0.1667
 N2 661 923
 PS2 0.2617 0.1983
 T -1.75 * -0.19

 -3 N1 21 13 I.D. I.D. 23
 PS1 0.4286 0.9231 0.6522
 N2 670 1396 945
 PS2 0.2313 0.1103 0.2201
 T 1.76 * 10.50 *** 4.22 ***

 -2 N1 15 1 9 I.D. 14
 PS1 0.5333 0 1 .7143
 N2 762 975 941 1557
 PS2 0.2585 0.2226 0.2572 0.893
 T 2.05 * . 52.11 *** 4.98 ***

 -1 N1 10 I.D. 1 I.D. 7
 PS1 0.30 . 0.2857
 N2 740 977 1022
 PS2 0.1676 0.1965 0.2045
 T 0.86 . 0.44

-1 to -4 63 14 10 I.D. 50
 PS1 0.3492 0.8571 1.00 0.5600
 N2 2834 4483 3631 4519
 PS2 0.229 0.1682 0.2289 0.1662
 T 1.97 * 7.09 *** 110.59 *** 5.54 ***

 1 to 4 46 1 10 I.D. 10
 PS1 0.4783 1.00 1.00 1.00
 N2 2989 3599 4363 4364
 PS2 0.2349 0.2437 0.1948 0.1948
 T 3.25 *** . 134.27 *** 134.30 ***

 1 N1 4 I.D. 6 I.D. 6
 PS1 0.25 1 1.00
 N2 767 900 901
 PS2 0.2099 0.2778 0.2775
 T 0.16 48.35 *** 48.41 ***

 2 N1 16 1 2 I.D. 2
 PS1 0.3750 . 1 1
 N2 647 1076 884 884
 PS2 0.2133 0.2035 0.2455 0.2455
 T 1.28 . 52.10 *** 52.10 ***

 3 N1 20 I.D. 1 I.D. 1
 PS1 0.5500 1 1.00
 N2 889 1089 1089
 PS2 0.2475 0.169 0.169
 T 2.63 ** . .

 4 N1 6 I.D. 1 I.D. 1
 PS1 0.6667 1 1.00
 N2 686 1490 1490
 PS2 0.2668 0.1336 0.1336
 T 1.89 . .

 Panel B: In State Non-Failed Banks

 Q C. I. F. R. M. B. F. C. ALLTX

 CP N1 69 I.D. 41 3 5
 PS1 0.5072 0.4146 0.6667 0.20
 N2 78 175 173 176
 PS2 0.1795 0.4286 0.5087 0.5114
 T 1.70 ** -0.16 0.47 1.53

 -4 N1 17 I.D. I.D. I.D. 6
 PS1 0.1176 0.1667
 N2 13 8
 PS2 0.6923 0.500
 T -3.69 *** 1.32

 -3 N1 21 13 I.D. I.D. 23
 PS1 0.4286 0.9231 0.6522
 N2 11 31 19
 PS2 0 0.3226 0.2632
 T 3.87 *** 5.23 *** 2.68 **

 -2 N1 15 1 9 I.D. 14
 PS1 0.5333 . 1 0.7143
 N2 8 14 27 32
 PS2 0.125 0.3571 0.3171 0.2813
 T 2.23 ** . 4.91 *** 2.91 ***

 -1 N1 10 I.D. 1 I.D. 7
 PS1 0.300 1 0.2857
 N2 12 15 16
 PS2 0.1667 0.2667 0.375
 T 0.70 . -0.40

-1 to -4 63 14 10 I.D. 50
 PS1 0.3492 0.8571 1.00 0.5600
 N2 44 74 71 76
 PS2 0.2727 0.3649 0.4648 0.3158
 T 0.84 4.39 *** 8.98 *** 2.75 ***

 1 to 4 46 1 10 I.D. 10
 PS1 0.4783 1.00 1.00 1.00
 N2 53 74 61 62
 PS2 0.3208 0.3784 0.2951 0.2903
 T 1.60 . 11.97 *** 12.21 ***

 1 N1 4 I.D. 6 I.D. 6
 PS1 0.25 1.00 1.00
 N2 18 16 17
 PS2 0.2222 0.3750 0.3529
 T 0.10 5.00 *** 5.42 ***

 2 N1 16 1 2 I.D. 2
 PS1 0.3750 . 1 1.00
 N2 7 21 14 14
 PS2 0.4286 0.381 0.2143 0.2143
 T -0.23 . 6.90 *** 6.90 ***

 3 N1 20 I.D. 1 I.D. 1
 PS1 0.55 1 1.00
 N2 21 20 20
 PS2 0.2857 0.45 0.45
 T 1.73 * . .

 4 N1 6 I.D. 1 I.D. 1
 PS1 0.6667 1 1.00
 N2 7 11 11
 PS2 0.5714 0 0
 T 0.33 . .

In this table, insider trading in the failed bank is compared to
insider trading in the non-failed banks using the transactions
methodology. N1 and N2 are the number of insider trades in the
failed bank and non-failed banks respectively. PS1 and PS2 are
the proportion of trades in the failed bank and non-failed banks
that were sales respectively. T is the test statistic for the
two-sample T-test. *** indicates significance at the 1 percent
level. ** indicates significance at the 5 percent level and
* indicates significance at the 10 percent level. I.D. indicates
that there was insufficient data to perform the test.

Table 5: Comparison of Insider Trading in the Failed Bank to
Insider Trading in Non-Failed Banks using the Shares Traded
Methodology

 Panel A: Nationwide Non-Failed Banks

 Q C. I. F. R. M. B. F. C. ALLTX

 CP N1 69 I.D. 41 3 8
 SH1 452.3 -1283 81.67 70.87
 N2 4486 7851 4806 4806
 SH2 -2272 63.12 1588.3 1588.3
 T 0.32 -3.11 *** -0.95 -1.01

 -4 N1 17 I.D. I.D. I.D. 6
 SH1 2700.8 138.33
 N2 661 923
 SH2 252.61 217.28
 T 1.35 -0.76

 -3 N1 21 13 I.D. I.D. 23
 SH1 -1100 -166.8 -2424
 N2 670 1396 945
 SH2 573.94 297.45 66.62
 T -3.39 *** -6.86 *** -3.63 ***

 -2 N1 15 1 9 I.D. 14
 SH1 -1053 100 -1569 -74.21
 N2 762 975 941 1557
 SH2 520.54 93.81 5794 237.32
 T -2.86 *** . -3.63 *** -4.71 ***

 -1 N1 10 I.D. 1 I.D. 7
 SH1 -286.7 -861 140
 N2 740 977 1022
 SH2 914.16 264.74 185.46
 T -1.77 * . -0.28

 1-to-4 63 14 10 I.D. 50
 SH1 66.02 -147.8 -1499 -1100
 N2 2834 4483 3631 4519
 SH2 573.29 164.81 145.09 183.55

 T -0.88 -5.27 *** -3.99 *** -3.57 ***

 1to4 46 1 10 I.D. 11
 SH1 -604.6 -50 -3040 -2755
 N2 2989 3599 4363 4363
 SH2 3466.3 507.14 655.26 655.26
 T 2.09 ** . -3.35 *** -3.27 ***

 1 N1 4 I.D. 6 I.D. 7
 SH1 -11.5 -1243 -1051
 N2 767 900 900
 SH2 2768.9 1609 1609
 T -1.07 -2.36 ** -2.20 **

 2 N1 16 1 2 I.D. 2
 SH1 -704.9 . -3122 -3122
 N2 647 1076 884 884
 SH2 2178.2 -12132 111.36 111.36
 T -0.79 . -3.66 -3.66

 3 N1 20 I.D. 1 I.D. 1
 SH1 -481.4 -5000 -5000
 N2 889 1089 1089
 SH2 7450 706.32 706.32
 T -2.25 ** . .

 4 N1 6 I.D. 1 I.D. 1
 SH1 -1144 -11700 .
 N2 686 1490 1490
 SH2 298.17 364.54 364.54
 T -1.24 . .

 Panel B: In State Non-Failed

 Q C. I. F. R. M. B. F. C. ALLTX

 CP N1 69 I.D. 41 3 8
 SH1 452.3 -1283 81.67 70.88
 N2 78 175 173 173
 SH2 1403.8 191.57 -648 -648
 T -0.87 -3.21 *** 1.15 1.73 *

 -4 N1 17 I.D. I.D. I.D. 6
 SH1 2700.8 138.33
 N2 13 8
 SH2 -372.5 8.00
 T 1.70 0.79

 -3 N1 21 13 I.D. I.D. 23
 SH1 -1100 -166.8 -2424
 N2 11 31 19
 SH2 2545 465.74 564.84
 T -1.97 * -2.19 ** -3.86 ***

 -2 N1 15 1 9 I.D. 14
 SH1 -1053 100 -1569 -74.21
 N2 8 14 27 32
 SH2 -179.3 7.78 -65.81 575.06
 T -1.38 . -3.22 *** -2.50 **

 -1 N1 10 I.D. 1 I.D. 7
 SH1 -286.7 -861 140
 N2 12 15 16
 SH2 92.5 867 -170.3
 T -0.76 . 1.01

 1-to-4 63 14 10 I.D. 50
 SH1 66.02 -147.8 -1499 -1100
 N2 44 74 71 76
 SH2 518.84 347.61 123.32 348.99

 T -0.62 -2.95 *** -3.82 *** -372 ***

 1to4 46 1 10 I.D. 11
 SH1 -604.6 -50 -3040 -2755
 N2 53 74 61 61
 SH2 90.99 223.08 145.93 145.93
 T -0.54 . -2.96 ** -2.86 **

 1 N1 4 I.D. 6 I.D. 7
 SH1 -11.5 -1243 -1051
 N2 18 16 16
 SH2 720.28 252.81 252.81
 T -0.29 -3.61 *** -3.15 **

 2 N1 16 1 2 I.D. 2
 SH1 -704.9 -50 -3122 -3122
 N2 7 21 14 14
 SH2 -348.9 -106.1 383.07 383.07
 T -0.11 . -3.93 -3.93

 3 N1 20 I.D. 1 I.D. 1
 SH1 -481.4 -5000 -5000
 N2 21 20 20
 SH2 -87.62 -181.1 -181.1
 T -1.54 . .

 4 N1 6 I.D. 1 I.D. 1
 SH1 -1144 -11700 -11700
 N2 7 11 11
 SH2 -553.7 283.18 283.18
 T -0.49 . .

In this table, insider trading in the failed bank is compared to
insider trading in non-failed banks using the shares traded
methodology. N1 and N2 are the number of insider trades in the
failed bank and non-failed banks respectively. CP indicates the
comparison period. SH1 and SH2 are the mean number of shares
traded in the failed banks and non-failed banks respectively. T
is the test statistic for the two-sample T-test, ***, **, and
* indicate significance at the 1, 5 and 10 percent levels
respectively. I.D. indicates that there was insufficient data to
perform the test.