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How Legislation Affects Value: The Failure of Credit Card Cap Legislation
Marcia Millon CornettWe examine the credit card cap bill of November 1991. This legislation took only six business days to be formulated, pass in the Senate, and then die unenacted. We find that banks with high credit card exposure experienced significant negative abnormal returns during the period in which the bill looked certain to be enacted. When it became evident that the bill would fail, the banks did not recoup the full value lost.
* Recent finance literature has examined the impact of several regulatory events. One difficulty with these studies is that information tends to develop over a sometimes lengthy period of time. For example, Congress debated and revised the Financial Institutions Reform, Recovery, and Enforcement Act of 1989 over an eight-month period (see Sundaram, Rangan, and Davidson, 1992) and the FDIC Improvement Act (FDICIA) of 1991 over a 13-month period (see Chen, Cornett, Mehran, and Tehranian, 1998). Risk-based deposit insurance called for in FDICIA took Congress another nine months to debate (see Cornett, Mehran, and Tehranian, 1998). As of July 1999, Congress continues (after more than three years of debate) to consider the Leach Bill, legislation that would repeal sections of the Glass-Steagall Act. Such lengthy periods of review, revision, and debate make it difficult to pinpoint a clear event date for studies involving regulatory events.
In contrast to most legislative action, credit card interest-rate-cap legislation, which initially called for a cap of credit card interest rates of 14%, took only six business days in November 1991 to be suggested, see overwhelming Senate passage, and die unpassed. Therefore, this legislation presents an opportunity to examine a clearly identifiable event date of an issue that was a clear "surprise" to the financial markets.
In this paper, we use traditional event-study methods to examine the stock returns for bank holding companies over the six- (business) day period associated with credit card cap legislation, and in particular, how banks' reactions depended on their credit card exposures.
The results of the study indicate that during the period in which credit card cap legislation appeared to be imminent (November 12 through November 15, 1991) bank holding companies experienced significant negative abnormal returns that were related to the size of their credit card receivables and their credit card risk exposure. When it became evident that the credit card legislation would fail, the banks did not recoup the full value lost. It appears that the surprise involved in this legislation left a lasting impact on bank values. Despite the demise of this particular piece of credit card cap legislation, the market appears to have impounded this surprise permanently in the form of loss in value, possibly in anticipation of further attempts at such regulation (that no longer would be quite the unexpected event).
The paper is organized as follows. Section I discusses the events that took place while Congress debated the credit card interest-rate-cap legislation. Section II describes the data and methodology used in the empirical tests. Section III presents the results, and Section IV concludes the paper.
I. Chronology of Events and Their Expected Impact on Bank Stock Prices
In this section, we outline the series of events over the six- (business) day period from Tuesday, November 12 through Tuesday, November 19, 1991. Because stock price values are affected by other macroeconomic events, we also summarize other macroeconomic-oriented news events that occurred on each of the six days. Both categories of events are listed in Table 1.
On Tuesday, November 12, 1991, during a fundraising luncheon in New York, President George Bush suggested that the economy would recover faster from recession if banks reduced their credit card rates commensurate with other rate declines throughout the economy. [1]
Wednesday's Wall Street Journal quoted several consumer advocate and credit card watchdog groups as doubting that the Bush comments would spur banks to make any changes.
On the same day, the Wall Street Journal (WSJ) reported a one-point gain in 30-year Treasury bonds and attributed it to strong overseas demand. The Standard and Poor's (S&P) 500 rose 0.92%, and the S&P Financials increased 0.68%. The 90-day T-bill rate remained constant at 4.62%.
The next day, Senator Alfonse D'Amato introduced a bill in the Senate to cap credit card interest rates at 4% over the rate the Internal Revenue Service charges on unpaid taxes. Based on rates at that time, the cap would have been set at 14%, compared with the average of 18.94% charged by credit card issuers. The bill, which came as a complete surprise, breezed through the Senate by a 74-19 vote late Wednesday afternoon (although the story did not cross the Dow Jones News Wire until 7:32 a.m. Thursday). The administration backed away from the bill. On Wednesday night, President Bush said that cutting credit card rates "... isn't a government decision."
In other news, the producers' price index climbed 0.7% in October, the largest increase in a year. The S&P 500 increased 0.17%, and the S&P Financials rose 0.12%. The yield on 90-day T-bills rose 0.03% to 4.65%.
On Thursday, November 14, House speaker Foley announced "... a high degree of likelihood the House will take it [the credit card cap proposal] up next week." Meanwhile, the administration gave mixed signals about its intentions. Treasury Secretary Brady said that "... [although] the administration strongly supports lower credit card rates, ... we should let the marketplace determine the rates, not inflexible legislation that some estimate could reduce credit to Americans by $100 billion. This would only make the credit crunch worse and impede the economic recovery." Nevertheless, legislators were under considerable pressure in a pre-election year, as indicated by Senator D'Amato's comment that "... If they veto this bill, they'll be telling the American people they don't give a damn for the little guy." White House Press Secretary Marlin Fitzwater refused to say whether or not the President would veto the bill, saying "We'll have to wait and see."
In other news, consumer prices rose 0.1% in October, the smallest increase in seven months. The S&P 500 fell 0.07%, and the S&P Financials dropped 0.03%. The yield on 90-day T-bills fell 0.02% to 4.63%.
On Friday, November 15, President Bush backed away a little more from the credit card cap bill, although he did not promise a veto. Federal Reserve Board Chairman Alan Greenspan said the proposed cap would have "... a number of possible serious effects on the economy and financial institutions."
In other news, figures for October's industrial output were reported as being stagnant for the third consecutive month, September's inventory rose (for the first time since January 1991), and a survey showed that consumer confidence had fallen sharply. The yield on 90-day T-bills fell 0.04% to 4.59%. In heavy trading, the Dow Jones Industrial Average fell 120.31 points to 2943.20. The S&P 500 fell 3.66% and the S&P Financials dropped 4.37%. [2] The Washington Post reported that unnamed "traders and investors" cited "... news of a halt in Soviet oil exports, fears that General Motors might have trouble paying its quarterly dividend to shareholders, and a congressional proposal to restrict interest rates charged on credit cards" as causing the large drop. The New York Times reported analysts most frequently cited the credit card cap bill, "an abrupt downturn in biotechnology stocks" and "sudden reports of real estate problems of several leading insurers." The popular media cited the bill as the cause of the November 15 crash, the fifth largest DJIA point decline in market history.
Over the weekend, the bill lost much of its momentum. An editorial in the Monday, November 18, Wall Street Journal condemned the idea of a credit card cap as "spectacularly insane," adding that "Everyone else knows that consumers with real money to spend have better ways to borrow it. It would, however, harm the already beleaguered banks." Monday's WSJ also reported that the House was considering revising the proposal by delaying it for 18 months" ... to give the industry time to adjust rates on its own." The WSJ reported that "... if the House Democratic leadership embraces this plan, it could reach the House floor this week." Such endorsement was not forthcoming.
Tuesday's Washington Post reported that Congress had all but abandoned the credit card cap plan, and House Speaker Foley announced "... there is increasing evidence that the impact of an absolute cap might be to have consequences that were not entirely seen ... I don't think anything abrupt is desirable." The same day, the Wall Street Journal reported that Senate Minority Leader Bob Dole, who had voted for the D'Amato amendment, was now looking for a way to soften it.
The S&P 500 rose by 0.68%, and the S&P Financials increased 0.32%. The yield on 90-day T-bills fell 0.01% to 4.58%.
By late Tuesday, the credit card bill was killed, having been blocked in the House by the chairman of the Banking Committee, Henry B. Gonzalez.
The S&P 500 fell by 1.51%, and the S&P Financials declined by 2.08%. The yield on 90-day T-bills fell 0.05% to 4.53%.
Given the complete, and quick, turnaround of this legislation, we divide the six days into two periods. From Tuesday, November 12, 1991 through Friday, November 15, 1991, credit card cap legislation seemed certain to pass. By Monday, November 18, the bill was headed for certain demise, and on Tuesday, November 19, it died unenacted. Therefore, we examine the period of "passage" relative to the period of "demise" for credit card cap legislation. Our a priori expectations are that bank holding companies, one of the likely losers from this legislation, should experience value losses during the period of "passage" of credit card cap legislation [3] that would be recouped during the period of "demise."
Further, we expect that the greater the credit card receivables held by the bank, the greater the reaction to credit card cap legislation. If abnormal returns are negative and investors penalize all banks equally, regardless of their credit card exposure, then a type of contagion exists. Other studies examine the issue of contagion in relation to other events, such as dividend announcements, the 1982 Penn Square and Mexican debt problems, and the 1987 Brazilian debt crisis. Laux, Starks, and Yoon (1998) find that large revisions in dividends are accompanied by stock price reactions for industry rivals of the announcing firm unlikely to be affected by competitive realignments in the industry. Lamy and Thompson (1986) analyze the 1982 Penn Square problem and find contagion, but Karafiath and Glascock (1989) find none. Similarly, Bruner and Simms (1987) find temporary contagion at the time of the Mexican debt crisis, but Smirlock and Kaufold (1987) do not. Finally, Karafiath, Mynatt, and Smith (1991) find conta gion in BHC returns at the time of the 1987 Brazilian debt crisis, but Musumeci and Sinkey (1990) do not.
Because the interest rate ceiling is not equally binding on all credit card issuers, we look at the impact of the legislation as it relates to the rate the bank charges. We measure bank charges as credit card revenues divided by credit card receivables outstanding. We expect that those banks operating close to or above the cap (initially 14%) will experience a stronger response to credit card cap legislation. Because not all banks face the same average probability of default by credit card customers, we look at the impact of the legislation on the credit card risk exposure of the bank. We measure this exposure as credit card write-offs divided by credit card receivables, or, alternately, divided by credit card revenues. We expect that those banks with riskier customers will experience greater return fluctuations in response to the announcements pertaining to credit card cap legislation.
II. Data and Methodology
In this section, we describe the data and methodology used to examine credit card cap legislation.
A. Data
Our data comprise daily stock returns for bank holding companies (BHCs) with credit card receivables listed as an asset on their balance sheet. We obtained the initial set of banks from SMR Research Corp. (which provided bank total assets and credit card receivables at year end 1991 [4]) and bank stock prices from the S&P Daily Stock Price Record. In our final sample, we included only banks that went no more than 15 days without trading during the estimation and event periods, and banks that were among the top 150 in exposure to credit card receivables. For banks with fewer than 15 days without trading, the average of the bid-and-ask prices was used on the days when no trade occurred. This left us with a final sample of 84 banks.
Initially, we intended to use unexposed BHCs as a control group. However, there were few such BHCs, and many of them were traded irregularly; thus, the sample was too small to use. Instead, we ranked the sample of 84 exposed BHCs in descending order of credit card exposure and divided them into four quartiles of 21 banks each. We define exposure as credit card receivables divided by total assets, as listed by SMR Research Corporation. [5]
Table 2 lists the banks in the sample and provides the percentage of their assets comprised of credit card receivables. Table 2 shows that credit card exposure drops off rapidly (average of 10.89% of assets for the top quartile vs. only 3.59% for the second quartile). The third and fourth quartiles are essentially unexposed (average of 2.07% for the third quartile and 0.88% for the fourth quartile). So much of the exposure is concentrated in the top quartile that this group's dollar amount of credit card receivables was over twice as much as that of the other three groups combined.
We also examine the impact of the degree to which the credit card cap is binding for the bank, and the creditworthiness of the bank's credit card customers. Our data on credit card revenue and write-offs of credit card receivables are drawn from the 1991 FDIC Call Report Data tapes. We use these data to calculate three ratios: credit card revenues divided by credit card receivables (to measure the degree to which the credit card cap would be binding), credit card write-offs divided by credit card receivables, and credit card write-offs divided by credit card revenues (two measures of the creditworthiness of the bank's credit card customers).
Table 3 presents summary statistics for the ratios used in the study. Using 1991 data on credit card receivables, revenue, and write-offs to measure the impact of credit card cap legislation implicitly assumes that market analysts know the relative exposure of the banks. The data we use in our study are for the end of 1991, after the actual event. To check the stability of the measures, we collected 1990 data for EXP, REVREC, WRITREC, and WRITREV. The correlations of the ratios for the 1990 and 1991 data are 98.29%, 94.59%, 86.59% and 51.77%, respectively. Thus, over the two-year period, the values of EXP, REVREC, and WRITREC appear to be highly stable. The correlation for WRITREV is not quite as stable, but this could be due to the fact that write-offs are essentially a managerial decision. Because the size of credit card write-offs reflects managerial decisions, during the recession in the late 1980s and 1990, banks delayed writing off loans they believed would not be paid because of the consequent detrimental effect on net income. As the economy and bank profits picked up in 1991, banks wrote off many of the accumulated bad loans. Thus, we see the relatively high write-off ratios.
The statistics for the exposure measure, credit card receivables as a percentage of total assets, reinforce the discussion of Table 2. That is, credit card exposure drops off rapidly from MBNA's very high level of 57.62% to Northern Trust's 0.22%. Credit card revenues to credit card receivables average 13.52% and run as high as 22.41%.
The credit card cap bill proposed an initial interest rate cap of 14%. It appears that many of the sample banks are near or even exceed this cap. Thus, the legislation would certainly have affected the revenue of these banks had it been approved.
Write-offs to credit card receivables average 3.5 1% for the sample and are as high as 17.08%. Write-offs to credit card revenue average 26.07% and run as high as 132.83%. [6] Again, many of the sample banks are issuing credit cards to risky borrowers. Had credit card cap legislation passed, these customers would no longer have been eligible to receive credit, and would therefore have been lost to the banks.
B. Methodology
We estimate each security's abnormal return from the market model, i.e.,
[AR.sub.it] = [R.sub.it] - ([alpha] + [[beta]R.sub.mt]) (1)
where [AR.sub.it] = stock i's abnormal return on day t,
[R.sub.it] = stock i's raw return on day t,
[R.sub.mt] = CRSP unweighted index (including dividends) on day t, and
[alpha], [beta] = market model parameters from estimation period data.
We define the estimation period as the 110 trading days between June 4 and November 6, inclusive. We choose event windows from the period between Tuesday, November 12 to Tuesday, November 19, inclusive.
Absent from the case of the credit card bill is one condition that ordinarily clouds event studies of legislative events. Legislation typically is passed over a long period of time, and it is difficult to pinpoint an appropriate event date (see Binder, 1985). However, the time between Bush's suggestion that banks lower their rates and the demise of the bill in the House of Representatives was only a week. But because all firms in the sample have a common event date and because the firms under consideration in each test (BHCs, manufacturers, and retailers) have similar lines of business, we expect that abnormal returns are cross-sectionally correlated and that the standard assumption of independence is violated. Thus, a clustering problem exists.
Our initial tests use the crude dependence adjustment method (see Brown and Warner, 1980, and Jaffe, 1974) to deal with it. In its tests for significance of event-period abnormal returns, this method uses the standard deviation of the appropriate portfolio (BHCs, manufacturers, or retailers) during the estimation period. Since the event-period abnormal return is an out-of-sample prediction, we make the appropriate adjustment. This adjustment is particularly important when the independent variables (market returns) are unusually distant from the mean, as they are here. As Binder (1985) states "... if abnormal returns are measured as market model residuals or prediction errors, hypothesis tests about the average abnormal return which use the `portfolio method' introduced by Jaffe (1974) and Mandelker (1974) are well specified." The appropriate statistics for this and all other tests of abnormal returns appear in the Appendix.
We also conduct a regression analysis to examine the relation between banks' event-period abnormal returns and credit card exposure, the degree to which the proposed credit card cap was binding, and the creditworthiness of the banks' credit card customers. Although ordinary least squares has been widely used in the literature (see, Bruner and Simms, 1987; Musumeci and Sinkey, 1990), Karafiath, Mynatt, and Smith (1991) point out that since the abnormal returns are prediction errors, each has its own variance, and the standard ordinary least squares assumption of homoscedasticity is violated. Thus, a procedure using ordinary least squares will be misspecified and its tests will be less powerful than those of generalized least squares.
Accordingly, we use weighted least squares (see Judge, Hill, Griffiths, Lutkepohl, and Lee, 1988) in our cross-sectional regressions. To obtain efficient estimators, we weight observations in the cross-sectional regression by using the inverse of the standard error estimate for each bank for the 110-day estimation period from the market model.
We estimate multiple variations of the following regression:
[CAR.sub.(t-day)i] = [b.sub.1] [EXP.sub.i] + [b.sub.2] [REVREC.sub.i] + [b.sub.3] [WRITREC.sub.i] + [b.sub.4] [WRITREV.sub.i] + [e.sub.i] (2)
where
[CAR.sub.(t-day)i] = the t-day cumulative abnormal return for firm i
[EXP.sub.i] = credit card receivables as a percentage of total assets for bank i
[REVREC.sub.i] = credit card revenues as a percentage of credit card receivables for bank i
[WRITREC.sub.i] = credit card write-offs as a percentage of credit card receivables for bank i
[WRITREV.sub.i] = credit card write-offs as a percentage of credit card revenues for bank i
Consistent with the two distinct periods of momentum for credit card cap legislation, we look at regression results separately for each time period. That is, we look at the relation between bank credit card cap characteristics and abnormal returns during the period of credit card legislation passage separately from the relation during the period of the legislation's demise.
III. Empirical Results
This section presents the portfolio and regression results of the empirical analysis.
A. Bank Common Stock Returns
Table 4 presents abnormal portfolio returns and t-statistics for each of the six days over which credit card cap legislation was considered. We present results for the full sample of banks, and for the sample split into quartiles on the basis of credit card exposure. In addition to individual day abnormal returns, we report cumulative abnormal returns for both credit card cap legislation passage and demise.
The abnormal returns in Table 4 are predominantly negative during the period of passage and positive during the period of demise. Although banks in the full sample have no statistically significant abnormal returns, those in the top quartile do show significant abnormal returns for each of the three windows containing the bill's passage. In addition, the top quartile's abnormal returns are significant at the 0.10 level on November 15 (AR = -1.30%, t = -1.91). None of the other three quartiles has statistically significant abnormal returns during any of the days or windows considered.
These results suggest that credit card exposure most certainly affected abnormal announcement period returns. We note that if the credit card bill caused the negative abnormal returns of exposed banks, then we can infer that these BHCs should have earned offsetting positive abnormal returns on Monday and Tuesday when it became clear the bill was dead. Although we consistently find positive abnormal returns during the period of credit card demise, none of the reported abnormal returns is statistically significant. Further, the abnormal returns reported for the full sample for the two days of demise (November 18 and 19), 0.68%, is less than half the magnitude of the abnormal return for the four days during passage (November 12 through 15), -1.45%. For the top quartile banks, the recovery over November 18 and 19, 1.23%, is only one-third the loss in the four-day passage period, -3.63%.
We also estimate the banks' average correlations with each other to be about 0.06. Our estimation procedure assumes that each pair of bank stocks has the same correlation of returns and finds the value of that correlation which produces the specified portfolio variance. However, we find that even with a cross-sectional correlation as small as 0.06, an adjusted cross-sectional test (which assumes independence) is badly misspecified and rejects the null hypothesis far too frequently. For example, when we apply an adjusted cross-sectional test to the entire portfolio of 84 BHCs for each of the 110 days of the estimation period, t-values with absolute values exceeding two occurred for 48 of the 110 days (instead of five or six days). Thus, we do not report any bank tests that require independent residuals.
Because MBNA had such a large credit card exposure (57.62% of total assets), the abnormal returns reported in Table 4 could be driven by this single issuer. To test this, we estimate abnormal returns over the six days for the top quartile of banks excluding MBNA. Removing MBNA, the abnormal return for the November 12 through 15 window is -2.82% (t=-2.75, significant at the 0.01 level); for the November 13 through 15 window, -1.84% (t = 1.79, significant at the 0.10 level); and for November 15 alone, -1.27% (t = -1.78, significant at the 0.10 level). Again, we see an insignificant positive abnormal return at the bill's demise: the November 18 through November 19 abnormal return is 1.45% (t = 1.13).
The results in Table 4 lead us to conclude that the unexpected announcement of credit card cap legislation and the speed with which it started through the legislative process did indeed have a significant negative impact on bank values. The impact appears to be related to the size of the credit card exposure of the bank. However, the negative returns were not recovered when it became evident that the credit card cap bill was dead. Instead, the market appears to have impounded this surprise permanently in bank stock prices in the form of a loss of value, possibly in anticipation of further attempts at such regulation.
A natural implication of the results for banks is that department store credit card issuers, whose credit card rates were averaging about 20.4% at the time, were also impacted by the credit card cap legislation. To test this, we collected stock returns on November 15 (during passage) and November 18 (during demise) for five department stores (Dayton Hudson, Dillards, May, Mercantile, and Sears), each of which issues its own credit cards. All but three observations had abnormal returns with the expected sign. Dayton Hudson, May, and Sears had abnormal returns of 0.40%, -0.22%, and -1.39% on November 15, and Dayton Hudson, Dillards, May, and Sears had abnormal returns of 1.35%, 0.53%, 1.37%, and 1.99% on November 18. Only Dillards' and Mercantile's November 15 returns of 0.01% and 0.84%, and Mercantile's November 18 return of -0.71%, did not have the predicted sign.
Although such a small sample might only have value as anecdotal evidence, we were particularly interested to note that Sears' returns had the predicted sign on both days. Since Sears' Discover card is almost as widely accepted as Visa and Mastercard, we would expect Sears' stock returns to react to credit card legislation in a manner similar to that of banks with large credit card exposure.
B. Bank Regression Results
The stock return results listed in Table 4, and in particular the results by quartile, suggest that the abnormal returns are related to banks' credit card exposure. To test this more explicitly, we use weighted-least-squares regression analysis. This regression analysis allows us to examine the relation between abnormal returns and the degree to which the credit card cap bill would be binding, and between abnormal returns and the creditworthiness of the bank's credit card customers. Table 5 presents the regression results. We estimate regressions that analyze separately the relation between abnormal returns and these bank characteristics for the periods of credit card cap legislation passage and demise.
The first four regressions for each period (passage and demise) reported in Table 5 are regressions of cumulative abnormal returns on the various bank characteristics. Regression P1 strongly confirms the results in Table 4 that relate abnormal returns to credit card exposure. The regression coefficient on the variable EXP is -0.308 (t = -9.23). There is also a positive relation between cumulative abnormal returns and EXP in the period of credit card cap legislation demise. The coefficient in regression D1, 0.049 (t = 1.72), is smaller than that in the period of passage and only marginally significant (at the 0.10 level). Any recovery of lost market value was less closely related to the bank's credit card exposure.
Regression P2 in Table 5 examines the degree to which the constraint in the credit card cap bill might be binding on a bank. As listed in Table 3, some banks in the sample appear to be charging credit card rates at or above the initial 14% cap. Regression P2 for the period of passage indicates that the higher the ratio of credit card revenues to credit card receivables, the more negative the cumulative abnormal return. Thus, those banks that were most likely to be affected by the proposed cap level did experience the largest loss of value.
Regression D2, which examines the period of the credit card cap bill demise, reveals a positive coefficient for the variable REVREC of 0.035 (t = 1.66). Thus, any recovery of value lost was less significantly related to the binding nature of the 14% credit card cap.
Regressions P3 and P4 look at the relation between cumulative abnormal returns and credit card customer creditworthiness in two different ways; write-offs to credit card receivables (WRITREC) and write-offs to credit card revenues (WRITREV). The larger either variable, the lower the credit quality of the bank's credit card customers. In both cases, the coefficient on the independent variable is negative and significant at the 1% level, suggesting that the lower the credit quality of its credit card customers, the lower the abnormal return to the bank during the credit card cap legislation period.
The regression results indicate that those banks that serviced the lower-credit-quality customers experienced the lowest abnormal returns. During the period of demise, Regressions D3 and D4 indicate that any recovery of value lost is related to credit card customer creditworthiness, but again, the coefficients are smaller and less significant than those in Regressions P3 and P4.
Regressions P5 through P7 and D5 through D7 combine bank characteristics in multivariate regression analysis. In all cases, the coefficients become smaller and less significant. [7] Only the variable EXP is significant in any of these regressions. This finding suggests that credit card exposure is what drives the banks' abnormal returns during the period of credit card legislation.
Because retailers rely heavily on credit card sales of goods, but manufacturers do not, we expect that abnormal returns to retailers will be smaller than those of manufacturers during the period of credit card cap legislation, and larger than those of manufacturers during demise. Contrary to these expectations, retailers experience positive abnormal returns from November 12 through 14 (0.363%, 0.679%, and 0.304%, respectively). These returns are significantly larger than the manufacturers' abnormal returns on November 12 and 14 (-0.323% and -0.381%, respectively). The returns are not significantly different on November 13 (manufacturers' abnormal returns are 0.506% on November 13). Only on November 15, the last day of the passage period, are retailers' abnormal returns (-0.408%) significantly smaller than manufacturers' (0.333%).
Also, as we expected, retailers experience positive abnormal returns that are significantly higher (at the 0.01 level) than the negative abnormal returns to manufacturers on November 18 (0.802% compared to -0.205%), when it became clear that the credit card cap bill would have a difficult time passing. Thus, there is some evidence suggesting that retailers suffered from the credit card bill on Friday, when Alan Greenspan pointed out the detrimental effects of the bill on the economy, but rebounded on its Monday demise.
We find similar results when we delete the five department stores with their own credit cards from the sample, since their average returns on both days (-0.43% on Friday and 0.91% on Monday) are virtually indistinguishable from the average retailer's returns (-0.41% on Friday and 0.81% on Monday).
At the bottom of Table 5 we report regression results for cumulative abnormal returns regressed on credit card exposure for the top quartile banks and the top quartile with MBNA removed. During the period of credit card cap legislation, the relation between cumulative abnormal returns and EXP is negative, -0.326, and significant, t = -5.94, for the top quartile banks. Although significant at the 0.01 level, the relation between these two variables is stronger when the full sample is used as in Regression P1. During the bill's demise, we find no relation between cumulative abnormal returns and EXP for the top quartile banks. The results are similar when we remove MBNA, although the significance level again decreases.
IV. Conclusion
In contrast to most legislative action, credit card interest-rate-cap legislation, which called for a cap on credit card interest rates of 14%, took only six business days in November 1991 to be suggested, see overwhelming Senate passage, and die unpassed. Therefore, this legislation presents an opportunity to examine the impact of a clearly identifiable event that was a clear "surprise" to the financial markets.
In this paper, we use traditional event-study methods to examine the stock returns for bank holding companies over the six (business)-day period associated with credit card cap legislation, and in particular, its dependence on credit card exposure.
The empirical results in this study lead us to conclude that bank holding companies with high credit card exposure experienced significant negative abnormal returns during the period in which it appeared that the credit card cap bill would pass. The returns during this period were significantly related to banks' credit card receivables exposure (a negative relation), the degree to which the proposed credit card cap would be binding on banks (a negative relation), and the credit quality of banks' credit card customers (a positive relation). When it became evident that the credit card cap bill would fail, the banks did not recoup the full value lost during the initial four days.
It appears that the surprise involved with this legislation left a lasting impact on bank values. Despite the demise of this particular bill, the market appears to have impounded the surprise permanently in the form of a loss in value, possibly in anticipation of further attempts at passing similar legislation.
Appendix. Test Statistics Employed in the Empirical Analysis
Using the crude dependence adjustment method, the appropriate t-statistic for abnormal returns on Day E is:
t = [[[sigma].sup.n].sub.j=1] AR/n iE/[[sigma].sub.p] [[1 + (1/T) + [([R.sub.mE] - [R.sub.m]).sup.2]/[[[sigma].sup.T].sub.t=1][([R.sub.mt] - [R.sub.m]).sup.2]].sup.0.5] (A.1)
The appropriate t-statistic (mean-adjusted method) for testing whether market returns are significantly different from the average on Day E is:
t = [R.sub.mE] - [R.sub.m]/[[1/T-1 [[[sigma].sup.T].sub.I=1][([R.sub.mt] - [R.sub.m]).sup.2]].sup.0.5] (A.2)
Similarly, for a window of E days, the t-statistic is:
t = [[[sigma].sup.E].sub.e=1] [(R.sub.mE] - [R.sub.m]).sup.2]/[[E/T-1[[[sigma].sup.T].sub.t=1][([R.sub.mt] - [R.sub.m]).sup.2]].sup.0.5] (A.3)
Marcia Millon Cornett is a Professor of Finance and Jim Musumeci is an Associate Professor of Finance at Southern Illinois University-Carbondale.
Jim Musumeci acknowledges support by a research grant from the Kogod College of Business Administration. The authors also wish to thank Dave Davidson, Stu Feldstein, Rick Osborne, Joseph F. Sinkey, John Walter, an anonymous referee, the Editors, and participants in the Southern Illinois University-Carbondale and University of Wisconsin-Milwaukee workshops for their gracious comments and other assistance. Any errors are exclusively the authors' responsibility.
References
Ausubel, L., 1991, "The Failure of Competition in the Credit Card Market," American Economic Review (January), 50-81.
Binder, J., 1985, "Measuring the Effects of Regulation with Stock Price Data," Rand Journal of Economics (Summer), 167-183.
Brown, S. and J. Warner, 1980, "Measuring Security Price Performance," Journal of Financial Economics (September), 205-258.
Bruner, R. and J. Simms, 1987, "The International Debt Crisis and Bank Security Returns in 1982," Journal of Money, Credit, and Banking (February), 46-55.
Chen, A.H., M.M. Cornett, S.C. Mazumdar, and H. Tehranian, 1998, "An Empirical Analysis of the Effects of the FDICIA of 1991 on Commercial Banks," Research in Finance (forthcoming).
Cornett, M.M., H. Mehran, and H. Tehranian, 1998, "The Impact of Risk-Based Premiums on FDIC-Insured Institutions," Journal of Financial Services Research (April), 153-169.
Hannan, T. and A. Berger, 1991, "The Rigidity of Prices: Evidence from the Banking Industry," American Economic Review (December), 938-945.
Jaffe, J.F., 1974, "Special Information and Insider Trading," Journal of Business (July), 410-428.
Judge, G., R. Hill, W. Griffiths, H. Lutkepohl and T. Lee, 1988, Introduction to the Theory and Practice of Econometrics, New York, NY, Wiley.
Karafiath, I., and J. Glascock, 1989, "Intra-Industry Effects of a Regulatory Shift: Capital Market Evidence from Penn Square," Financial Review (February), 123-124.
Karafiath, I., R. Mynatt, and K. Smith, 1991, "The Brazilian Default Announcement and the Contagion Effect Hypothesis," Journal of Banking and Finance (June), 699-716.
Lamy, R. and G. Thompson, 1986, "Penn Square, Problem Loans, and Insolvency Risk," Journal of Financial Research (Summer), 103-111.
Laux, P., L. T. Starks, and P. S. Yoon, 1998, "The Relative Importance of Competition and Contagion in Intra-Industry Information Transfers: An Investigation of Dividend Announcements," Financial Management (Autumn), 5-16.
Mandelker, G., 1974, "Risk and Return: The Case of Merging Firms," Journal of Financial Economics (December), 303-335.
Musumeci, J. and J. Sinkey, Jr., 1990, "The International Debt Crisis, Investor Contagion and Bank Security Returns in 1987: The Brazilian Experience," Journal of Money, Credit, and Banking (May), 209-220.
Sinkey, J.F., Jr. and R. Nash, 1993, "Assessing the Riskiness and Profitability of Credit-Card Banks," Journal of Financial Services Research (September), 127-150.
Sinkey, J.F., Jr. and R. Nash, 1997, "On Competition, Risk, and Hidden Assets in the Market for Bank Credit Cards," Journal of Banking and Finance (January), 89-112.
Smirlock, M. and H. Kaufold, 1987, "Bank Foreign Lending, Mandatory Disclosure Rules, and the Reaction of Bank Stock Prices to the Mexican Debt Crisis," Journal of Business (July), 347-364.
Sundaram, S., N. Rangan, and W.N. Davidson, III, 1992, "The Market Valuation Effects of the Financial Institutions Reform, Recovery, and Enforcement Act of 1989," Journal of Banking and Finance (November), 1097-1122.
(1.) The issue of when and why interest rates might be sticky has earned a great deal of attention on its own, e.g., see Hannan and Berger (1991).
(2.) Although the more relevant percent decline was estimated by the November 24 New York Times to have been only the 134th worst in DJIA history. It was the 88th worst day for the CRSP unweighted index between January 2, 1962 and December 31, 1991 (a total of 7,420 trading days).
(3.) Ausubel (1991) suggests that the credit card market is not characterized by perfect competition. Perfect competition implies that the net present value of such operations is zero, but it does not generally imply that any firm's share price will remain unchanged if the project is dropped. Sinkey and Nash (1993, 1997) find banks with high credit card receivables are riskier than average banks, and suggest that Ausubel's findings of apparent excess profits could be compensation for this increased risk.
(4.) SMR Research Corp. obtained these values from Federal Reserve Y-9 reports.
(5.) We also divided the banks into quartiles with exposure defined as credit card receivables divided by equity; although the results of this classification had the correct sign, the statistical significance was generally weaker than when defining exposure in terms of credit card receivables as described in the text.
(6.) The 132.83% is reported by Shawmut Bancorp. Excluding Shawmut, the maximum value is 75.72%.
(7.) Multicollinearity does not appear to be a problem. The correlation coefficients between EXP and REV, WRITREC, and WRITREV are 0.219, 0.155, and 0.134, respectively. None of these is statistically significant.
Bank Holding Companies and Exposures
This table lists the bank holding companies
analyzed in this study by their credit card
exposure, which is defined as credit
card receivables (CC) divided by
total assets (TA)
Top Quartile BHCs CC/TA
MBNA Corp. 0.576
Security Bancorp, Inc. 0.236
Norwest 0.152
Colorado National Bankshares 0.150
Chase Manhattan 0.102
First Bank System, Inc. 0.100
Banc One Corp. 0.093
Citicorp 0.083
BankAmerica 0.079
First Chicago 0.079
Bank of NY, Inc. 0.076
Wells Fargo 0.069
Signet Banking Corp. 0.067
Central Fidelity Banks 0.063
Wachovia 0.060
Mercantile Bnacorp 0.058
Mellon Bank 0.051
First Tennessee Nat'l Corp. 0.050
Crestar Financial Corp. 0.049
INB Financial Corp. 0.048
Firstar Corp. 0.048
Second Quartile BHCs
Ameritrust Corp. 0.047
Comerica Inc. 0.047
Commerce Bancshares 0.044
BayBanks Inc. 0.044
NCNB 0.043
Chemical 0.043
First Interstate 0.043
Society Corp. 0.041
Dominion Bankshares Corp. 0.040
Northeast Bancorp Inc. 0.039
Barnett Banks 0.037
Central Bancshares of the South 0.034
United Missouri Bancshares, Inc. 0.032
Valley Nat'l Corp. 0.030
First Security Corp. 0.029
National City Corp. 0.029
First Virginia Banks, Inc. 0.027
Star Banc Corp. 0.027
First Union 0.027
West One Bancorp 0.026
Provident Bankshares Corp. 0.026
Third Quartile BHCs
WestAmerica Bancorp 0.026
Pacific Western Bancshares 0.026
First Hawaiian Inc. 0.025
SunTrust Banks 0.025
First of America Bank Corp. 0.024
Fifth Third Bancorp 0.023
Huntington Bancshares 0.022
Security Pacific 0.021
Bancorp Hawaii, Inc. 0.021
MNC Financial, Inc. 0.021
Keycorp 0.021
Amsouth Bancorporation 0.021
First Bancorporation of Ohio 0.020
PNC Financial 0.019
Integra Financial Corp. 0.018
United Carolina Bancshares 0.018
Southtrust Corp. 0.017
First Alabama Bancshares 0.017
Wilmington Trust Corp. 0.017
Boatmen's Bancshares 0.017
Citizens First Bancorp 0.017
Lowest Quartile BHCs
NBD Bancorp 0.015
First Fidelity Bancorp, Inc. 0.013
Valley Bancorporation 0.013
Dauphin Deposit Corp. 0.013
First Florida Banks Inc. 0.013
Michigan National Corp. 0.012
Manufacturers National Corp. 0.012
First Empire State Corp. 0.011
Valley National Bancorp. 0.011
Deposit Guaranty Corp. 0.010
Fleet/Norstar Financial Group 0.009
Marshall & Ilsley Corp. 0.008
Riggs National Corp. 0.008
Mercentile Bankshares 0.008
US Bancorp 0.006
UJB Financial Corp. 0.005
Midlantic Corp. 0.005
Bank of Boston 0.004
Shawmut 0.004
First City Bancorp of Texas 0.004
Northern Trust Corp. 0.002
Descriptive Statistics
This table lists descriptive statistics for 84 bank holding companies in the period around credit card cap legislation. Credit card receivables and total assets are obtained from SMR Research Corp., and credit card revenues and credit card write-offs are taken from the FDIC Call Report data tapes.
Mean (%) Standard Deviation (%)
Credit Card (CC) receivables as a percentage 4.34 6.98
of total assets (EXP)
CC Revenues as a Precentage of 13.52 4.32
CC receivables (REVERC) [a]
CC Write-offs as a precentage of 3.51 2.26
CC receivables (WRITREC)
CC Write-offs as a precentage of 26.07 16.10
CC revenue (WRITREV)
Minimum (%) Maximum (%)
Credit Card (CC) receivables as a percentage 0.22 57.62
of total assets (EXP)
CC Revenues as a Precentage of 0.67 22.41
CC receivables (REVERC) [a]
CC Write-offs as a precentage of 0.04 17.08
CC receivables (WRITREC)
CC Write-offs as a precentage of 1.43 132.83
CC revenue (WRITREV)
(a.)One bank, Mercantile Bancorp, had
its credit card revenues omitted on the
bank data tapes and was, therefore,
removed from tests involving this variable.
Bank Holding Company Abnormal Returns
This table presents abnormal returns and t-statistics (in parentheses) for 84 bank holding companies around each of six events. Results are presented for the full sample and for the sample divided into quartiles based on credit card exposure (credit card receivables divided by total assets).
Event Date Full Sample Top Quartile Quartile 2 Quartile 3 Quartile 4
AR AR AR AR AR
11/12 -0.60% -1.02% -0.49% -0.47% -0.42%
(-0.94) (-1.49) (-0.60) (-0.64) (-0.47)
11/13 -0.24 -0.42 -0.01 -0.11 -0.42
(-0.38) (-0.62) (-0.01) (-0.15) (-0.48)
11/14 -0.12 -0.90 0.18 -0.21 0.44
(-0.19) (-1.32) (0.22) (-0.29) (0.50)
11/15 -0.49 -1.30 0.91 -0.62 -0.94
(-0.77) (-1.91) [*] (1.11) (-0.86) (-1.07)
11/18 0.37 0.50 0.82 0.59 -0.44
(0.58) (0.73) (0.99) (0.80) (-0.50)
11/19 0.32 0.73 0.57 -0.09 0.06
(0.50) (1.07) (0.69) (-0.13) (0.07)
CAR CAR CAR CAR CAR
11/12-15 -1.45% -3.63% 0.59% -1.41% -1.34%
(-1.14) (-2.68) [***] (0.35) (-0.97) (-0.77)
11/13-15 -0.85 -2.61 1.09 -0.94 -0.93
(-0.77) (2.23) [**] (0.76) (-0.75) (-0.61)
11/14-15 -0.61 -2.19 1.10 -0.83 -0.51
(-0.68) (-2.29) [**] (0.94) (-0.75) (-0.41)
11/18-19 0.68 1.23 1.39 0.49 -0.38
(0.76) (1.28) (1.19) (0.48) (-0.30)
(***.)Significant at the 0.01 level.
(**.)Significant at the 0.05 level.
(*.)Significant at the 0.10 level.
Regression Results
Weighted least squares regressions of CAR for bank holding companies around credit card cap legislation. Results for versions of the following regression are reported (t-values are reported in parentheses).
[CAR.sub.(t-day)i] = [b.sub.1]([EXP.sub.i]) + [b.sub.2]([REVREC.sub.i]) + [b.sub.3]([WRITREC.sub.i]) + [b.sub.4]([WRITREV.sub.i]) + [e.sub.I] (1)
CAR Regression EXP REVREC WRITREC
11/12-15 P1 -0.308 -
(Passage) (-9.23) [***]
P2 - -0.100 -
(-2.95) [***]
P3 - - -0.399
(-2.98) [***]
P4 - -
P5 -0.318 0.015 -
(-8.29) [***] (0.51)
P6 -0.311 - 0.018
(-8.25) [***] (0.17)
P7 -0.309 - -
(-8.25) [***]
11/18-19 D1 0.049 - -
(Demise) (1.72) [*]
D2 - 0.035 -
(1.66) [*]
D3 - - 0.183
(2.19) [**]
D4 - - -
D5 0.034 0.023 -
(1.04) (0.95)
D6 0.026 - 0.148
(0.83) (1.57)
D7 0.029 - -
(0.91)
Top Quartile
11/12-15 (Passage) -0.326 - -
(-5.94) [***]
11/18-19 (Demise) 0.036 - -
(0.77)
MBNA Removed
11/12-15 (Passage) -0.191 - -
(-2.14) [**]
11/18-19 (Demise) 0.086 - -
(0.86)
CAR WRITREV [a] Adjusted [R.sup.2]
11/12-15 - 0.503
(Passage)
- 0.085
- 0.087
-0.050 0.086
(2.97) [***]
- 0.499
- 0.498
0.000 0.497
(0.03)
11/18-19 - 0.035
(Demise)
- 0.021
- 0.044
0.022 0.038
(2.08) [**]
- 0.045
- 0.040
0.017 0.036
(1.46)
Top Quartile
11/12-15 (Passage) - 0.730
11/18-19 (Demise) - 0.057
MBNA Removed
11/12-15 (Passage) - 0.260
11/18-19 (Demise) - 0.056
(***.)Significant at the 0.01 level.
(**.)Significant at the 0.05 level.
(*.)Significant at the 0.10 level.
(a.)EXP = Credit card receivables as a percentage of total assets; REVREC = credit card revenues as a percentage of credit card receivables; WRITREC = credit card write-offs as a percentage of credit card receivables; and WRITREV = credit card write-offs as a percentage of credit card revenues.
How Legislation Affects Value: The Failure of Credit Card Cap Legislation
Legislative action in the United States most often occurs over a period of several months. Such lengthy periods of deliberation make it difficult to pinpoint a clear event date when examining the economic impact of the legislation on market values of affected firms. This study examines the credit card cap bill of November 1991, legislation that took only six business days to be suggested, see overwhelming Senate passage, and die unenacted.
We find that banks experienced significant negative abnormal returns during the period in which the bill looked certain to be enacted. These returns were related to a bank's credit card exposure, the extent to which the credit card cap would be binding on a bank, and the creditworthiness of the bank's credit card customers. When it became evident that the bill would fail, banks did not recoup the full value lost.
Our data comprise daily stock returns for 84 bank holding companies (BHCs) with credit card receivables listed as an asset on their balance sheet. We rank the sample of exposed BHCs in descending order of credit card exposure and divide them into four quartiles of 21 banks each. We define exposure as credit card receivables divided by total assets.
We also examine the impact of the degree to which the credit card cap is binding on the bank and the creditworthiness of the bank's credit card customers. We do this by collecting data on credit card revenue and write-offs of credit card receivables. Using this data, we calculate three ratios: credit card revenues divided by credit card receivables (to measure the degree to which the credit card cap would be binding), credit card write-offs divided by credit card receivables, and credit card write-offs divided by credit card revenues (two measures of the creditworthiness of the bank's credit card customers).
It appears that the surprise legislation left a lasting impact on bank values. Despite the demise of this bill, the market appears to have impounded the surprise permanently in the form of a loss in value, possibly in anticipation of further attempts at similar legislation.
We estimate each security's abnormal return for the period between Tuesday, November 12 and Tuesday, November 19, inclusive. We also conduct a regression analysis to examine the relation between banks' event-period abnormal returns and credit card exposure, the degree to which the proposed credit card cap was binding, and the creditworthiness of the banks' credit card customers.
Consistent with having two distinct periods of momentum for credit card cap legislation (expected passage, from November 12 through November 15, and demise from November 18 through November 19), we conduct empirical analyses separately for each time period.
We find that abnormal returns for the sample banks are predominantly negative during the period of expected passage and positive during the period of demise. Although banks in the full sample had no statistically significant abnormal returns, those in the top quartile did during the period of the bill's expected passage. If the credit card bill caused the negative abnormal returns of exposed banks, then it is reasonable to infer that these BHCs should have earned offsetting positive abnormal returns as it became clear the bill was dead. Although we consistently find positive abnormal returns during the period of the credit card legislations' demise, none of the reported abnormal returns is statistically significant.
Regression analysis strongly confirms the results relating to abnormal returns and credit card exposure. Regressions for the period of expected passage indicate that the closer the bank operated to the proposed credit card cap, the more negative the cumulative abnormal return. Thus, those banks that were most likely to be affected by the proposed cap level experienced the largest loss of value. Any recovery of lost value is significantly related to the binding nature of the 14% credit card cap. Also, the lower the credit quality of its credit card customers, the lower the abnormal return to the bank during credit card cap legislation period. During the period of demise, regression results indicate that any recovery of lost value is related to credit card customer creditworthiness, but again, the recovery is smaller than the earlier losses.
COPYRIGHT 1999 Financial Management Association
COPYRIGHT 2000 Gale Group
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