On competition, risk, and hidden assets in the market for bank credit cards

ELSEVIER

Journal of Banking & Finance 21 (1997) 89-112

Journalof BANKING & FINANCE

On competition, risk, and hidden assets in the market for bank credit cards Robert C. Nash a, Joseph F. Sinkey Jr. b,, a Department of Economics and Finance. Robert G. Merrick School of Business, The UniversiO' of Baltimore, Baltimore, MD 21201-5779, USA b Department of Banking and Finance, Terry College of Business, The Universi~' of Georgia, Athens, GA 30602-6253, USA

Received 16 July 1994; accepted 22 April 1996

Abstract The market for credit cards has been the subject of recent attention and controversy because of 'high' profits earned on credit cards and substantial premiums on the resale of credit-card receivables. This paper estimates risk-return profiles for credit-card banks and explores the role of intangible assets in determining resale premiums on credit-card receivables. In addition, the effects on the resale market of securitization and the opportunity cost of acquiring new accounts are analyzed. Using alternative measures of risk and alternative control groups, we find, for the years 1989 to 1995, that credit-card banks earned significantly higher returns on assets but that these returns were associated with greater risk-taking. Analysis of premia for the years 1993 to 1995 suggest that acquiring banks pay higher premia for mid-sized regional accounts than for larger, national portfolios, perhaps because of richer cross-selling opportunities. JEL classification: G21; D40; L10; M41 Keywords: Credit cards; Credit-card banks; Intangible ('hidden') assets; Resale premiums; Securitiza-

tion

* Corresponding author. Tel.: (+1) 706-542.3649; fax: (+1) 706-542.9434; e-mail: j sinkey @cbacc.cba.uga.edu. Nash: tel= (+1) 410-837.4996; fax: (+1) 410-837.5722; e-mail: rnash@ ubmail.ubalt.edu. 0378-4266/97/$17.00 Copyright © 1997 Elsevier Science B.V. All fights reserved. PII S037 8-4266(96)00030-1

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R.C. Nash, J. F. Sinkey Jr./Journal of Banking & Finance 21 (1997) 89-112 Credit-card lending is a competitive market consisting o f many thousands o f card issuers, all free to establish their own prices and other lending terms. The credit-card market has changed significantly over the past f e w years. Board of Governors Federal Reserve System Statement to Congress, February 9, 1994

1. Introduction If credit-card lending is as highly competitive as the epigram suggests, then, except for a normal return to capital, excess profits should be competed away. According to Ausubel (1991), high accounting returns (i.e., "three-to-five times the ordinary rate of return in banking during the period 1 9 8 3 - 1 9 8 8 " , p. 50) and the substantial premiums paid for credit-card receivables were indicators of a 'failure of competition' in the credit-card market. Ausubel's findings were a paradox. ~ He attributed the quandary, in part, to irrational behavior in which consumers did not take account of the high probability of paying interest on outstanding balances. During the 1980s, the combination of large balances and 'sticky' interest rates generated large and stable revenues for credit-card lenders. Since then, increased competition and restrictive monetary policy have dropped credit-card interest rates precipitously 2 while profit rates remain high. Although an 'irrational' explanation might account for consumer behavior in the short run, over the longer run, especially with the adverse publicity associated with the pricing of credit-card services (resulting in greater disclosure) and with increased competition (making more accurate information available more readily), the explanation of irrational behavior begs for an alternative empirical story. Our research draws on two building blocks: (1) risk measurement, e.g., as proxied by Hannah and Hanweck's accounting model of bank risk (Hannan and

I For his test period, 1983-1988, Ausubel assumed that 4,000 firms and lack of regulatory barriers to entry would be hospitable for 'perfect competition'. The environmentfor competitionin 1994 would appear to be even more hospitable as the GAO (1994) describes the U.S. credit-card industry as consisting of roughly 6,000 card issuers. 2 Regarding rate competition, when interest rates on credit cards were sticky, they hovered around 20 percent. We do not focus directly on the stickinessof credit-card rates nor on search or switch costs. A GAO (1994) study suggests that rates are less sticky, at least for low-risk users. As a result of increased price competitionamong card issuers, the average card rate fell to 16.83 percent in 1993, and it has continuedto decline in 1994 and 1995. For example, Wachovia Corp. offers a Prime-For-Life product pegged to whatever the prime rate is (9% then, American Banker (February 10, 1995, p. 12)).

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Hanweck, 1988), and (2) the role of intangible or hidden assets as captured by the notion of Kane and Unal (1990) of 'hidden capital'. We present a risk-return explanation for the 'abnormal' returns generated by credit-card banks (CCBs). In addition, we consider the cost of obtaining new card accounts by acquisition versus other methods. Our findings indicate that accounting returns for 1989 to 1995 have continued to be several times the ordinary return in banking. Our analysis also suggests, however, that credit-card banks are riskier than ordinary banks. Moreover, they were riskier than ordinary banks over the years 1984 to 1988. We also extend existing research on credit-card banks by analyzing the role of 'hidden assets' in the premium paid for credit-card receivables. Specifically, we contend that banks purchasing credit-card receivables also acquire a 'hidden asset' representing the opportunity to cross-sell other products and otherwise develop new customer relationships. The decline in the size and number of deals, many replaced by securitizations, suggests that would-be sellers might be more eager to retain these hidden assets and the cross-selling opportunities. Although the value of intangible assets is difficult to estimate, the process does permit buyers to 'play games with the numbers' to justify high premiums. In addition, opportunity cost plays an important role since the cost of purchasing new accounts can be substantially less than the expense of acquiring them by other methods. The paper continues in six sections. Section 2 provides some background on the credit-card controversy and develops our hidden-assets hypothesis. Section 3 presents the methods, samples, and data while Section 4 provides empirical evidence on accounting measures of risk and return for CCBs, banks with 75 percent or more of total assets invested in credit cards or related plans. 3 Section 5 provides analysis of market premiums on the sale of credit-card receivables, including the case of MBNA, a CCB that came on the market in January 1991 as an initial public offering. The effects of the rapid growth of the securitization of credit-card receivables also are analyzed. The last section summarizes and concludes the paper, and indicates areas for further research.

2. Background: Economic considerations and the hidden-assets hypothesis Many observers have been puzzled by the high accounting profits and the high premiums on the resale of credit-card receivables that characterize the bank-card industry. Ausubel (1991), Calem (1992), and Calem and Mester (1993) attempt to

3 We use the 75-percentthreshold of Sinkey and Nash (1993) and Ausubel (1991) as the minimum investment in credit cards for a CCB. On average, our sample CCBs invested over 93 percent of their assets in credit cards during the period 1984 to 1993.

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understand the behavior of this market by examining, among other things, default risk, search costs, and switching costs. Ausubel and Calem both use cardholder delinquency rates, among other measures, as a proxy for the risk of credit-card receivables and argue that greater risk fails to justify the high returns earned by credit-card banks. Regarding search costs, the costs consumers face acquiring information about card rates and terms, Calem claims that such an explanation " s e e m s inconsistent with important aspects of the bank-card industry" (p. 4). Ultimately, he contends that switching costs (difficulties consumers encounter when switching accounts) offer the most appealing solution to the credit-card puzzle. Nevertheless, Calem concludes that " i t is too early to draw firm conclusions or to rule out some other explanation for the puzzling behavior of the bank-card m a r k e t " (p. 13). 2.1. The hidden-assets hypothesis and economic returns in banking

Hidden assets (what Kane and Unal (1990) call 'hidden capital') exist whenever the economic or market values of bank assets and liabilities differ from their accounting or b o o k values, based on historical cost or generally accepted accounting principles (GAAP). 4 These differences become most evident when a bank experiences financial difficulties, when bank mergers or acquisitions occur, or when a bank sells assets. The important concept is that economic value, the capitalized future cash flows derived from an asset, may include hidden components not captured by G A A P . Both tangible and intangible ( ' h i d d e n ' ) assets are expected to generate future cash flows for their owners. While a tangible asset can, in principle, be bought and sold separately, an intangible asset has value only in conjunction with other tangible assets or with a fight granted by a corporation or government. It is in this sense that the asset is said to be hidden. For a bank, tangible financial and nonfinancial assets include securities, loans, computers, ATMs, and buildings. Bank intangible assets represent 'going-concern values' such as the values of government guarantees, customer relationships, core deposits, and off-balance sheet activities. Hidden assets and hidden costs (e.g., unbooked reserves for expected loan losses) play important roles in determining economic or real returns in banking. 5 Moreover, since the accounting concept of intangible 'goodwill' is

4 Kane (1993) provides an excellent exposition of the itemization issues involved in classifying bank assets as tangible or intangible, and of the need to recognize the values of unbooked intangible assets and liabilities. This section draws on his work and that of Kane and Unal (1990). 5 See Kane and Unal (1990) for a theoretical and empirical analysis of hidden capital, Kareken (1987) for the importance of off-balance sheet activities or contingent commitments to banks, Merton (1977, 1978) for the role of government guarantees in valuing insured depositories, and Hodgman (1961, 1963) and Kane and Malkiel (1965) for the role of customer relationships in banking.

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recorded in the United States only when accounting for a bank's purchase price, hidden assets and hidden costs remain invisible until an asset purchase occurs. For example, in bank mergers in which premia are paid for target banks, hidden assets or goodwill drive merger prices. When a bank sells or spins off particular assets, the purchaser expects to receive future cash flows based on both the explicit and implicit assets acquired. For our purposes, when a bank buys another institution's credit-card receivables, it acquires more than just the outstanding loans. It also instantly acquires customer relationships and gains the opportunity to 'cross-sell' other products. Moreover, when the whole credit-card operation is sold (e.g., MNC' s spinoff of MBNA as an IPO, see Panel B of Table 6), other intangible assets such as reputation, quality of management, and market presence are conveyed. The 'goodwill' hidden in creditcard receivables represents the capitalized value of the accounts neglected by GAAP. Kane (1993) partitions hidden value into three time-related components:

1. the value of 'identifiable' intangible assets such as corporate reputation, staff, business locations, and a customer base whose value is built up from wise expenditures made in the past; 2. the capitalized value of ongoing net regulatory subsidies or burdens that flow from existing and projected laws and supervisory practices; and 3. the present value of growth opportunities (PVGO), which varies according to strategic adaptions and innovations that the corporation might make in the future.

Regarding the sale of credit-card receivables, the acquisition of a customer base (past investment) and the PVGO associated with those customers (future strategic opportunity) capture hidden assets critical to the analysis of the market for credit cards. In 1993, the U.S. Supreme Court ruled that intangible assets such as customer lists can be depreciated for tax purposes if the companies can determine the dollar value and useful life of the assets (Barrett, 1993). In addition, shortly before the Supreme Court's decision, bank regulators ruled that intangible assets such as purchased credit-card relationships and purchased mortgage-servicing rights are permitted to be counted as part of Tier-I capital in meeting risk-based capital requirements (Federal Register, 1993). These legal and regulatory rulings confirm our economic analyses of the importance of hidden assets in valuing a portfolio of credit-card receivables. By considering the role of hidden assets in the analysis of resale premiums, we attempt to proxy a portfolio of credit-card receivables priced in an efficient market.

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2.2. The costs o f acquiring new card accounts 6

Banks have four methods of expanding their number of cardholders: (1) solicitation through direct mail or tele-marketing, (2) branch banking, (3) agentbank programs in which a major player sponsors another bank's card (the other bank gets its name on the front of the card while the major player's name is, in small print, on the back of the card), and (4) buying accounts. Once a new account is acquired, the bank always has the opportunity to cross-sell to the new cardholder. Although purchasing existing accounts is the growth method highlighted in this research, the expense of developing new accounts via the other methods cannot be ignored since it represents a relevant opportunity cost. Hammer (1996) reports that for 1995 the weighted average cost of acquiring new card accounts by the first three methods was about $75 per account, with a range between $20 and $200 per account. Given these cost considerations, it is not unreasonable for a buyer to pay up to $200 dollar for a 'premium account' and still be able to breakeven. 7 The reported gross premiums paid for credit-card receivables are not net of this opportunity cost. The net cost or premium would reflect the value to the buyer of the opportunity to cross-sell (which one industry source says is a factor in pricing the deals, but difficult to estimate), and any other relevant intangible assets. 2.3. The effects o f portfolio composition on resale premia: National versus regional portfolios

Card issuers build large, national portfolios through 'heavy solicitations' from other institutions or mass marketing on a national level or both. In contrast, mid-sized and smaller portfolios typically consist of 'home-grown' or branch-generated accounts. Because of the local nature of banking, cross-selling opportunities should be greater for a portfolio consisting of regional accounts. Having acquired cardholders concentrated in the same region, the purchasing bank might be able to take advantage of its market presence and name recognition to more effectively sell other products. For example, banks located in the same market as the acquired cardholders would be more likely to convince these cardholders to open checking or savings accounts. On balance, because of richer cross-selling potential, acquiring banks should pay higher premia for mid-sized, regional card portfolios than for larger, national portfolios.

6 Hammer (1996) provided the cost estimates presented in this section. He is the Chairman and Chief Executive Officer of R.K. Hammer Investment Bankers, Thousand Oaks, CA. 7 The term 'premium' refers to upscale, gold, or platinum accounts and should not be confused with the premium on the scale of card receivables.

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2.4. The effects o f securitization on the resale market

Should a bank sell or securitize its card portfolio? Although either transaction removes the assets from the balance sheet, the cash flows differ. The sale, all other things being equal, generates a larger cash inflow because of the premium paid for the face value of the receivables; the securitization generates only the face value of the debt with the obligation to pass-through the contractual cash flows of interest and principal. A bank looking for a bigger boost to its capital adequacy would favor a sale over a securitization; a bank with adequate capital might prefer to securitize its receivables. The improvement in the banking industry's capital adequacy from the 1980s to the 1990s suggests that we might observe a decline in sales and an increase in securitizations over this period.

3. Sample banks, data, and methods We define credit-card banks (CCBs) as banks with 75 percent or more of their assets in credit cards or related plans. The number of CCBs in each year beginning with 1984 through 1993 are 13, 14, 26, 34, 33, 34, 37, 40, 36, and 34. Banks with credit-card assets less than 75 percent and greater than 25 percent are excluded from our analysis. For the years 1984 to 1993, the number of banks in this middle group ranged from seven to 32, smaller numbers than those for the number of CCBs. Banks with credit-card assets less than 25 percent of their total assets, which number in the thousands, are used as one of our control groups. Similar to Eisenbeis and Kwast (1991), we excluded de facto failed banks from any of our samples. 8 In addition, we eliminated all banks with negative total equity and all banks with missing or obviously inaccurate data (e.g., total assets or total liabilities less than zero). Finally, to allow for start-up experience, any bank not in existence for at least six months was excluded from the analysis. 3.1. Data and control banks

All data are from Reports of Condition and Reports of Income and Dividends computer tapes as processed by the National Technical Information Service (1984-1993), Springfield, VA. These 'call-report data' are supplied by banks to their federal bank regulators. We compare the risk and profitability of CCBs with two alternative control groups: (1) a matched sample of non-CCBs of similar size (i.e., within plus or

s De facto failures are defined as banks with negative ROAs(in terms of absolute values) that were more than twice their ratios of equity capital to total assets.

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minus 5% of the CCB's total assets), similar age (date of establishment within three years), and affiliation with a holding company or not and (2) all insured commercial banks with credit cards and related plans less than 25 percent of total assets. The number of banks in the second control group ranged from 15,451 in 1984 to 11,262 in 1993; the number of banks in the first group ranged from 22 to 203.

3.2. Measures of bank risk To analyze the riskiness of credit-card banks, we apply the risk measure developed and used by Hannan and Hanweck (1988). 9 The empirical form of their risk index, which we label Z, is

Z = [ ROA + C A P ] / s ,

(1)

where ROA = pretax return on assets (earnings before taxes and securities gains/losses divided by average assets), CAP = equity capital-to-asset ratio, and s = the standard deviation of ROA. lo From an accounting perspective, Eq. (1) is appealing because it includes ROA (the most widely accepted accounting measure of overall bank performance), the variability of ROA (a standard measure of risk in financial economics), and book capital adequacy (an industry standard for bank 'safety and soundness'). The resultant Z statistic is a measure, expressed in units of standard deviations of ROA, of how much a bank's accounting earnings can decline until it has a negative book value. Intuitively, Z gauges the thickness of the book-value 'cushion' a bank has available to absorb accounting losses. Therefore, a lower Z implies a riskier bank; a higher Z implies a safer bank. Since we compute the standard deviations of ROA in two different ways, we have two measures of Z: (1) computing the standard deviations of ROA on a bank-by-bank basis over time, we get a Z based on a time-series approach and (2) computing standard deviations of ROA across our sample of CCBs and control banks, we get a Z based on a cross-sectional approach. The first method assumes that the risks associated with credit-card lending are reflected in the standard deviation of ROA over time for a given institution. We use a min/max of five years of data to compute each bank's standard deviation of ROA. Given our

9 The measure also has been used by Liang and Savage (1990), Eisenbeis and Kwast (1991), McAllister and McManus (1992), Berger and Udell (1993), and Sinkey and Nash (1993). Hannan and Hanweck originally developed their risk measure as a probability of book-value insolvency. Although Hannan and Hanweck and Liang and Savage label the risk ratio ' g ' , we follow others and label it 'Z'. l0 Using the symbols from Eq. (1), Hannan and Hanweck (1988, pp. 204-205) derive the probability of book-value insolvency, p, as p < 0.5s2/[E(ROA)+ CAP] 2. We emphasize that this p expresses the probability of book-value insolvency which, in many instances, significantly differs from market-based measures of solvency.

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sample period of 1984 to 1993, the time-series approach limits our empirical analysis to 1989 through 1993. It also explains why the sample sizes differ between our cross-sectional and time-series tests. The second method, our cross-sectional approach, assumes that the risks associated with such lending are reflected in the standard deviation of R O A across the sample of CCBs in a given year. A potential weakness of the cross-sectional method is that the standard deviation of R O A across CCBs may reflect product differentiation. For example, CCBs with the highest R O A s may be serving submarkets consisting of customers with comparatively high s e a r c h / s w i t c h costs or with affinity relations in a particular region while CCBs with lower R O A s may be newer CCBs with smaller market shares. ~1 W e interpret the Zs based on the cross-sectional method, as Eisenbeis and Kwast (1991) do, as measures of the average or 'typical' C C B ' s riskiness. The cross-sectional approach permits analysis of each of the years from 1984 to 1993.

4. Empirical findings Table 1 shows means, standard deviations, and medians of the risk indices (Zs) and of the components of the Zs (i.e., ROA, CAP, and s) for the years 1989 through 1993 (Panels A through E) for both CCBs and the matched sample of control banks (non-CCBs). These figures are calculated with a m i n / m a x of five years of year-end R O A for each bank. The major drawback of this time-series approach to calculating the standard deviations is the reduction in the number of observations. Nevertheless, by comparing the means and the medians within and across groups, our results consistently show that CCBs were riskier than non-CCBs for the years 1989 to 1993. Focusing on the components of the risk index ( Z = [ROA + C A P ] / s ) , we find significant differences. Specifically, ROA and CAP are significantly higher, indicating greater profitability and greater safety. But the standard deviation of ROA, s, is also significantly higher, indicating greater risk. When combined in the risk index, Z, the individual differences disappear as the Zs are not significantly different (Table 1). 12 The notes to Table 1 show the probabilities of book-value insolvency { p = 1 / ( 2 [ Z 2])} corresponding to each Z. The components of Z reveal

11 The issue is that differing degrees of market power (across finns or over time) can lead to a variance of ROAs within a range that is greater than or equal to the normal competitive return. 12The components of our risk measure were calculated using three alternative weighting schemes: (1) simple averages, (2) averages weighted by total assets (to capture potential effects due to differences in size) and (3) averages weighted by total credit-card loans and related plans (to capture potential effects due to differences in market share). Since the results did not vary substantially across the three weighting schemes, we present Zs based on the simple averages.

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Table 1 Means, standard deviations, and medians of the risk indices (Zs) and ROA, CAP, and s components for CCBs and non-CCBs, 1989-1993 ( Z = [ ROA + A ]/ s ) Control banks (Non-CCBs)

Credit-card banks (CCBs) ROA

CAP

s

Z

ROA

CAP

s

Z

0.0105 0.0128 0.0121

0.0719 0.0279 0.0684

0.0076 0.0068 0.005l

18.9 13.1 13.9

Panel B. 1990 (16 CCBs and 87 Matched Non-CCBs) b Mean 0.0500 0.1336 0.0256 22.4 0.0113 S. dev. 0.0185 0.1178 0.0394 23.4 0.0150 Median 0.0443 0.0941 0.0108 13.8 0.0128

0.0715 0.0188 0.0708

0.0076 0.0069 0.0057

19.9 17.8 15.9

Panel C. 1991 (11 CCBs and 112 Matched Non-CCBs) c Mean 0.0502 0.1230 0.0270 11.7 0.0091 S. dev. 0.0524 0.0745 0.0414 6.9 0.0115 Median 0.0443 0.1098 0.0158 12.7 0.0118

0.0762 0.0209 0.0720

0.0074 0.0061 0.0057

23.8 30.3 15.1

Panel D. 1992 (15 CUBs and 94 Matched Non-CCBs) ~ Mean 0.0572 0.2065 0.0195 19.3 0.0124 S. dev. 0.0413 0.2110 0.0160 12.9 0.0134 Median 0.0527 0.1354 0.0150 14.2 0.0146

0.0771 0.0188 0.0756

0.0053 0.0037 0.0043

26.1 18.8 20.9

Panel E. 1993 (16 CCBs and 73 Matched Non-CCBs) ~ Mean 0.0783 0.1506 0.0198 17.2 0.0097 S. dev. 0.0461 0.1180 0.0160 12.2 0.0155 Median 0.0686 0.1082 0.0140 13.7 0.0134

0.0862 0.0413 0.0791

0.0069 0.0078 0.0041

27.3 20.9 23.2

Panel A. 1989 (11 CCBs and 34 Matched Non-CCBs) a Mean S. dev. Median

0.0342 0.0136 0.0342

0.0822 0.0314 0.0736

0.0134 0.0062 0.0108

12.3 9.7 8.7

a The mean ROA are significantly different at the one-percent level between the two groups while the s are significantly different at the five-percent level. Using mean Zs, average probabilities of insolvency, p = 1/(2[Z2]), are 0.33% for the CCB group and 0.14% for the non-CCB group. b The mean ROA and C4P are significantly different at the one-percent level while the mean s are significantly different at the five-percent level. Using mean Zs, average probabilities of insolvency, p = 1/(2[Z2]), are 0.10% for the CCB group and 0,13% for the non-CCB group. c The mean ROA and s are significantly different at the one-percent level between the two groups while the mean CAP are significantly different at the five-percent level. Using mean Zs, average probabilities of insolvency, p = 1/(2[Z2]), are 0.37% for the CCB group and 0.09% for the non-CCB group. d The mean ROA, s, and CAP are significantly different at the one-percent level between the two groups. Using mean Zs, average probabilities of insolvency, p = 1/(2[Z2]), are 0.13% for the CCB group and 0.07% for the non-CCB group. The mean ROA, s, and CAP are significantly different at the one-percent level between the two groups. Using mean Zs, average probabilities of insolvency, p = 1/(2[Z2]), are 0.17% for the CCB group and 0.07% for the non-CCB group. Source: Figures calculated from Reports of Condition and Reports of Income and Dividends computer tapes, National Technical Information Service (1984-1993).

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the sources of this greater riskiness. Using averages of the medians for 1989 to 1993 provides the following Z profiles: ROA

Average CCB Average Non-CCB

0.0403 0.0130

CAP 0.1065 0.0732

s 0.0141 0.0050

Z

p

10.4 17.2

0.0046 0.0017

While the average CCB generates significantly more pre-tax income per dollar of assets and has more equity capital per dollar of assets, the variability of its R O A results in substantially higher risk measures than the average non-CCB. Based on the profiles above, the Zs are 10.4 = [0.0403 + 0.1065]/0.0141 for the average CCB and 17.2 = [0.0130 + 0.0732]/0.0050 for the average non-CCB; they map into probabilities of insolvency of 0.46 percent and 0.17 percent, respectively. The standard deviations of ROA(s) shown in Table 1 are significantly different for each year. These results indicate that our sample of CCBs had greater variability of R O A than our matched sample of control banks of similar size, age, and holding-company status. 13 The R O A s and capital ratios reveal similar statistical differences. An important assumption of the Hannan and Hanweck (1988) approach is that the probability distribution of R O A is symmetric. To check for violations of this assumption, we plotted histograms of the ROAs. These results in conjunction with the data in Table 1 indicate symmetrical distributions. In contrast, the capital ratio has an occasional outlier that creates a bias in favor of a higher Z for the average CCB. On balance, our results do not appear to be driven by distributional problems with the data. The probabilities are simply mirror-image risk measures of the Zs.

4.1. Cross-sectional risk-return profiles: Large-sample results

Table 2 reports risk indices based R O A for CCBs and the much larger credit-card assets less than 25 percent qualitatively similar to those in Table the cross-sectional standard deviations

on cross-sectional standard deviations of control group of all ' v i a b l e ' banks with of total assets. Although these results are 1 (i.e., CCBs are riskier), the Zs based on of R O A are substantially smaller, but not

13 Calem (1992) and Ausubel (1991) use historical delinquency data to measure the riskiness of credit-card banks. Although delinquencies increased from 3.4% at the start of 1990 to almost 4.5% at the start of 1991, they argue that this measure of the risk of credit-card receivables cannot explain the abnormal returns of CCBs. We contend that the standard deviation of ROA provides a more comprehensive measure of bank risk.

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Table 2 Risk indices (Z) and probabilities of insolvency (p) based on cross-sectional standard deviations of ROA for CCBs and Non-CCBs, 1984-1993 ~ Credit-card banks

Control group

Year

Z

p

N

Z

p

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

5.96 3.05 3.17 3.59 5.90 5.44 3.68 3.96 2.31 2.97

1.4% 5.4 5.0 3.9 1.4 1.7 3.7 3.2 9.3 5.7

13 14 26 34 33 34 37 40 36 34

5.45 5.08 4.15 3.98 4.98 6.55 5.15 5.30 6.82 4.30

1.7% 1.9 2.9 3.1 2.0 1.2 1.9 1.8 1.1 2.7

Ave.

4.00

4.1%

5.18

2.0%

a The number of banks in the control group ranged from 15,451 in 1984 to 11,262 in 1993. Source." Figures calculated from Reports of Condition and Reports of Income and Dividends computer tapes, National Technical Information Service (1984-1993).

u n e x p e c t e d l y so. T h e s e risk i n d i c e s are s i m i l a r in m a g n i t u d e to t h o s e r e p o r t e d b y E i s e n b e i s a n d K w a s t ( 1 9 9 1 ) w h o u s e d t h e c r o s s - s e c t i o n a l m e t h o d . 14 T h e a v e r a g e c o m p o n e n t s o f t h e Z risk m e a s u r e s for the b a n k s s h o w n in T a b l e 2 are: Bank group

ROA

s.d. o f R O A

C a p i t a l ratio

CCBs Non-CCBs

0.0406 0.0105

0.0458 0.0203

0.1183 0.0954

T h e s e d a t a a g a i n s h o w t h a t C C B s are m o r e p r o f i t a b l e b u t m o r e risky t h a n the c o n t r o l b a n k s . T o partially o f f s e t t h e risk f r o m h i g h e r v a r i a b i l i t y o f t h e i r e a r n i n g s , C C B s h o l d a l a r g e r a m o u n t o f e q u i t y capital. T h e risk i n d i c e s c o r r e s p o n d i n g to t h e s e a v e r a g e data are 3.5 ( C C B s ) a n d 5.2 ( n o n - C C B s ) c o m p a r e d to 4.0 ( C C B s ) a n d 5.2 ( n o n - C C B s ) as s h o w n in T a b l e 2.

14 Remember that the Z-score approach assumes that ROA is normally distributed. Thus, an important caveat and distinction between our Zs and those found by Eisenbeis and Kwast (1991) is that the comparatively high variance of ROA for real-estate banks (REBs) was in large part due to a comparatively 'thick tail' with respect to low returns. Thus, their Z-scores may understate the comparative riskiness of REBs while our estimates may overstate the comparative riskiness of CCBs.

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101

4.2. Cross-sectional risk-return profiles: Matched-sample results Table 3 presents risk-return profiles for our sample of CCBs and the matched sample of control banks. Return on assets and the standard deviation of ROA, both computed cross-sectionally, for the years 1984 to 1993 are shown. On average, for the period, CCBs had a 331 basis-point ROA advantage over the non-CCBs. The average ROAs were 4.06 percent (CCBs) versus 0.75 percent (non-CCBs). For each year the ROAs between the two groups were significantly different at the five-percent level or better. Since the non-CCBs are matched on the basis of size, age, and holding-company status, these three factors do not account for the differences in means. The cross-sectional standard deviations of ROA shown in Table 3 reveal that CCBs had a higher variability of ROA than non-CCBs. This larger average standard deviation of ROA provides evidence of the greater riskiness of CCBs. Overall, the data in Table 3 suggest that the higher profitability of credit-card banks might come at the expense of greater risk. 15

4.3. Adjustment for loans sold Our profit rate, ROA, is measured by the ratio of pre-tax accounting profits divided by average total assets; a flow variable divided by a stock variable. If a bank sells assets during the year, it will affect ROA. To test for potential bias arising from loan sales, we calculated an alternative measure of ROA and used it to estimate our Z and p measures of risk. Our ROA adjusted for loan sales use the same numerator (pre-tax net income) but adjusts the denominator as follows:

A A ( A D J L S ) = ( Beginning assets + Ending assets + Loans s o l d ) / 2 . This adjustment does two things: (1) it makes the stock variable more compatible with the flow variable in the numerator and (2) it treats loans sold as an 'add-back', which would be most appropriate if loans were sold near the end of the year but less appropriate if loans were sold near the beginning of the year. This adjustment changed ROA and its standard deviation only slightly or not at all and

15This high cross-sectional variability of returns could arise if some CCBs earned normal returns while others earned supranormal returns. We measure how many CCBs report normal versus supranormal ROAs. We define 'normal' returns in 3 ways: (1) mean ROA for the matched control group, (2) median ROA for the matched control group, and (3) the industry standard of 1% ROA. We find that, on average for 1984-1993, approximately 85% of the CCBs exceeded the mean normal return. Similarly, 84% of CCBs exceeded the median control bank return while 84% also reported returns greater than 1%. Since a vast majority of CCBs earn 'supranormal' returns, it appears that the cross-sectional variability of ROA primarily results from deviations in performance among CCBs earning 'supranormal' returns. We thank a referee for bringing this issue to our attention.

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Table 3 Cross-sectional risk-return profiles for CCBs and non-CCBs as measured by ROA and the standard deviation of ROA a Credit-card banks

Matched control banks

ROA

Year

ROA

s

N

ROA

s

N

spread

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

0.0348 0.0308 0.0273 0.0324 0.0309 0.0344 0.0334 0.0449 0.0688 0.0684

0.0204 0.0369 0.0341 0.0353 0.0196 0.0261 0.0515 0.0463 0.1140 0.0743

13 14 26 34 33 34 37 40 36 34

0.0079 0.0046 0.0030 0.0055 0.0126 0.0081 0.0068 0.0063 0.0105 0.0094

0.0161 0.0186 0.0171 0.0203 0.0143 0.0161 0.0259 0.0206 0.0151 0.0236

29 22 66 93 55 81 161 97 203 124

269 bp 263 245 269 183 262 266 385 582 590

Averages Memo:

0.0406

0.0458

0.0075

0.0188

s/ROA = 0.8865

331 bp

s/ROA = 0.3989

a The ROA spread is expressed in basis points (bp). The group means for each year are significantly different at the 1% level or better, except for 1985 where the difference is significant at the 5% level. The control banks are matched on the basis of size (all banks within 5% of the total assets of the CCB), age (date of establishment within three years), and affiliation with a holding company or not. Source: Figures calculated from Reports of Condition and Reports of Income and Dividends computer tapes, National Technical Information Service (1984-1993).

c o n s e q u e n t l y the results in T a b l e 3 w e r e n o t affected. ~6 T h e m a i n r e a s o n the results are u n a f f e c t e d is that loans sold w e r e r e l a t i v e l y s m a l l in c o m p a r i s o n to total assets. F r o m 1984 to 1993, l o a n s sold a v e r a g e d o n l y 6.6 p e r c e n t o f total C C B y e a r - e n d assets. O v e r the s a m e period, l o a n s sold b y c o n t r o l b a n k s a v e r a g e d 1.3 p e r c e n t o f y e a r - e n d assets. T o s u m m a r i z e , the results in T a b l e s 1 - 3 s h o w that C C B s e a r n h i g h e r r e t u r n s b u t t h a t t h e y are riskier t h a n n o n - C C B s . T h u s , a n a l t e r n a t i v e a n d m o r e p l a u s i b l e e x p l a n a t i o n for t h e h i g h pre-tax R O A s e x h i b i t e d b y c r e d i t - c a r d b a n k s is as c o m p e n s a t i o n for the g r e a t e r risk o f s p e c i a l i z i n g in u n s e c u r e d c r e d i t - c a r d debt.

4.4. Regression results T a b l e 4 p r e s e n t s O L S e s t i m a t e s o f the a s s o c i a t i o n b e t w e e n p r o f i t a b i l i t y or risk a n d p o r t f o l i o c o n c e n t r a t i o n in c r e d i t - c a r d assets. W e r u n the f o l l o w i n g s i m p l e model:

Yi = bo + b,( CCTA ) + b2( L N T A ) + b3( SMA ) + e, where

Y/= either ROA

or s t a n d a r d d e v i a t i o n o f R O A ,

~6 These results are available from the authors on request.

CCTA = the ratio o f

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Table 4 Ordinary least squares estimates of the relationship between profitability or risk and credit-card activity, 1989 1993 a

Yi = bo + b , ( C C / T A ) + bz(LNTA ) + b3(SMA ) + e Independent variables

1989

1990

1991

1992

1993

Panel A. The relationship between ROA and the ratio of credit-card loans to total assets (dependent variable = ROA)

CC loans/Total assets Natural log of total assets SMA dummy ( = 1 if in SMA) Intercept R-square Number of observations

0.0269 * (0.0060) - 0.0011 (0.0020) -0.0095 (0.0055) 0.0318 (0.0239) 43.6% 45

0.0346 * 0,0425 * 0.0490 * 0.0613 * ( 0 . 0 0 6 4 ) (0.0082) ( 0 . 0 0 7 0 ) (0.0092) 0.0027 0.0013 0.0014 0.0068 * ( 0 . 0 0 1 8 ) (0,0016) ( 0 . 0 0 1 9 ) (0.0026) -0.0069 -0.0068 -0.0058 -0.0060 (0.0052) (0.0045) (0.0044) (0.0070) - 0.0148 - 0,0007 0.0008 - 0.0648 (0.0213) ( 0 , 0 1 8 6 ) (0.0210) (0.0302) * 45.0% 103

29.6% 123

39.5% 109

55.3% 89

Panel B. The relationship between the standard deviation of ROA and the ratio of credit-card loans to total assets (dependent variable = standard deviation of ROA)

CC loans/Total assets Natural log of total assets SMA dummy ( - 1 if in SMA) Intercept

R-square Number of observations

0.0069 * (0.0033) - 0.0004 (0.0011) 0.0004 (0.0030) 0.0126 (0.0123) 12.3% 45

0.0205 * 0.0190 * 0.0169 * 0.0146 * (0.0069) ( 0 . 0 0 5 9 ) (0.0023) (0.0036) 0.0003 0.0010 - 0.0007 - 0.0003 (0.0019) ( 0 . 0 0 1 2 ) (0.0006) (0.0010) 0.0078 0.0035 0.0042 * 0.0062 * ( 0 . 0 0 5 6 ) ( 0 . 0 0 3 3 ) ( 0 . 0 0 1 5 ) (0.0028) 0.0057 - 0.0071 0.0098 0.0057 ( 0 . 0 2 2 9 ) (0.0134) ( 0 . 0 7 0 2 ) (0.012l) 14.5% 103

15.2% 123

40.2% 109

24.7% 89

a Figures in parentheses are standard errors. An asterisk indicates that the variable is statistically significant at the five percent level or better.

c r e d i t - c a r d assets to total assets, L N T A = natural log o f total assets, S M A is a d u m m y variable equal to one if the b a n k is l o c a t e d in a s t a n d a r d m e t r o p o l i t a n area or S M A , and e is a r a n d o m - d i s t u r b a n c e t e r m w i t h the usual properties. T h e focal variable is

CCTA

while

LNTA

and

SMA

are f u r t h e r c o n t r o l s for size and

g e o g r a p h i c market. Recall that e a c h C C B w a s m a t c h e d b y size, age, and h o l d i n g c o m p a n y status w i t h a control bank. T h e a s s o c i a t i o n b e t w e e n C C T A a n d e i t h e r R O A or s is e x p e c t e d to be positive. T h e r e g r e s s i o n results s h o w n in T a b l e 4 c o n f i r m our e x p e c t a t i o n s . F o r e v e r y year, a s t r o n g l y significant, p o s i t i v e relation exists b e t w e e n R O A or its variability and a b a n k ' s c o n c e n t r a t i o n in c r e d i t - c a r d assets. T h e s e f i n d i n g s i n d i c a t e that b a n k s w h i c h i n v e s t h e a v i l y in c r e d i t - c a r d r e c e i v a b l e s are m o r e p r o f i t a b l e and m o r e risky than less s p e c i a l i z e d banks. E x c e p t for L N T A in 1993 for the R O A e q u a t i o n

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(Panel A) and SMA in 1992 and 1993 for the standard-deviati0n equation (Panel B), none of the control variables are statistically significant. Overall, the model (i.e., CCTA) explains 30 to 55 percent of the variation in ROA over the years 1989 to 1993 and 12 to 40 percent of the variation in the standard deviation of ROA. On balance, a strong and statistically significant relation exists between ROA or its variability and a bank's concentration in credit-card assets. 4.5. The trend in card-industry profitability

Using data supplied by Hammer (1996), this section briefly reports on the profitability of the card industry by comparing pre-tax profits and its components between 1983 and 1995. The following data highlight the risk-return tradeoffs and suggests a reduced stickiness of the major component of total income, interest income: 17 Component Total income Operating expense Charge-offs Cost of funds Net pre-tax

1983 24.4% 7.2 1.9 9.9 5.4%

1995 18.0% 4.2 4.1 6.1 3.6%

Change (basis points) - 6.4 - 3.0 + 2.2 - 3.8 - 1.8

The favorable trends include reduced operating expenses and lower funding costs but these were more than offset by reduced total income and higher loan losses. Despite this overall unfavorable trend, the accounting profitability of credit-card banks still remains high compared to nonspecialized bank lenders.

5. Analysis of market premiums Ausubel (1991) argues that credit-card resale premiums represent the capitalized future stream of positive economic profits from the accounts purchased and interprets these premiums as evidence of the ex ante profitability of the credit-card market. We do not challenge his argument; instead, we expand it by suggesting that part of the expected profits stemming from the accounts purchased may be unrelated to the credit-card function per se and may result from the PVGO of cross-selling other bank products. Specifically, when purchasing credit-card accounts, acquiring banks receive hidden assets (the potential for new customer relationships). This intangible opportunity to cross-sell other products has a value

~7Interest income tends to average about 75 percent of total income for CCBs. Also see footnote2, which documentsthe decline in card rates during the 1990s.

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Table 5 Information on the resale of credit-card accounts Panel A. A ten-year history of credit-card portfolio sales of national-brand deals only (MC-VISA) excluding private-label and retail cards, 1986-1995

1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 Average or total

Range of estimated gross premiums

Average weighted gross premiums .,d

# Transactions b

Assets sold c ($ billions)

5.0% -27.5 % 7.0% -21.0% 7.1%-23.0% 16.8%-25.0% 7.0%-24.0% 7.0% -27.0% 3.0%-25.0% 7.8%-25.5% 10.5%-16.0% 14.3%-29.0%

20.7% 18.0% 18.1% 16.8% 14.0% l 8.7 % 18.9% 20.3% 20.3% 20.4%

19 18 14 17 32 26 25 15 6 3

$0.91 $0.75 $0.82 $0.88 $3.80 $5.40 $6.60 $1.60 $0.86 $1.60

8.6%-24.3%

18.6%

175

$23.22

Panel B. Average-weighted premiums for the sale of credit-card receivables by card portfolio asset size, 51 transactions for the years 1993 to 1995

Asset-size category ($ millions) Gross premium

< 15

15-100

High price Weighted average premium Low price

12.0% 10.2

27.0% 18.1

> 100 20.0% 15.0

5.0

15.0

13.0

a 'Gross" premiums, prior to discounting for delinquent accounts b Does not include numerous smaller deals, typically less than $15 million c Does not include 18 'mergers of equals' or total organization purchases in 1995. d Summary of Ausubel's data (27 obs. for the period April 1984 to April 1990): Mean reported premium of 19.8 percent (median 21% and standard deviation 5.2%) on average outstanding balances of $374 million (median $226 and s.d. $327) Sources: Panel A - Hammer (1996) and Ausubel (1991, calculated from his Tbl. 9, p. 66). Panel B Hammer (1996).

c o n c e p t u a l l y d i s t i n c t f r o m the c r e d i t - c a r d r e c e i v a b l e s . T h e r e f o r e , w e p r o p o s e a n a l t e r n a t i v e e x p l a n a t i o n t h a t the r e s a l e p r e m i u m s m a y i n c l u d e a p a y m e n t for intangible opportunities associated with customer relationships. P a n e l A o f T a b l e 5 s h o w s the gross p r e m i u m s p a i d o n the r e s a l e o f c r e d i t - c a r d a c c o u n t s for the y e a r s 1986 to 1995. T h e r e p o r t e d t r a n s a c t i o n s are for u n s e c u r e d n a t i o n a l b r a n d p o r t f o l i o s o n l y a n d e x c l u d e p r i v a t e label, retail, or s e c u r e d a c c o u n t s c o l l a t e r a l i z e d b y c o n s u m e r deposits. T h e u n d e r l y i n g assets are d e s c r i b e d as ' g o o d , o p e n - t o - b u y a c c o u n t s ' p r i o r to a n y d e d u c t i o n or d i s c o u n t i n g for d e l i n q u e n t accounts. T h e f i g u r e s d o not i n c l u d e n u m e r o u s s m a l l e r t r a n s a c t i o n s , t y p i c a l l y less

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than $15 million, which are often unknown to anyone other than the parties in the transactions. The reported premia in Panel A provide a number of insights. First, 83 of the deals (47%) occurred during the three years 1989 to 1991 and accounted for $15.8 billion or 68 percent of the assets sold. Since then, both the volume and number of deals have declined. The average size deal during the years 1989 to 1991 was $190 million compared to an average size of $80 million in the other seven years (92 transactions worth $7.4 billion for the years 1986-1988 and 1992-1995). The average premium paid on the 83 deals in the three years 1989 to 1991 was 17.2 percent compared to an average premium of 19.2 percent for 92 transactions in the other seven years (1986-1988 and 1992-1995). Panel B of Table 5 shows gross premiums by size of transaction for the 51 deals over the years 1993 to 1995. These transactions were worth $2.48 billion or about $49 million per deal, down substantially from the average for the previous seven years of $167 million. The premiums reveal that mid-sized transactions ($15 million to $100 million) have generated the highest premiums, 18.1 percent on average, compared to average premiums of 15 percent for large deals over $100 million and 10.2 percent for small deals under $15 million. The lower premia on larger portfolios may reflect lower opportunities to cross-sell other products because the larger deals represent national portfolios that are geographically dispersed. Therefore, because of richer cross-selling potential, mid-sized, regional portfolios may command higher premiums than larger, national portfolios. Ausubel's data, which cover the period April 1984 to April 1990, represent a subset of the transactions reported in Panel A of Table 5. With 27 observations, he reports an average premium of 19.8 percent with a high of 27 percent, a low of 11 percent, and a standard deviation of 5.2 percent (see notes to Panel A of Table 5). Using the 75 transactions from 1986 to 1990, which were valued at $16.1 billion or $214 million per deal, the average premium of 19.7 calculated from Table 5 shows that Ausubel's smaller sample of 27 deals is representative. As noted above, however, the market has changed substantially since the end of his test period (April 1991). One reason for the decline in both the size of the average deal and in the number of deals has been the surge in the securitization of credit-card receivables. When large portfolios are not commanding high premiums in the resale market, securitization offers an attractive alternative to selling receivables. In addition, by securitizing assets rather than selling them, customer relationships are maintained, along with opportunities to cross-sell. One of the major securitizers of credit-card receivables is MBNA. From 1991 to 1995, MBNA issued $16.7 billion in securities backed by credit-card receivables. 18 The 22 issues had an average size

is We thank Jeff Unkle of MBNA for providing the data described in this section.

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of $759 million with an issue size ranging from $500 million to $1 billion. Seven of the issues or card trusts carried fixed rates of interest while the other 15 were floating-rate contracts. Expected maturities ranged from five-to-ten years. Because of prepayment risk, the exact maturities of the securities are not known. The following data are representative of the pre-tax profitability of MBNA' s securitizations as of December 1995:19 Card Trust Deal Size Securities Expected maturity

MBNA 1994-B $1 billion Floating rate 15 September 1999

Excess-spread analysis Cash yield Blended coupon Servicing fees Charge-offs Excess spread

17.13% 5.70 2.50 3.51 5.42%

In the declining-rate environment of most of the first half of the 1990s, floating-rate securitizations have been very profitable for MBNA. This phenomena along with the reduced premia for large deals, along with the difficulty of finding a buyer for large deals, helps explain the shrinking market for large transactions of credit-card receivables. Table 6 shows credit-card IPOs for the period March 1990 to July 1995. Unfortunately, only one of these IPOs - MNC Financial's offering - has a reported premium. Moreover, Stewart (1991, see notes to Table 6) regards MNC's reported premium of 14 percent as overstated by 100 percent. Since MNC's IPO of MBNA was made under distressed conditions, a seven percent premium may not be unusual. Nevertheless, as analyzed below, MBNA's subsequent performance suggests substantial PVGO arising from the hidden assets embodied in its IPO. Although the IPO data in Table 6 lack reported premiums (except for MNC), other aspects of the transactions and the market's subsequent reaction to the offerings provide interesting insights concerning the riskiness of credit-card assets. Consider MBNA's stock market performance after the IPO. (MBNA, which is traded on the NYSE, is the name of MNC's IPO spinoff.) For the first 14 months of MBNA's trading, Sullivan (1992) reports an equity beta of 1.6, which means

J9 MBNA also has an international securitization of 207.5 million pounds outstanding. Issued in 1995, it carries a floating rate with an expected maturity of 15 August2000. The three-monthaverage excess spread on the issue for the months of October-to-Decemberof 1995 was 8.6%.

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Table 6 Initial public offerings (IPOs) of credit cards Issuer (exchange, date)

Shares issued

Issue

Ratios ( 2 / 1 / 9 6 )

Mils.

% Public

price

Mkt to issue

Price to EPS

VeriFone (NYSE, 3/90) MBNA Corp. (NYSE, 1/91) Envoy Corp. (OTC, 5/91) Sears Payment (NYSE, 2/92) First Data (NYSE, 4/92) First USA (NYSE, 5/92) Capital One (NYSE, 11/94)

2.5 49.5 2.0 3.0 50.0 4.5 7.1

12 100 20 25 46 89 94

$16 22.5 a 10 16 22 9.5 16

2.38 1.78 2.07 1.91 3.30 5.34 1.67

28 17 nm b 19 nm b 16 14

Averages

16.9

55

2.64

19

16

a Stewart (1991) reported in Credit Card Management that "When underwriting fees and other obligations were subtracted MBNA was left with a disappointing 7% premium" (p. 54). However, since MNC's sale of MBNA was made under distressed conditions, its premium may be atypical. b Not meaningful (nm) because of low or negative EPS. Verifone went public on the OTC and was listed on the NYSE during 1995. Sears Payment is listed on the NYSE as SPS Transaction Service (PAY). On February 28, 1995, Capital One was spun-off by Signet Banking Corp. to its shareholders as a dividend. Sources: Information on IPOs is from Stewart (1992, p. 29). The ratios of market price to issue price and the P / E ratios are based on the closing prices as of February 1, 1996 as reported in The Wall Street Journal (2-2-96).

that, o n a v e r a g e , M B N A ' s s t o c k r e t u r n m o v e s 1.6 p e r c e n t for a 1.0 p e r c e n t c h a n g e in the m a r k e t p o r t f o l i o (as c a p t u r e d b y a m a r k e t index). 2o O v e r this s a m e period, the N Y S E ' s ' F i n a n c i a l I n d e x ' (a c o m p o s i t e o f the stocks o f f i n a n c i a l - s e r v i c e s f i r m s ) h a d a b e t a o f 1.18. B e t a s for ( n o n d i s t r e s s e d ) b a n k h o l d i n g c o m p a n i e s w i t h a c t i v e l y t r a d e d e q u i t i e s t e n d to r a n g e b e t w e e n 0.65 a n d 1.2 o v e r o u r s a m p l e period. 21 M B N A ' s h i g h e r b e t a p r o v i d e s a m a r k e t - d e t e r m i n e d c a s e o f the g r e a t e r relative riskiness of credit-card banks. Musumeci (1993) provides additional e v i d e n c e o f the a b o v e - a v e r a g e m a r k e t s e n s i t i v i t y o f b a n k s w i t h h e a v y c o n c e n t r a t i o n s in c r e d i t - c a r d r e c e i v a b l e s . F o r 84 b a n k h o l d i n g c o m p a n i e s ( B H C s ) , h e reports a n a v e r a g e b e t a o f 1.41 for the s e c o n d h a l f o f 1991. T h e m o s t e x p o s e d B H C s , as c a p t u r e d b y t h e top quartile, h a d an a v e r a g e b e t a o f 1.58.

20 The only other 'pure play' in credit cards is Advanta Corp., which trades on the OTC and has about one-fourth the amount of credit-card loans as MBNA but a similar volatility of returns. Advanta is not included in Panel C of Table 6 because it has been a stand-alone company since 1974 and does not qualify as a recent IPO. See Stewart (1992) and Sullivan (1992). 21 Distressed BHCs such as the failed Bank of New England and the once-troubled Bank of Boston have higher betas, 1.3 and 1.4, respectively. In contrast, as of December 14, 1990, Value Line reported betas of 1.05 for J.P. Morgan, BankAmerica, Chemical Bank, Wells Fargo, and Security Pacific.

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These market measures of relative risk, which complement and confirm our accounting measures of the greater riskiness of CCBs, suggest that credit-card receivables should be priced to yield a higher rate of return than other banking products. For example, assuming a risk-free rate of five percent and a market risk premium of eight percent, the required return on the N Y S E Financial Index would be 14.4% ( = 5% + 1.18 X 8%) while the required return on M B N A ' s stock would be 17.8% ( = 5% + 1.6 X 8%). The last two columns of Table 6 show the ratio of the most recent market price (1 February 1996) of each credit-card IPO to its issue price and its most recent price-earnings ( P / E ) multiple. Both of these variables embody investors' return and risk expectations. 22 The average market-to-issue ratio of 2.64 suggests growth (or wealth-creation) opportunities in credit-card assets or enhanced value due to a more favorable interest-rate environment or both. For comparative purposes, however, P / E ratios are easier to use because the data are reported daily. The credit-card IPOs in Table 6 have an average P / E of 19 compared to 11.2 for 15 major bank holding companies. 23 The higher average P / E for the specialized credit-card firms could reflect either higher growth prospects (e.g., P V G O associated with hidden assets embedded in a portfolio concentrated in credit-card receivables) or the higher expected earnings and the greater risk associated with an undiversified portfolio of unsecured assets.

5.1. I PO insights and anecdotal evidence

The literature on IPOs offers further insight into our attempt to proxy the economic value of a portfolio of credit-card receivables. Ritter (1991) reports that "... many firms go public near the peak of industry-specific f a d s " (p. 23) and that "... issuers successfully time offers to lower their cost of capital" (p. 24). We do not, of course, regard credit cards as a 'fad'. Nevertheless, like other products, credit cards can be expected to have a 'life cycle' - one which industry analysts agree is in its 'maturity stage' (Mandell, 1990). If sales of credit-card receivables occur as the bank-credit industry matures and if sellers successfully time offers to lower their cost of capital, the values of hidden assets are further enhanced. The expansion of the securitization of card receivables, a substitute for the sale of these

22 Both of these variables are widely accepted as proxies for a firm's investment opportunity set. For example, see Booth (1992), Gaver and Gaver (1993), and Smith and Watts (1993). z3 The P/E ratios are as of 1 February 1996. Splitting the sample into 'money-center' and 'superregionals', the respective P/Es are 10.6 and 11.8. Stewart (1992, p. 28) focused on 14 BHCs ranked in terms of total loans outstanding and led by Citicorp with total loans of $150.9 billion including $34 billion in credit-card loans (22.5%) as of year-end 1991. He listed 15 BHCs but since then Security Pacific was acquired by BOA and Chase has acquired Chemical. He reported a P/E of 10.1 for his group of BHCs.

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assets that permits lenders to retain customer relationships, provides additional evidence to support our hidden-assets hypothesis. In the future, convenience users of credit cards (those paying off their outstanding balances each billing cycle and not incurring any interest charges) could be priced into becoming debit-card users if banks would simply eliminate the interest-free grace period. By spinning off credit-card receivables before such changes occur, CCBs enhance the value of the hidden assets embedded in credit-card receivables, Recent behavior of the resale market provides some support for this 'timing' phenomenon as reflected by the drop in resale premiums (Table 5). Although the timing aspect is important, the underlying economic value derives from the potential customer relationships (list) - the intangible or hidden asset - attached to the tangible cash flows associated with the credit-card receivable. Accordingly, credit-card receivables should continue to sell at a premium to reflect the value of the hidden assets. 24

6. Summary, conclusions, and future research Considering the overall risk of credit-card banks, the role of hidden assets, the surge in the securitization of card receivables, and the opportunity cost of acquiring new accounts provide alternative explanations for the 'puzzling' behavior of the credit-card market. First, measuring overall bank risk with the model ofHannan and Hanweck (1988) (Z = [ROA + C A P ] / s ) , we find that credit-card banks were riskier than non-specialized banks over our test period. This result was evident regardless of how we calculated variability of returns (cross-sectional or time-series standard deviation of ROA). Nevertheless, since the Z-score approach relies on an assumption of ROA being normally distributed, it is an area for future research. The major component of credit-card risk is default risk as captured by loan charge-offs. Here the picture is clear: in 1983, charge-offs were 1.9 percent; by 1995, they had risen to 4.1 percent. Second, analysis of the premiums paid for credit-card portfolios over the years 1986 to 1995 reveals structural changes associated with hidden or intangible assets (e.g., the growth opportunities associated with newly acquired customer relationships) and the securitization of card receivables (a substitute for selling them). Specifically, the premiums have declined slightly but more importantly they show an inverse U-shape with respect to the size of the transaction. This relationship

24 Hammer (1996) provides the following projection: "A few surprise sellers, including larger deals, portfolio prices rising (in some cases to near historic high levels) depending upon credit quality, source or origin of accounts, maturity or seasoning of accounts, and portfolio size. Major players managing their balance sheets through asset sales/purchases will continue to bring larger (partial sale) deals to market' '.

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between the premium and size of the transaction exists because the mid-sized portfolios provide the richest cross-selling opportunities. The largest deals have become less profitable with securitization serving as an alternative way of removing the assets from the seller's balance sheet. Recent rulings by the Comptroller of the Currency (1993), which permit credit-card relationships to be included as a qualifying intangible asset in Tier-1 capital, and by the U.S. Supreme Court, which permits customer lists to be depreciated for tax purposes, strengthen our case about the importance of hidden assets in valuing a portfolio of credit-card receivables. On balance, this paper shows that the higher accounting and market returns earned by credit-card banks do not point to a failure of competition nor to irrational behavior on the part of cardholders. Another explanation for the high premiums is the opportunity cost of acquiring new accounts, which on average is about $75 per account with the cost of soliciting 'premium accounts' as high as $200.

Acknowledgements We thank David W. Blackwell, Mary Dehner, Prakash Dheeriya, Edward J. Kane, Stefanie Kleimeier, Carlos Maquieira, William L. Megginson, James J. Musumeci, and James A. Verbrugge for comments on earlier versions of the manuscript. The latest version of the paper has benefited greatly from comments and suggestions by two journal referees and from discussions with and data supplied by Robert Hammer and Jeff Unkle. An earlier version of the paper was presented at the 1993 Annual Meeting of the FMA in Toronto. This research started while Nash was a doctoral candidate at The University of Georgia. The usual caveats apply.

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