- Email: [email protected]
Linking profits to Greek bank production management
prod&ion economics ELSEVIER
Int. J. Production
Economics
43 (1996) 67-73
Linking profits to Greek bank production management Dimitrios
Vasiliou
Department of Management Science and Marketing, Athens University of Economics and Business, 76 Patission St., GR-10434, Athens, Greece Received
3 October
1994; accepted
31 October
1995
Abstract
This paper applies the statistical cost accounting @CA) methodology to investigate profitability differences between high-profit and low-profit Greek banks. The SCA approach hypothesizes that a bank’s net income can be expressed as the weighted sum of its various assets and liabilities, the weights being the net revenue or costs attributable to each item. The study uses a sample of pooled time series and cross-section data over the period 1977-1986. The empirical evidence suggests that asset and - to a lesser extent - liability management play a role in explaining interbank differences in profitability in Greece during the period examined. Keywords:
Greek banking; Decision making; Bank profitability
1. Introduction
There is a large number of studies which employ the statistical cost accounting (SCA) technique to explain interbank differences in earnings, especially in the USA.’ However, there is no such experience regarding Greek banking, which is quite different from the US system. This gap in the relevant literature may be fulfilled by this paper. This paper uses the statistical cost accounting model to examine profitability differences between four highprofit and four low-profit Greek banks over the period 1977-1986. The comparison of coefficient estimates of the two banking groups with statisti-
’ For reviewing relevant literature see Hester and Zoellner [I], Hester and Pierce [2]. Kwast and Rose [3], Rose and Wolken [4], and Vasiliou [5]. 0925-5273/96/$15.00 0 1996 Elsevier SSDI 0925-5273(95)00202-2
Science B.V. All rights
reserved
cal testing represents a step forward in the research methodology in Greek banking. Moreover, this is the first SCA study to test and correct for autocorrelation utilizing pooled data. The study is divided into five parts. The first section outlines the statistical cost accounting model briefly. The second section presents the data used. The estimation procedure which is employed follows next. Section four reports and interprets the regression results. And finally, some concluding remarks are presented.
2. The model The statistical cost accounting technique attributes interbank differences in profitability to differences in their balance sheet composition. This relation is examined by regressing accounting
68
D. Vasilioulht.
J. Production Economics 43 (1996) 67-73
earnings on bank assets and liabilities. The fundamental hypothesis of the model2 is that the rate of return on assets is positive and vary across assets, and the rate of cost on liabilities is usually negative and vary across liabilities. Thus, the statistical cost accounting model assumes that variations in banks’ earnings for a period of time may be written as Ykn = a + Cbi Aikq f ccj
Ljkq + &kq,
(1)
where Y is the net operating income of a bank; A, is the ith asset, i = 1,2, . . . , M; Lj is the jth liability or equity, j = 1,2, . . . , N; k shows the number of banks, and k = 1,2, . . . , K, q denotes the time period studied, and q = 1,2, . . . , T; bi is the net rate of return on assets after deducting directly associated operating expenses,3 and therefore each bi should be non-negative (i.e. positive or zero); cj is the net rate of cost on liabilities, including operating expenses but eliminating service charges, and therefore each cj should be non-positive (i.e. negative or zero); a is a constant term indicating the existence of income that is unrelated to banks’ portfolio structure; and &k,,is an error term which accounts for stochastic differences among individual banks. Needless to say, the above interpretation of the parameters of the assets and liabilities is general, and changes whenever the definition of the dependent variable change. The regression analysis employed in this study utilizes bank income before income taxes as dependent variable. Bank income before taxes is defined as the income that a bank receives in a year less loan interest expenses, loan losses and securities losses (or gains). Operating costs are included in the dependent variable, because salaries which are the major part of this kind of expenditure in banking were regulated to some extent in Greece during the period examined; that is, they did not vary substantially among Greek banks. As a result, not deducting operating expenses from the bank income variable does not 2 See Hester and Pierce [2, p. 961. 3 We should estimates of include only include loan net of those
bear in mind that as the model provides separate the cost of funds (liabilities), operating expenses non-financing costs. However, operating expenses losses, as the dependent variable is actually the losses.
appear to result in the loss of any informatic albeit it may blur the picture. Consequently, t estimates of this regression may be interpreted marginal rates of return, incorporating thou directly associated operating expenses. The model of Eq. (1) can be used to estim: different rates of return on balance sheet items f the high-profitable banks alone and the lo profitable banks alone. To accomplish this task 1 divide our sample of banks in two groups, accot ing to their returns on assets (ROA). The fi group incorporates the four more profitable fim while the second the four less profitable firms.
3. The data The data assets and liabilities used for this stu, are taken from bank balance sheets. Balance shec report year-end data. Therefore, we transfol them to mid-year data by averaging (using t money weighted arithmetic mean) each year-e] balances with the year-end balances of the pre ous year. The data of the earnings of the banks a obtained from their income statements. Our study covers the period 1977-1986. 1 choose this particular period because the Gre banking system has not undergone drastic chang during this time. Since the beginning of 1987 t process of financial reform in Greece has be accelerated enormously. Consequently, we exclu the more recent period from our analysis, becat various exogenous factors which may spring frc this rapid transition period from a rather regulat system to a deregulated one, may influence ban balance sheet items differently, and shadow the 1: ture considerably. The Greek commercial bar that were operating for at least five years befc the ten year period we examine, are eight.4 It worth pointing out, however, that the market sh, which is commanded by these eight banks is o 90% of the whole commercial Greek bank
4 The behaviour of new banks may be quite different of older institutions. It usually takes time to train build up loan portfolios, attract depositors and so more discussion on this argument see Kwast [6, p. and Olson [7, pp. 37-381, and Kwast and Black fn. 31.
from person forth. 381, F [8, p.
D. Vasilioulht. J. Production Economics 43 (1996) 67-73
industry. Nevertheless, our sample consists of eighty observations; that is, ten year observations of eight commercial banks.
4. Estimation procedure Estimation of the model expressed by Eq. (1) cannot be done, because the model exhibits perfect collinearity. This is owing to the fact that the sum of all assets is equal to the sum of all liabilities and equity. However, the empirical estimation is possible, if we omit equity capital from Eq. (l), byassuming that its rate of cost is zero.5 By this assumption we overcome the problem of perfect collinearity. The justification of the above exclusion is based upon the notion that net income includes dividend payments, and consequently equity contributes no expense on the income statement. Let us assume now that all the coefficients of Eq. (1) are constant for each banking group. That is, there is a common intercept and a common set of slope coefficients for each banking class for the whole period studied. This assumption may seem too stringent,6 but it is necessary for our purpose as we can employ neither solely cross-section nor solely time-series analysis due to the small population of the Greek commercial banks. This assumption permits us to run an ordinary leastsquares (OLS) regression for each banking group by pooling all the respective observations. Our sample consists of cross-section and timeseries data, and therefore it is possible that the usual assumptions about heteroscedasticity and 5If the rate of cost on equity capital is non-zero, the estimated coefficients should be interpreted as the difference between the rate of return (cost) on each particular asset (liability), and the rate of cost on equity capital. Otherwise, specification error will be introduced to the regression equation. Even if this is the case, however, the specification error does not seem to shadow the scene excessively, because we estimate differences in yields between two banking groups; the high-profitable group and the low-profitable one. 6 We have also applied a more realistic assumption: the slope coefficients are constant, but the intercept varies across banking firms and over time. A least squares with dummy variables (LSDV) model was used for this purpose. However, an F-test which was utilized in order to decide upon whether we should include the dummy variables in our model, obliged us to reject the null hypothesis, and abandon the LSDV model.
69
autocorrelation may be violated. Consequently, we apply some tests to ensure that the classical conditions are satisfied. To test for autocorrelation in our model, we cannot utilize the Durbin-Watson statistic because of the transition of the ordered sample across banking firms. Instead, the autocorrelation coefficient (p) is computed directly by using the regression residuals, and assuming that the parameter p has the same value for all the banks (i.e. all cross-sectional units).7 The estimated values of the parameter p provide some evidence of correlated disturbances. To correct for autoregression, assuming firstorder pattern8 we apply the Cochrane-Orcutt [lo] iterative method. However, this technique does not offer a guarantee that it will locate a global minimum.’ To ensure location of a global minimum we also apply the Hildreth-Lu [12] method. As far as the first banking group is concerned, both techniques provide estimates of the autocorrelation coefficient (p) close to minus one. Consequently, we take the first sums of the original data, and apply OLS to the transformed model. Regarding the second banking group, the estimated rho by the Cochrane-Orcutt and the Hildreth-Lu techniques is found statistically insignificant. Hence, the evidence suggests that there is no first-order autocorrelation in the model applied to the second group. To test for heteroscedasticity in our model, we employ a Breusch-Pagan [13] test. The relevant statistic is 4 = 24.246 for the model applied to the first banking group and q = 36.939 to the second. Thus, the null hypothesis of homoscedastic disturbances is accepted for the first class, but rejected for the second [x*0.95,15= 24.996, x*0.99,15= 30.5781. To correct for heteroscedasticity in the model applied to the second group, we perform a number of transformations. However, none of ‘See Kmenta
[9, pp. 618-6221.
*We employ the assumption of first-order autoregression for two reasons. First, we utilize annual data for our study and therefore we do not expect to observe more than a year dependence of errors (i.e. a second- or a higher-order autoregressive scheme). Second, the use of a higher- than a first-order autocorrelation pattern and the associated loss of degrees of freedom would decrease the reliability of our estimates. ‘See Judge
et al. [ll,
p. 2881.
70
D. Vasilioullnt. J. Production Economics 43 (1996) 67-73
them provide a Breusch-Pagan statistic that could accept the nuil hypothesis. Consequently, we apply the formulae suggested by White [14] to compute consistent variances of the estimators.
5. Empirical results The explanatory variables used in our analysis are described in Table 1 Tables 2 and 3 report the estimated coefficients of the model which is applied to the high-profit banking group, and the low-profit, respectively. Thirteen out of the sixteen coefficients of the first regression are statistically significant in a twotailed t-test, and twelve coefficients of the second regression. These estimates appear generally plausible in that their signs conform to a priori expectations. All the rates of return on assets but one are found positive, and most of the significant rates of cost on liabilities are found negative. As previously discussed, these coefficients should approximate the marginal rates of return or costs that banks realize from holding various assets and liabilities, including directly associated expenses. The intercept of the high-profit banking group is found significantly positive, indicating that the earnings that are unrelated to the balance sheet items of the high-income banks exceed their respec-
Table 1 Definition
of variables
Symbol
Description
Al A2 A3 A4 A5 A6 A7 A8 Ll L2 L3 L4 L5 L6 L7
Cash and due from banks Discounts Loans and advances up to one year Loans and advances over a year Sundry asset accounts Total securities Buildings and other fixed assets Unclassified and miscellaneous assets Loss provisions Demand deposits Savings deposits Time deposits Sundry liability accounts Dividends payable Unclassified and miscellaneous liabilities
Table 2 Regression
results for the high-profit
Dependent
variable:
Bank income
banking before
group
taxes
Variables
Coefficients
t-statistics
C Al A2 A3 A4 A5 A6 A7 A8 LI L2 L3 L4 L5 L6 L7
0.0843 0.1310 0.3803 0.1171 0.0920 0.1721 -0.0461 0.7779 0.5336 0.4223 -0.2092 -0.1214 -0.2063 0.0013 3.4751 0.9298
2.4s 7.12** 7.06** 4.87** 3.33** 7.60** -0.88 4.42** 6.37** 0.83 -4.64** -6.16** -5.25** 0.17 4.26** 5.98**
SER=0.143 R= = 0.999 P=o.999 F-statistic = 8944.680 Note: Starred *(**) terms indicate parameters statistically different from zero at 0.05 (0.01) confidence level in a two-tailed t-test.
tive expenses. In consequence, the most profitable firms seem to face revenues produced by, say, trust departments, underwriting, and safe deposit facilities which surpass their expenses for electricity and advertising. Tables 2 and 3 furnish some puzzling results as far as the asset side is concerned. Low-income firms appear to experience higher rates of return on cash and due from banks, loans and advances up to one year, loans and advances over a year, and sundry asset accounts than their high-income opponents. On the other hand, the estimated annual rates of return on discounts, buildings and other fixed assets, and unclassified and miscellaneous assets are found higher for the first banking class than the second. While the latter findings are expected and therefore will not be discussed further, the former results need some comments. As far as credit is concerned, the above unusual divergence may be justified with the assistance of the
D. Vasilioullnt. J. Production Economics 43 (1996) 67-73 Table 3 Regression
results for the low-profit
Dependent
variable
banking
group
: Bank income before taxes
Variables
Coefficients
t-statistics
C Al A2 A3 A4 A5 A6 A7 A8 Ll L2 L3 L4 L5 L6 L7
0.0097 0.2603 0.2067 0.2025 0.1882 0.2358 0.1754 0.3175 0.2996 0.2021 -0.2327 -0.1791 -0.2736 -0.1995 2.4006 0.3168
0.48 3.49** 2.35’ 2.94** 2.62* 3.55** 1.91 8.33** 3.35** 0.97 -2.58* -2.81* -3.58** -2.65* 5.05** 1.78
SER = 0.092
R2=0.999 F=o.999 F-statistic
= 39732.100
Notes: (1) Starred *(**) terms indicate parameters statistically different from zero at the 0.05 (0.01) confidence level in a twotailed t-test. (2) The t-statistics shown are heteroscedastic-consistent estimates.
following conjecture. During the examined period, the Greek authorities have imposed ceilings on almost all rates on credit. These ceilings “favoured” some activities like exports and longterm capital investment, and “disfavoured” some others like trade and consumer credit. As a result, the encouraging activities enjoyed low interest rates, while the discouraging activities experienced high interest rates. In consequence, the empirical evidence of this study suggests that low-earnings banks may finance relatively less the “priority sectors” than their high-earnings opponents, and vice versa. As regards sundry asset accounts, the preceding odd result should not bother us enormously due to the unknown composition of these accounts. Finally, the difference in yields on cash and due from banks between the two groups may be explained by quessing that the composition of this asset category of low-performance banks favours more interest-bearing components that are
71
also less liquid, than that of their high-performance rivals. lo The results of the liability side provide information which emerge less paradoxical than that of the asset side. High-performance institutions seem able to experience lower rates of cost on demand deposits, savings deposits, and time deposits than their low-performance counterparts. However, the estimated return on demand deposits appears surprisingly high. This finding may be attributed to the following reasons. First, it may be due to the high servicing cost per cheque, as cheques are not very popular as a means of payment in the country of our interest. Second, it may be owing to the structure of this account, as interestbearing current account deposits are reported with non-interest-bearing demand deposits in Greece. And finally, the existence of implicit interest expences like free banking, free other services and so forth may increase the rate of cost of this account. The estimated difference in time deposits between the two banking groups may be due to a different composition of this account. That is, high-earnings firms may have relatively more lowinterest time deposits than their low-earnings opponents.” Nevertheless, the divergence of the estimated rates of costs on demand, savings and time deposits between the two banking classes suggests that the cost of funds of the high-earnings firms may be lower than that of the low-earnings fiITllS.
The above comparison of coefficients of high- and low-income financial institutions will not provide us with sufficient information if it is not accompanied by statistical testing. This is presented in Table 4, which tabulates the differences between high- and low-profit banks’ coefficients. Eight out of the sixteen differences in coefficients are found statistically significant in a two-tailed t-test. Consequently, high-income banks appear to experience lower rates of return on cash and due
“It is worth noting that cash and due from banks is a consolidated account consisting of cash in hand, deposits with domestic and foreign banks, deposits with the Bank of Greece, and interest-bearing Greek treasury bills. ‘t The interest rates on time deposits set by the Greek authorities during the period studied, depended upon the time and the volume of the funds placed at the bank.
12
D. Vasilioullnt.
J. Production
Table 4 Differences of the estimated yields between high-profit and lowprofit banks Variables
Difference in coefficients
f-statistics
C
0.0746 -0.1293 0.1736 - 0.0854 - 0.0962 -0.0637 -0.2215 0.4604 0.2340 0.2202 0.0235 0.0577 0.0673 0.2008 1.0745 0.6130
2.64* -2.38* 2.38* - 1.66 - 1.17 -1.28 - 2.96** 3.61** 2.10* 0.57 0.33 1.22 1.11 3.75*+ 1.61 3.67**
Al A2 A3 A4 A5 A6 A7 A8 Ll L2 L3 L4 L5 L6 L7
Note: Starred *(**) terms indicate parameters statistically significant at the 0.05 (0.01) confidence level in a two-tailed f-test.
from banks as well as total securities. On the other hand, high-profit banks seem to enjoy higher rates of return on discounts, buildings and other fixed assets, and unclassified and miscellaneous assets, as well as higher earnings that are unrelated to their balance sheet items than their low-profit rivals. Finally, high performance firms appear to experience relatively lower rates of cost on sundry liability accounts as well as unclassified and miscellaneous liabilities than their low-performance opponents.
6. Concluding remarks This paper reports differences in empirical estimates of net rates of return earned by two groups of Greek commercial banks from various assets and liabilities in their portfolios. The first group incorporates the high-profit banks, while the second category includes the low-profit banks. On the whole, the study confirms the fundamental hypothesis of the SCA model. Most of the estimated rates of return on assets (liabilities) are
Economics
43 (1996) 67-73
found positive (negative) and vary across assets (liabilities). Most researchers agree that differences in banks’ earnings should be reflected in at least one of the following four categories: (i) net rate of return on assets, (ii) net rate of cost on liabilities, (iii) composition of asset portfolio, and (iv) sources of liability funding. The preceding study seems to support two major conclusions regarding interbank differences in profitability in Greece during the period examined. First, there is evidence to suggest that high-profit banks earn higher rates of return on some of their assets, than their low-profit counterparts. Second, the regression results indicate that high-earnings banks may experience lower rates of cost on their liabilities, than their low-earnings rivals. This finding implies that asset - and to a lesser degree - liability management may be important factors in achieving high earnings in Greek banking. In addition, the differences of the estimated deposit coefficients between the two banking groups are found statistically insignificant. This result indicates that liability funding is of rather minor importance in Greek bank profitability. Moreover, the differences of the estimated yields from the two banking classes do not provide compelling evidence that high-profit banks set considerably different prices than their low-profit rivals. These findings suggest that the composition of asset portfolio may be more important than differences in marginal rate of return on assets and liabilities. The above investigation points out that the highprofit banking group realizes income generated by off-the-balance-sheet activities. The off-balancesheet activities of banks usually denote transactions which generate income without passing across the balance sheet. In this case the off-balance-sheet items of banks embrace two main classes of activities. The first class involves contingent claims, while the second class incorporates various financial services. The items that give rise to direct contingent claims could be classified under the following four headings: loan commitments, guarantees, swap and hedging transactions, and investment banking activities. The financial services that are associated with off-balance-sheet
D. Vasilioullnt. J. Production Economics 43 (1996) 67-73
activities are loan-related services, trust and advisory services, brokerage/agency services, payment services, and export/import services.” The off-balance-sheet transactions of banks is by no means a new phenomenon, although the extent of such activity is now sizeable and growing world-wide. Nevertheless, the finding of this study suggests that the off-balance-sheet business is of considerable importance in Greek commercial banking. From the foregoing it follows that the SCA methodology could be useful in identifying some of the factors that account for Greek bank profitability. Highlighting the importance of these factors will be valuable not only to bank managers but to bank regulators as well. The former will be provided with some indications as to how and where they should allocate their time and attention in order to improve the performance of their institutions. The latter will be assisted in improving their understanding of the effects of their policies on bank profitability and in evaluating the soundness of the financial firms they supervise. Finally, the findings of this study may also be used as avenues for future research. Also, it would be worthwhile to apply the SCA methodology to examine profitability differences between highprofit and low-profit Greek banks over different time periods. Marginal rates of return estimated for this two banking groups in different years should considerably improve our knowledge of Greek bank profitability.
Acknowledgements The author wishes to thank two anonymous reviewers of this journal for valuable comments and suggestions on an earlier version of this paper.
‘*For more information of banks see Lewis [15].
about
off-the-balance-sheet
activities
13
References Hester, D.D. and Zoellner, J.F., 1966. The relation between bank portfolios and earnings: An econometric Analysis, Rev. Econom. Statist., 48: 372-386. PI Hester, D.D. and Pierce, J.L., 1975. Bank Management and Portfolio Behavior. Yale University Press, New Haven. 131 Kwast, M.L. and Rose, J.T., 1982. Pricing, operating efficiency, and profitability among large commercial banks. J. Banking Finance, 6: 233-254. [41 Rose, J.T. and Wolken, J.D., 1986. Statistical cost accounting models in banking: A reexamination and an application, Staff Economic Studies No. 150, Board of Governors of the Federal Reserve System, Washington DC. of the [51 Vasiliou, D., 1990. An empirical investigation profitability of Greek commercial banks, Unpublished Ph.D. Thesis, University of Kent at Canterbury, Canterbury. commercial Fl Kwast, M.L., 1981. New minority-owned banks: A statistical analysis, J. Bank Res., 12: 37-45. 171 Ford, W.F. and Olson, D.A., 1978. How 1000 high-performance banks weathered the recent recession. Banking, 70: 36-48. PI Kwast, M.L. and Black, H., 1983. An analysis of the behaviour of the mature black-owned commercial banks. J. Econom. Bus., 35: 41-54. 2nd ed. [91 Kmenta, J., 1986. Elements of Econometrics, Macmillan Publishing Co., New York. [lOI Cochrane, D. and Orcutt, G.H., 1949. Application of least squares regressions to relationships containing autocorrelated error terms. J. Amer. Statist. Assoc., 441 32-61. 011 Judge, G.G., Grifiths, W.E., Hill, R.C., Lutkepohl, H. and Lee, T., 1985. The Theory and Practice of Econometrics, 2nd ed. Wiley, New York. [121 Hildreth, C. and Lu, J.Y., 1960. Demand relations with autocorrelated disturbances. Michigan State University Agricultural Experiment Station Technical Bulletin 276, East Lansing, Michigan. 1131 Breusch, T.S. and Pagan, A.R., 1979. A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47: 1287-1294. covariv41 White, H., 1980. A heteroscedasticity-consistent ante matrix estimator and a direct test for heteroscedasticity. Econometrica, 48: 817-838. activities. in: [I51 Lewis, M.K., 1992. Off-the-balance-sheet Newman, P., Milgate, M. and Eatwell, J. (Eds.), The New Palgrave Dictionary of Money & Finance, Vol. 3, pp 67-72. The Macmillan Press Limited, London.
Int. J. Production
Economics
43 (1996) 67-73
Linking profits to Greek bank production management Dimitrios
Vasiliou
Department of Management Science and Marketing, Athens University of Economics and Business, 76 Patission St., GR-10434, Athens, Greece Received
3 October
1994; accepted
31 October
1995
Abstract
This paper applies the statistical cost accounting @CA) methodology to investigate profitability differences between high-profit and low-profit Greek banks. The SCA approach hypothesizes that a bank’s net income can be expressed as the weighted sum of its various assets and liabilities, the weights being the net revenue or costs attributable to each item. The study uses a sample of pooled time series and cross-section data over the period 1977-1986. The empirical evidence suggests that asset and - to a lesser extent - liability management play a role in explaining interbank differences in profitability in Greece during the period examined. Keywords:
Greek banking; Decision making; Bank profitability
1. Introduction
There is a large number of studies which employ the statistical cost accounting (SCA) technique to explain interbank differences in earnings, especially in the USA.’ However, there is no such experience regarding Greek banking, which is quite different from the US system. This gap in the relevant literature may be fulfilled by this paper. This paper uses the statistical cost accounting model to examine profitability differences between four highprofit and four low-profit Greek banks over the period 1977-1986. The comparison of coefficient estimates of the two banking groups with statisti-
’ For reviewing relevant literature see Hester and Zoellner [I], Hester and Pierce [2]. Kwast and Rose [3], Rose and Wolken [4], and Vasiliou [5]. 0925-5273/96/$15.00 0 1996 Elsevier SSDI 0925-5273(95)00202-2
Science B.V. All rights
reserved
cal testing represents a step forward in the research methodology in Greek banking. Moreover, this is the first SCA study to test and correct for autocorrelation utilizing pooled data. The study is divided into five parts. The first section outlines the statistical cost accounting model briefly. The second section presents the data used. The estimation procedure which is employed follows next. Section four reports and interprets the regression results. And finally, some concluding remarks are presented.
2. The model The statistical cost accounting technique attributes interbank differences in profitability to differences in their balance sheet composition. This relation is examined by regressing accounting
68
D. Vasilioulht.
J. Production Economics 43 (1996) 67-73
earnings on bank assets and liabilities. The fundamental hypothesis of the model2 is that the rate of return on assets is positive and vary across assets, and the rate of cost on liabilities is usually negative and vary across liabilities. Thus, the statistical cost accounting model assumes that variations in banks’ earnings for a period of time may be written as Ykn = a + Cbi Aikq f ccj
Ljkq + &kq,
(1)
where Y is the net operating income of a bank; A, is the ith asset, i = 1,2, . . . , M; Lj is the jth liability or equity, j = 1,2, . . . , N; k shows the number of banks, and k = 1,2, . . . , K, q denotes the time period studied, and q = 1,2, . . . , T; bi is the net rate of return on assets after deducting directly associated operating expenses,3 and therefore each bi should be non-negative (i.e. positive or zero); cj is the net rate of cost on liabilities, including operating expenses but eliminating service charges, and therefore each cj should be non-positive (i.e. negative or zero); a is a constant term indicating the existence of income that is unrelated to banks’ portfolio structure; and &k,,is an error term which accounts for stochastic differences among individual banks. Needless to say, the above interpretation of the parameters of the assets and liabilities is general, and changes whenever the definition of the dependent variable change. The regression analysis employed in this study utilizes bank income before income taxes as dependent variable. Bank income before taxes is defined as the income that a bank receives in a year less loan interest expenses, loan losses and securities losses (or gains). Operating costs are included in the dependent variable, because salaries which are the major part of this kind of expenditure in banking were regulated to some extent in Greece during the period examined; that is, they did not vary substantially among Greek banks. As a result, not deducting operating expenses from the bank income variable does not 2 See Hester and Pierce [2, p. 961. 3 We should estimates of include only include loan net of those
bear in mind that as the model provides separate the cost of funds (liabilities), operating expenses non-financing costs. However, operating expenses losses, as the dependent variable is actually the losses.
appear to result in the loss of any informatic albeit it may blur the picture. Consequently, t estimates of this regression may be interpreted marginal rates of return, incorporating thou directly associated operating expenses. The model of Eq. (1) can be used to estim: different rates of return on balance sheet items f the high-profitable banks alone and the lo profitable banks alone. To accomplish this task 1 divide our sample of banks in two groups, accot ing to their returns on assets (ROA). The fi group incorporates the four more profitable fim while the second the four less profitable firms.
3. The data The data assets and liabilities used for this stu, are taken from bank balance sheets. Balance shec report year-end data. Therefore, we transfol them to mid-year data by averaging (using t money weighted arithmetic mean) each year-e] balances with the year-end balances of the pre ous year. The data of the earnings of the banks a obtained from their income statements. Our study covers the period 1977-1986. 1 choose this particular period because the Gre banking system has not undergone drastic chang during this time. Since the beginning of 1987 t process of financial reform in Greece has be accelerated enormously. Consequently, we exclu the more recent period from our analysis, becat various exogenous factors which may spring frc this rapid transition period from a rather regulat system to a deregulated one, may influence ban balance sheet items differently, and shadow the 1: ture considerably. The Greek commercial bar that were operating for at least five years befc the ten year period we examine, are eight.4 It worth pointing out, however, that the market sh, which is commanded by these eight banks is o 90% of the whole commercial Greek bank
4 The behaviour of new banks may be quite different of older institutions. It usually takes time to train build up loan portfolios, attract depositors and so more discussion on this argument see Kwast [6, p. and Olson [7, pp. 37-381, and Kwast and Black fn. 31.
from person forth. 381, F [8, p.
D. Vasilioulht. J. Production Economics 43 (1996) 67-73
industry. Nevertheless, our sample consists of eighty observations; that is, ten year observations of eight commercial banks.
4. Estimation procedure Estimation of the model expressed by Eq. (1) cannot be done, because the model exhibits perfect collinearity. This is owing to the fact that the sum of all assets is equal to the sum of all liabilities and equity. However, the empirical estimation is possible, if we omit equity capital from Eq. (l), byassuming that its rate of cost is zero.5 By this assumption we overcome the problem of perfect collinearity. The justification of the above exclusion is based upon the notion that net income includes dividend payments, and consequently equity contributes no expense on the income statement. Let us assume now that all the coefficients of Eq. (1) are constant for each banking group. That is, there is a common intercept and a common set of slope coefficients for each banking class for the whole period studied. This assumption may seem too stringent,6 but it is necessary for our purpose as we can employ neither solely cross-section nor solely time-series analysis due to the small population of the Greek commercial banks. This assumption permits us to run an ordinary leastsquares (OLS) regression for each banking group by pooling all the respective observations. Our sample consists of cross-section and timeseries data, and therefore it is possible that the usual assumptions about heteroscedasticity and 5If the rate of cost on equity capital is non-zero, the estimated coefficients should be interpreted as the difference between the rate of return (cost) on each particular asset (liability), and the rate of cost on equity capital. Otherwise, specification error will be introduced to the regression equation. Even if this is the case, however, the specification error does not seem to shadow the scene excessively, because we estimate differences in yields between two banking groups; the high-profitable group and the low-profitable one. 6 We have also applied a more realistic assumption: the slope coefficients are constant, but the intercept varies across banking firms and over time. A least squares with dummy variables (LSDV) model was used for this purpose. However, an F-test which was utilized in order to decide upon whether we should include the dummy variables in our model, obliged us to reject the null hypothesis, and abandon the LSDV model.
69
autocorrelation may be violated. Consequently, we apply some tests to ensure that the classical conditions are satisfied. To test for autocorrelation in our model, we cannot utilize the Durbin-Watson statistic because of the transition of the ordered sample across banking firms. Instead, the autocorrelation coefficient (p) is computed directly by using the regression residuals, and assuming that the parameter p has the same value for all the banks (i.e. all cross-sectional units).7 The estimated values of the parameter p provide some evidence of correlated disturbances. To correct for autoregression, assuming firstorder pattern8 we apply the Cochrane-Orcutt [lo] iterative method. However, this technique does not offer a guarantee that it will locate a global minimum.’ To ensure location of a global minimum we also apply the Hildreth-Lu [12] method. As far as the first banking group is concerned, both techniques provide estimates of the autocorrelation coefficient (p) close to minus one. Consequently, we take the first sums of the original data, and apply OLS to the transformed model. Regarding the second banking group, the estimated rho by the Cochrane-Orcutt and the Hildreth-Lu techniques is found statistically insignificant. Hence, the evidence suggests that there is no first-order autocorrelation in the model applied to the second group. To test for heteroscedasticity in our model, we employ a Breusch-Pagan [13] test. The relevant statistic is 4 = 24.246 for the model applied to the first banking group and q = 36.939 to the second. Thus, the null hypothesis of homoscedastic disturbances is accepted for the first class, but rejected for the second [x*0.95,15= 24.996, x*0.99,15= 30.5781. To correct for heteroscedasticity in the model applied to the second group, we perform a number of transformations. However, none of ‘See Kmenta
[9, pp. 618-6221.
*We employ the assumption of first-order autoregression for two reasons. First, we utilize annual data for our study and therefore we do not expect to observe more than a year dependence of errors (i.e. a second- or a higher-order autoregressive scheme). Second, the use of a higher- than a first-order autocorrelation pattern and the associated loss of degrees of freedom would decrease the reliability of our estimates. ‘See Judge
et al. [ll,
p. 2881.
70
D. Vasilioullnt. J. Production Economics 43 (1996) 67-73
them provide a Breusch-Pagan statistic that could accept the nuil hypothesis. Consequently, we apply the formulae suggested by White [14] to compute consistent variances of the estimators.
5. Empirical results The explanatory variables used in our analysis are described in Table 1 Tables 2 and 3 report the estimated coefficients of the model which is applied to the high-profit banking group, and the low-profit, respectively. Thirteen out of the sixteen coefficients of the first regression are statistically significant in a twotailed t-test, and twelve coefficients of the second regression. These estimates appear generally plausible in that their signs conform to a priori expectations. All the rates of return on assets but one are found positive, and most of the significant rates of cost on liabilities are found negative. As previously discussed, these coefficients should approximate the marginal rates of return or costs that banks realize from holding various assets and liabilities, including directly associated expenses. The intercept of the high-profit banking group is found significantly positive, indicating that the earnings that are unrelated to the balance sheet items of the high-income banks exceed their respec-
Table 1 Definition
of variables
Symbol
Description
Al A2 A3 A4 A5 A6 A7 A8 Ll L2 L3 L4 L5 L6 L7
Cash and due from banks Discounts Loans and advances up to one year Loans and advances over a year Sundry asset accounts Total securities Buildings and other fixed assets Unclassified and miscellaneous assets Loss provisions Demand deposits Savings deposits Time deposits Sundry liability accounts Dividends payable Unclassified and miscellaneous liabilities
Table 2 Regression
results for the high-profit
Dependent
variable:
Bank income
banking before
group
taxes
Variables
Coefficients
t-statistics
C Al A2 A3 A4 A5 A6 A7 A8 LI L2 L3 L4 L5 L6 L7
0.0843 0.1310 0.3803 0.1171 0.0920 0.1721 -0.0461 0.7779 0.5336 0.4223 -0.2092 -0.1214 -0.2063 0.0013 3.4751 0.9298
2.4s 7.12** 7.06** 4.87** 3.33** 7.60** -0.88 4.42** 6.37** 0.83 -4.64** -6.16** -5.25** 0.17 4.26** 5.98**
SER=0.143 R= = 0.999 P=o.999 F-statistic = 8944.680 Note: Starred *(**) terms indicate parameters statistically different from zero at 0.05 (0.01) confidence level in a two-tailed t-test.
tive expenses. In consequence, the most profitable firms seem to face revenues produced by, say, trust departments, underwriting, and safe deposit facilities which surpass their expenses for electricity and advertising. Tables 2 and 3 furnish some puzzling results as far as the asset side is concerned. Low-income firms appear to experience higher rates of return on cash and due from banks, loans and advances up to one year, loans and advances over a year, and sundry asset accounts than their high-income opponents. On the other hand, the estimated annual rates of return on discounts, buildings and other fixed assets, and unclassified and miscellaneous assets are found higher for the first banking class than the second. While the latter findings are expected and therefore will not be discussed further, the former results need some comments. As far as credit is concerned, the above unusual divergence may be justified with the assistance of the
D. Vasilioullnt. J. Production Economics 43 (1996) 67-73 Table 3 Regression
results for the low-profit
Dependent
variable
banking
group
: Bank income before taxes
Variables
Coefficients
t-statistics
C Al A2 A3 A4 A5 A6 A7 A8 Ll L2 L3 L4 L5 L6 L7
0.0097 0.2603 0.2067 0.2025 0.1882 0.2358 0.1754 0.3175 0.2996 0.2021 -0.2327 -0.1791 -0.2736 -0.1995 2.4006 0.3168
0.48 3.49** 2.35’ 2.94** 2.62* 3.55** 1.91 8.33** 3.35** 0.97 -2.58* -2.81* -3.58** -2.65* 5.05** 1.78
SER = 0.092
R2=0.999 F=o.999 F-statistic
= 39732.100
Notes: (1) Starred *(**) terms indicate parameters statistically different from zero at the 0.05 (0.01) confidence level in a twotailed t-test. (2) The t-statistics shown are heteroscedastic-consistent estimates.
following conjecture. During the examined period, the Greek authorities have imposed ceilings on almost all rates on credit. These ceilings “favoured” some activities like exports and longterm capital investment, and “disfavoured” some others like trade and consumer credit. As a result, the encouraging activities enjoyed low interest rates, while the discouraging activities experienced high interest rates. In consequence, the empirical evidence of this study suggests that low-earnings banks may finance relatively less the “priority sectors” than their high-earnings opponents, and vice versa. As regards sundry asset accounts, the preceding odd result should not bother us enormously due to the unknown composition of these accounts. Finally, the difference in yields on cash and due from banks between the two groups may be explained by quessing that the composition of this asset category of low-performance banks favours more interest-bearing components that are
71
also less liquid, than that of their high-performance rivals. lo The results of the liability side provide information which emerge less paradoxical than that of the asset side. High-performance institutions seem able to experience lower rates of cost on demand deposits, savings deposits, and time deposits than their low-performance counterparts. However, the estimated return on demand deposits appears surprisingly high. This finding may be attributed to the following reasons. First, it may be due to the high servicing cost per cheque, as cheques are not very popular as a means of payment in the country of our interest. Second, it may be owing to the structure of this account, as interestbearing current account deposits are reported with non-interest-bearing demand deposits in Greece. And finally, the existence of implicit interest expences like free banking, free other services and so forth may increase the rate of cost of this account. The estimated difference in time deposits between the two banking groups may be due to a different composition of this account. That is, high-earnings firms may have relatively more lowinterest time deposits than their low-earnings opponents.” Nevertheless, the divergence of the estimated rates of costs on demand, savings and time deposits between the two banking classes suggests that the cost of funds of the high-earnings firms may be lower than that of the low-earnings fiITllS.
The above comparison of coefficients of high- and low-income financial institutions will not provide us with sufficient information if it is not accompanied by statistical testing. This is presented in Table 4, which tabulates the differences between high- and low-profit banks’ coefficients. Eight out of the sixteen differences in coefficients are found statistically significant in a two-tailed t-test. Consequently, high-income banks appear to experience lower rates of return on cash and due
“It is worth noting that cash and due from banks is a consolidated account consisting of cash in hand, deposits with domestic and foreign banks, deposits with the Bank of Greece, and interest-bearing Greek treasury bills. ‘t The interest rates on time deposits set by the Greek authorities during the period studied, depended upon the time and the volume of the funds placed at the bank.
12
D. Vasilioullnt.
J. Production
Table 4 Differences of the estimated yields between high-profit and lowprofit banks Variables
Difference in coefficients
f-statistics
C
0.0746 -0.1293 0.1736 - 0.0854 - 0.0962 -0.0637 -0.2215 0.4604 0.2340 0.2202 0.0235 0.0577 0.0673 0.2008 1.0745 0.6130
2.64* -2.38* 2.38* - 1.66 - 1.17 -1.28 - 2.96** 3.61** 2.10* 0.57 0.33 1.22 1.11 3.75*+ 1.61 3.67**
Al A2 A3 A4 A5 A6 A7 A8 Ll L2 L3 L4 L5 L6 L7
Note: Starred *(**) terms indicate parameters statistically significant at the 0.05 (0.01) confidence level in a two-tailed f-test.
from banks as well as total securities. On the other hand, high-profit banks seem to enjoy higher rates of return on discounts, buildings and other fixed assets, and unclassified and miscellaneous assets, as well as higher earnings that are unrelated to their balance sheet items than their low-profit rivals. Finally, high performance firms appear to experience relatively lower rates of cost on sundry liability accounts as well as unclassified and miscellaneous liabilities than their low-performance opponents.
6. Concluding remarks This paper reports differences in empirical estimates of net rates of return earned by two groups of Greek commercial banks from various assets and liabilities in their portfolios. The first group incorporates the high-profit banks, while the second category includes the low-profit banks. On the whole, the study confirms the fundamental hypothesis of the SCA model. Most of the estimated rates of return on assets (liabilities) are
Economics
43 (1996) 67-73
found positive (negative) and vary across assets (liabilities). Most researchers agree that differences in banks’ earnings should be reflected in at least one of the following four categories: (i) net rate of return on assets, (ii) net rate of cost on liabilities, (iii) composition of asset portfolio, and (iv) sources of liability funding. The preceding study seems to support two major conclusions regarding interbank differences in profitability in Greece during the period examined. First, there is evidence to suggest that high-profit banks earn higher rates of return on some of their assets, than their low-profit counterparts. Second, the regression results indicate that high-earnings banks may experience lower rates of cost on their liabilities, than their low-earnings rivals. This finding implies that asset - and to a lesser degree - liability management may be important factors in achieving high earnings in Greek banking. In addition, the differences of the estimated deposit coefficients between the two banking groups are found statistically insignificant. This result indicates that liability funding is of rather minor importance in Greek bank profitability. Moreover, the differences of the estimated yields from the two banking classes do not provide compelling evidence that high-profit banks set considerably different prices than their low-profit rivals. These findings suggest that the composition of asset portfolio may be more important than differences in marginal rate of return on assets and liabilities. The above investigation points out that the highprofit banking group realizes income generated by off-the-balance-sheet activities. The off-balancesheet activities of banks usually denote transactions which generate income without passing across the balance sheet. In this case the off-balance-sheet items of banks embrace two main classes of activities. The first class involves contingent claims, while the second class incorporates various financial services. The items that give rise to direct contingent claims could be classified under the following four headings: loan commitments, guarantees, swap and hedging transactions, and investment banking activities. The financial services that are associated with off-balance-sheet
D. Vasilioullnt. J. Production Economics 43 (1996) 67-73
activities are loan-related services, trust and advisory services, brokerage/agency services, payment services, and export/import services.” The off-balance-sheet transactions of banks is by no means a new phenomenon, although the extent of such activity is now sizeable and growing world-wide. Nevertheless, the finding of this study suggests that the off-balance-sheet business is of considerable importance in Greek commercial banking. From the foregoing it follows that the SCA methodology could be useful in identifying some of the factors that account for Greek bank profitability. Highlighting the importance of these factors will be valuable not only to bank managers but to bank regulators as well. The former will be provided with some indications as to how and where they should allocate their time and attention in order to improve the performance of their institutions. The latter will be assisted in improving their understanding of the effects of their policies on bank profitability and in evaluating the soundness of the financial firms they supervise. Finally, the findings of this study may also be used as avenues for future research. Also, it would be worthwhile to apply the SCA methodology to examine profitability differences between highprofit and low-profit Greek banks over different time periods. Marginal rates of return estimated for this two banking groups in different years should considerably improve our knowledge of Greek bank profitability.
Acknowledgements The author wishes to thank two anonymous reviewers of this journal for valuable comments and suggestions on an earlier version of this paper.
‘*For more information of banks see Lewis [15].
about
off-the-balance-sheet
activities
13
References Hester, D.D. and Zoellner, J.F., 1966. The relation between bank portfolios and earnings: An econometric Analysis, Rev. Econom. Statist., 48: 372-386. PI Hester, D.D. and Pierce, J.L., 1975. Bank Management and Portfolio Behavior. Yale University Press, New Haven. 131 Kwast, M.L. and Rose, J.T., 1982. Pricing, operating efficiency, and profitability among large commercial banks. J. Banking Finance, 6: 233-254. [41 Rose, J.T. and Wolken, J.D., 1986. Statistical cost accounting models in banking: A reexamination and an application, Staff Economic Studies No. 150, Board of Governors of the Federal Reserve System, Washington DC. of the [51 Vasiliou, D., 1990. An empirical investigation profitability of Greek commercial banks, Unpublished Ph.D. Thesis, University of Kent at Canterbury, Canterbury. commercial Fl Kwast, M.L., 1981. New minority-owned banks: A statistical analysis, J. Bank Res., 12: 37-45. 171 Ford, W.F. and Olson, D.A., 1978. How 1000 high-performance banks weathered the recent recession. Banking, 70: 36-48. PI Kwast, M.L. and Black, H., 1983. An analysis of the behaviour of the mature black-owned commercial banks. J. Econom. Bus., 35: 41-54. 2nd ed. [91 Kmenta, J., 1986. Elements of Econometrics, Macmillan Publishing Co., New York. [lOI Cochrane, D. and Orcutt, G.H., 1949. Application of least squares regressions to relationships containing autocorrelated error terms. J. Amer. Statist. Assoc., 441 32-61. 011 Judge, G.G., Grifiths, W.E., Hill, R.C., Lutkepohl, H. and Lee, T., 1985. The Theory and Practice of Econometrics, 2nd ed. Wiley, New York. [121 Hildreth, C. and Lu, J.Y., 1960. Demand relations with autocorrelated disturbances. Michigan State University Agricultural Experiment Station Technical Bulletin 276, East Lansing, Michigan. 1131 Breusch, T.S. and Pagan, A.R., 1979. A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47: 1287-1294. covariv41 White, H., 1980. A heteroscedasticity-consistent ante matrix estimator and a direct test for heteroscedasticity. Econometrica, 48: 817-838. activities. in: [I51 Lewis, M.K., 1992. Off-the-balance-sheet Newman, P., Milgate, M. and Eatwell, J. (Eds.), The New Palgrave Dictionary of Money & Finance, Vol. 3, pp 67-72. The Macmillan Press Limited, London.