Share Performance and Profit Efficiency of Banks. in an Oligopolistic Market: Evidence from Singapore
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1 Share Performance and Profit Efficiency of Banks in an Oligopolistic Market: Evidence from Singapore Chu Sing Fat * and Lim Guan Hua Faculty of Business Administration National University of Singapore February 5, 1998 * Correspondence: Department of Decision Sciences, Faculty of Business Administration, National University of Singapore, 10 Kent Ridge Crescent, Singapore , SINGAPORE. Telephone: (65) , Fax: (65) , fbachucl@nus.sg 1
2 Share Performance and Profit Efficiency of Banks in an Oligopolistic Market: Evidence from Singapore Abstract Using the Data Envelopment Analysis (DEA) tool, we evaluate the cost and profit efficiencies of a panel of six Singapore listed banks during the period. Average cost and profit efficiencies were about 95% and 83%. In a follow-up regression study using efficiency scores comprehensively modified and ranked according to Andersen and Petersen (1993), we find that changes in the prices of the bank shares reflect changes in profit rather than cost efficiencies. This opens a new window for understanding share price fluctuations. Keywords : Banks, DEA, Cost efficiency, Profit efficiency, Oligopoly 2
3 Share Performance and Profit Efficiency of Banks in an Oligopolistic Market: Evidence from Singapore 1. Introduction The banking efficiency literature has been dominated to date by cost or X- efficiency studies in the competitive banking markets of Western Europe and North America (see for examples Ferrier and Lovell, 1990; Berg et al., 1993; the review on p of Berger et al., 1993a; and Resti, 1997). Berger et al. (1993b) appears to be the only published study on the issue of profit or P-efficiency. Studies on the stock markets, on the other hand, by and large found that stock prices do incorporate all relevant publicly known information. Efficiency scores are calculated based on published accounting numbers which are public information. Hence, an informationally efficient stock market would have taken such information into considerations in the price discovery process. In this paper, we intend to combine these two literature by linking bank efficiencies to the market place. Specifically, working within the Singapore domestic banking arena, we investigate the influence of X-efficiencies and P-efficiencies on the share prices of the banks. This exercise contributes, hopefully, an alternative perspective for explaining and understanding fluctuations in the banks share prices and stock market efficiency. At the end of 1996, there are a total of 220 banks operating in Singapore. Most of these banks hold restricted or offshore licences and deal primarily in the wholesale and offshore markets. Only 32 fully-licensed banks, 22 foreign and 12 local, offer the entire range of commercial banking services and cater to the domestic 3
4 banking market. Through their extensive branch networks, the 6 local banking groups command some 70% of the markets for deposits and loans. The six local banking groups are Tat Lee Bank (TLB), Keppel Bank (KB), Overseas Union Bank Group (OUB which also owns the International Bank of Singapore), United Overseas Bank Group (UOB which includes Chung Khiaw Bank, Far Eastern Bank, and Industrial and Commercial Bank), Overseas-Chinese Banking Corporation (OCBC which has in its stable the Bank of Singapore and Four Seas Bank), and DBS Bank (DBS). All six banks are publicly listed on the Singapore stock exchange. In terms of size (as measured by their average total assets in 1996), they ranged from S$9.13 billion to S$52.46 billion 1 (see row 1 of Table 1). The domestic banking market can be characterised as an oligopoly. Operationally, the banks offer similar prime rates, savings rates, and fixed deposit rates. UOB was the first mover in 57% of the cases of prime rate increases while OCBC and DBS together initiated about 90% of decreases in prime rates over the period January 1984 to June These changes in prime rates are, very often, followed up within days by the other banks. Hence, the competition in the market can be best described by the stackelberg model. The big three leads in initiating changes while the others follow closely behind. There are many reasons why we shift the focus of this paper towards the study of profit efficiency and away from cost efficiency. One, as the six banks deploy similar operational technologies, their cost structure may not differ 1 One Singapore dollar approximately equal to 0.70 US dollar 4
5 appreciably. Their X-efficiency scores would hence be similar. For instance, in a related study of Taiwanese banks during the pre-deregulation era, Yeh (1996) found that the average X-efficiency ranged between 0.82 and 0.95 for the six banks studied. Since Singapore is renown world-wide for its tight regulatory policies on its banking industry, we posit that the average X-efficiencies of the six local banks would be even higher and possibly less dispersed. Two, Berger et al. (1993b) conclude from their study that profit inefficiency appears to be a bigger problem at US banks than cost inefficiency. They also linked economies of scale to P-efficiency, a result which runs counter to most conclusions on X-efficiency. It is therefore of interest to investigate whether we can draw similar conclusions in an oligopolistic banking market like Singapore. Finally, we believe that shareholders would be more interested in P-efficiency rather than X-efficiency as profits serve as the source of current and future dividend income. This paper also investigates the existence of a statistical relationship between efficiencies and the market value or share prices of banks. This does not appear to have been discussed in the literature before. If such a statistical link can be established, it would afford an alternative explanation for share price fluctuations. This would enhance the relevance and value of research in bank efficiencies to investors, stock analysts and banking regulators. This rest of the paper is organised as follows. The next section lists our selection of banks inputs and outputs. It also describes the data and justifies the choice of the Data Envelopment Analysis (DEA) tool over its econometric 5
6 competitors for the evaluation of efficiency scores. Section 3 summarises the current state-of-the-art DEA techniques which we used in this paper. X-efficiency and P- efficiency scores are reported in Section 4. In section 5, we first modify several X- efficiency and P-efficiency scores to obtain an overall ranking of all the relative bankyear performances. We then regress annual changes in share prices on annual changes in the bank efficiencies. Section 6 concludes the paper. 2. Research Framework We adopt the perspective of Sealey and Lindley (1977) and view banks basically as financial intermediaries whose primary role is to obtain funds from savers in exchange for their own liabilities, and then, in turn make loans to others for a profit. That is, banks transform inputs such as labour, physical capital, deposits and financial capital into earning assets (see Figure 1). We define cost or X-efficiency as the relative efficiency of banks at minimising costs in the production of earning assets (Mester, 1996 and Resti, 1997). Profit or P-efficiency, on the other hand, is defined as the profit maximising or valued-added efficiency of banks. Notice that X-efficiency does not necessarily equate with P-efficiency as a bank may not improve its profit margin with a higher production of earning assets. Profits, unlike income, also takes into account expenses, taxes and the degree of financial leverage employed by the bank. insert Figure 1 here 6
7 Our choice of input and output variables is determined in part by the desire for a parsimonious model and in part by the smallness of our data set. The traditional economic factors of production are land, labour and capital. Labour and land are approximated by non-interest expense which encompasses wages, physical capital expenses as well as provisions for doubtful loans. Capital is provided either by the bank s shareholders or by its depositors. The latter is proxied by interest expenses. Interest expense is more relevant than volume of deposits as not all deposits (savings, fixed or time, and current or checking) carry the same interest expense (Mester, 1996). Interest and non-interest expenses have been used as inputs in bank efficiency studies by many researchers including Miller and Noulas (1996), Yeh (1996) and Resti (1997). The third input variable we use is the amount of financial capital provided by the bank s shareholders. This includes paid-up capital, capital reserves (share premium account and asset revaluation account), statutory reserves, and unappropriated earnings. Of the three inputs, this has been the least widely used input by researchers into banking efficiency. Examples of papers that used this input include Hughes and Mester (1993), McAllister and McManus (1993), Grabowski et al. (1994), and Mester (1996). Its importance cannot be under-estimated. Capital adequacy is a crucial parameter of solvency risks used by all bank regulators, and financial capital can be used as a substitute to deposits and purchased moneys to fund loans. Two outputs are considered. They are the annual increase in average total assets and either the total income from interest and non-interest activities (for X- 7
8 efficiency evaluation) or profits (for P-efficiency evaluation). In other words, X- efficiency and P-efficiency differ on the selection of either total income or net profit after taxation as the second output variable. We chose not to dis-aggregate total income into interest and non-interest categories since an inherent objective of this paper is to compare X-efficiency and P-efficiency scores. For fairness, these two scores should be based as far as possible on the same input and outputs. This is also in line with the suggestion made by Berger et al. (1993a, p 231) for a comparison between a profit and a cost function model estimated using the same data set. The annual increase in average assets does not appear to have been employed before. It may be regarded as a proxy for future income or future profit. Our choice of outputs brings a novelty to the literature as they represent both the present and the future earnings. This is important to shareholders as the bank can always sacrifices long term profits/income for short term gains, and the bank s long term profit potential definitely affects its current share price. The test period selected is from 1992 to is chosen as the starting date as it was only then that the six local banks started to provide a breakdown of their income and expenses into interest and non-interest categories. The latest reporting year for all six banks is All 6 banks have December 31 as their financial year end. We work with annual data simply because the accounting information required are only available on an yearly (not quarterly basis as in the U.S.) basis. Thus, our data set consists of 30 bank-year combinations. 8
9 The small size of the data set forces us to opt for Data Envelopment Analysis (DEA) as the tool to evaluate X-efficiency and P-efficiency. Alternative methods based on the econometric frontier (Ferrier and Lovell, 1990; Pi and Timme, 1993; and Mester, 1996), thick frontier (Berger and Humphrey, 1991) or distribution-free (Berger et al., 1993b) approaches only achieve stable results with large data sets, a luxury which we do not have. The potential of data error, often mentioned as a shortcoming of DEA (Mester, 1996), is minimised here as we employ only audited figures culled straight from the respective banks annual reports. End-of-the-year stock prices, duly adjusted for capitalisation changes, are obtained from the financial data bases maintained at the National University of Singapore and from the January 1997 issue of the monthly SES Journal for the 1996 numbers. The next section summarises the current state-of-the-art for the technical aspects of DEA that will be required in this paper. 3. DEA Technology: The State-of-the-Art DEA was developed to measure the relative productive efficiencies of similar multi-input multi-output Decision Making Units (DMUs). DEA evaluates the maximum ratio of a weighted sum of outputs to a weighted sum of inputs for each DMU subject to the constraint that the ratio does not exceed one for any DMU. Best practice DMUs are assigned a maximum ratio score of one. Together, they define the relative efficiency frontier. 9
10 Charnes et al. (1978) demonstrate that this ratio or fractional linear programming problem may be transformed into a standard linear programming problem upon either setting the denominator (that is, weighted sum of inputs) or the numerator (that is, weighted sum of outputs) to a constant. Thus, DEA may be cast in either an output-maximising (for example, profit maximisation) or an inputminimising (cost minimisation, for example) role. The linear program for computing P-efficiencies is given below. P-efficiency 0 = Max φ (1) n s.t. φ Y 0 - Y j j + S + = 0 n λ j= 1 λ X j - X 0 + S = 0 j j= 1 λ, j S+, S 0 In the above formulation, subscript 0 refers to the current DMU (or bank-year combination) being evaluated. The variables X j and Y j respectively denote the vectors of inputs and outputs for DMU j (j = 1,....., n; n = 30). The weights λ j indicate the influence of the best practice DMUs in benchmarking the efficiency of the current DMU. Finally, S and S + represent respectively, the input and output slacks vectors. These are useful for providing diagnostic feedback to management of inefficient banks. They indicate exactly where improvements should be made to achieve greater efficiency. This paper, as mentioned earlier, focuses on DEA in a profit maximisation role. If constant returns of scale (CRS) is assumed, DEA delivers an overall technical 10
11 efficiency (OTE) or P-efficiency score for each DMU. Banker et al. (1984) assume variable returns to scale (VRS) and show that the efficiency measure described by Charnes et al. (1978) can be decomposed into a product of two components, namely, pure technical efficiency and scale efficiency. These two components are denoted PTPE and SPE to highlight their link to P-efficiency in this paper. Pure technical efficiency measures how efficiently the bank is in the utilisation of resources while scale efficiency ask whether the bank is operating at the right scale of operation or size. The linear program for computing PTPEs is provided below (see Miller and Noulas, 1996 (p ) for details on the computation of X-efficiency, pure technical X-efficiency or PTXE and scale X-efficiency or SXE). PTPE 0 = Max ν (2) n s.t. νy 0 - Y j j + S + = 0 n λ j= 1 λ X j - X 0 + S = 0 = 1 j j= 1 n j j= 1 λ λ, j S+, S 0 Notice that the linear programs (1) and (2) differ only in the convexity constraint, n λ j= 1 j = 1, implied by VRS that is imposed on (2). To get a intuitive feel of these linear programs, we will now provide a graphical illustration. Figure 2 illustrates the computation of P-efficiency, PTPE and SPE in the special situation of one input (for example, shareholders fund) and one output (for 11
12 example, increase in total assets). In 1993, Keppel Bank (KB93) scored the highest ratio of increase in assets to shareholders fund. Thus its P-efficiency is one while the P-efficiencies of the other DMUs are less than one. Under the VRS assumption, the relative efficiency frontier is constructed around the convex combination KB92, KB93, and OUB96. These three bank-year combinations are (see Figure 2) assigned unitary PTPE while the other DMUs attract PTPEs less than one. Scale efficiency is computed as P-efficiency divided by PTPE. For example, the P-efficiency, PTPE and SPE of OUB94 correspond geometrically to the ratios AB/AD, AC/AD and AB/AC respectively. insert Figure 2 here The nature of the scale inefficiency can be uncovered using the conclusion of Zhu and Shen (1996). They show that a DMU exhibits decreasing (increasing) returns to scale when n λ j= 1 j exceeds (does not exceed) one upon the computation of its OTE. This information may suggests a possible future managerial course of action for the DMU involved, namely, whether it should contract or expand its scale of operations in the quest for greater efficiency. A serious shortcoming of DEA is that it may find a disproportionately large number of technically efficient DMUs. This is especially so if the total number of inputs and outputs is high relative to the number of DMUs. To remedy this situation, Andersen and Petersen (1993) propose the ranking of frontier DMUs by comparing them to a reference technology spanned by their frontier peers. This 12
13 means that in the constraints to the linear program formulations (1) and (2) above, the summation sign (running from 1 to n) should exclude the current DMU. With this modification, frontier DMUs may attract an efficiency score above one thereby allowing an overall ranking of all the n DMUs. This enhances the suitability of the modified efficiency scores for follow-up parametric linkages such as regression analyses. 4. X-efficiencies and P-efficiencies of Singapore Banks The literature abounds with empirical results on banks X-efficiencies. In their review paper, Berger et al. (1993a) report that authors studying US banks found average X-efficiencies in the 50 to 90 plus percentage or 75 to 80 percentage ranges depending on whether they employ DEA or econometric models. In a recent study on 270 Italian banks during the period, Resti (1997) concludes that Italian banks have average X-efficiencies of about 70% under both the DEA and econometric models. As a bridge to these results, we now report the X-efficiencies of Singapore banks. We find the average X-efficiency of the six Singapore listed banks for the period to be a remarkable 95.3 % (see Table 1). As expected, these scores are higher and less dispersed than those obtained in the Taiwanese market by Yeh (1996). The overall mean X-efficiency of Singapore banks is with a standard deviation of 0.06 compared to a mean X-efficiency of 0.9 with a standard deviation of 0.09 for Taiwanese banks (Yeh, 1996). Interestingly also, the average X-inefficiency of 13
14 Singapore listed banks is a mere 4.7%, lower than the just over 5 percent (p 507) reported in Miller and Noulas s (1996) study on US banks. The larger banks appeared to exhibit slightly higher X-efficiencies than the smaller banks. This is similar to earlier empirical findings from the US market (see for examples, Berger et al., 1993b; and Miller and Noulas, 1996). For example, Miller and Noulas (1996) found, also using DEA, from their study of 201 large US banks (defined as those with assets in excess of US$1b) between 1984 and 1990 that the larger banks displayed less X- inefficiencies. As a group, UOB, OCBC and DBS has an average X-efficiency score of 0.99 compared to the 0.92 average obtained by the other three banks. Furthermore, the smallest bank, TLB, has the lowest average X-efficiency score of insert Table 1 here Unlike Miller and Noulas (1996) who found average pure technical inefficiency twice the size of scale inefficiency, the six banks in our sample do not appear to have very different PTXE and SXE scores (see Table 1). The average PTXE is in fact comparable to the average SXE (0.978 versus 0.974). However, if we exclude the two smallest banks (TLB and KB who have average scale inefficiencies some 50% higher than their average pure technical inefficiencies), the other four banks displayed similar results. Though small, their average pure technical inefficiency nonetheless is still about doubled that of their average scale inefficiency. The average P-efficiency of the six Singapore banks is however, much (significant at the 5% level) lower at about 82.6% (see row 3 of Table 1). On average, scale P-efficiency is 95%, a number comparable to the 97% for SXE, and pure 14
15 technical P-efficiency is 87%. Thus, it appears that, P-inefficiency arises more from the inefficient production of profits rather than from the wrong scale of the operations. This conclusion concurs with that reached by Berger et al. (1993b). One possible explanation for the lower average P-efficiency compared to average X-efficiency could be the low degrees of financial leverage used by Singapore banks which are very conservative by nature. Singapore banks have routinely ranked among the world s strongest banks in terms of Tier 1 capital ratio (as defined by the Bank of International Settlements). Furthermore, the six banks (especially TLB and KB) have been raising additional financial capital over this period to meet new statutory requirements on minimum shareholders equity (S$800 million by 1998 and subsequently amended in 1996 to S$1.5 billion by 2001). Larger banks were once again more P-efficient than the smaller ones. There are several possible explanations for this result. Larger banks attract more deposits and loans transactions and they may actually command larger interest spreads. Larger banks offer more services. In the process, they derive substantial non-interest income from commissions, fees and treasury activities. For example, DBS Bank is the leading manager and underwriter of initial public offerings in Singapore. Furthermore, in an oligopolistic market, size and hence, economies of scale also equates to market power and the ability to set rates. Evidently, this market power has been translated into higher profits for the larger banks. Notice that while UOB obtains perfect X-efficiency scores for all the five years between 1992 and 1996, it ranks second to DBS in terms of average P-efficiency 15
16 over the same period (0.946 versus 0.966). The difference in average P-efficiency between the smaller banks (namely, TLB, KB and OUB) and the larger banks is even wider than that for average X-efficiency (0.71 versus 0.94 compared to 0.92 versus 0.99). Furthermore, using the result of Zhu and Shen (1995), we also find that the smaller P-inefficient banks generally exhibited decreasing returns to scale in the generation of current and future profits. This suggested that they may be sacrificing their interest margins in order to maintain or improve their market share. Again, we believe that this is a manifestation of the oligopolistic nature of the Singapore domestic banking market. This result is in contrast to U.S. studies into X-efficiency of banks (see for examples Hunter et al., 1990; Noulas et al., 1990; and Berger and Humphrey, 1991). The general conclusion there is that smaller banks experience increasing returns. The evidence on larger banks is, at best, constant returns to scale (McAllister and McManus) and at worst, decreasing returns to scale. 5. Efficiencies and Share Prices In this section, we investigate the influence of X-efficiencies or P-efficiencies on the share prices of the banks. This exercise appears to be a first in the banking efficiency literature. We first employed the method suggested by Andersen and Petersen (1993) to modify all the X-efficiency and P-efficiencies scores of obtained in the original run of DEA. The modified scores were all in excess of one (see 16
17 Table 2). Their inherent ranking should in theory enhance the statistical fit for this second-stage parametric exercise. insert Table 2 here Current regulations in Singapore allow foreigners collectively to own at most 40% of a bank s share capital. Tat Lee and Keppel Bank, the two smallest banks, have yet to reach this threshold foreign ownership percentage. The four larger banks, on the other hand, have all segregated their shares into local and foreign tranches in order to satisfy this legal requirement. Whereas foreigners are constrained to dealing in foreign shares, Singapore residents or institutions are allowed to invest in both local and foreign shares. Active trading in foreign shares has led them to command premiums as high as 100% over the local shares. For these four banks, we computed their share prices as the weighted averages of 0.6 times their local share prices plus 0.4 times their foreign share prices. We first regress changes in the annual closing share prices (adjusted for capitalisation changes) on changes in the annual X-efficiencies. KB92 and KB93 are excluded as Keppel Bank only got listed on the Singapore bourse in The correlation is a significant (at the 10% level) Using P-efficiencies in lieu of X- efficiencies in the regression improves the regression R-square to The correlation between changes in share prices and changes in P-efficiencies is a statistically significant However, residual analysis indicates a significant overreaction phenomenon. The market over-reacts to changes in P-efficiencies. 17
18 Improvements in P-efficiency caused a bull run, leading to high prices while falls in P- efficiency scores led to massive sell-offs sending prices tumbling down. We find that the over-reaction problem could be attenuated upon using changes in the annual moving averages of both the modified P-efficiencies and the share prices. Data smoothing mitigates the excessive reactions of investors to year-on-year changes in profit efficiencies. The scatter plot is displayed in Figure 3. The regression R-square based on the smoothed data improves to The slope coefficient is with a standard deviation of Statistically, this implies the slope may not be different from one. This has the interesting implication that changes in smoothed P-efficiencies are associated with equal changes in smoothed share prices. A similar implication also surfaces in the original regression based on unsmoothed data. insert Figure 3 here If moving averages of X-efficiencies are used instead of moving averages of P- efficiencies, the R-square would have been This leads us to conclude that share prices reflect P-efficiencies to a larger extent than X-efficiencies. This is to be expected as shareholders desire dividends which are paid out of profits and not income. It also supports the hypothesis that the stock market is indeed price efficient in the semi-strong form sense. 6. Conclusions 18
19 This paper makes a number of contributions to the literature. One, it studies cost and profit efficiencies of banks in an Asian (Singapore) market which also happens to be an oligopolistic/stackelberg competition type market. It fills a void in the literature which has to date concentrate more on Western European and North American markets where banks operate in a competitive banking environment. Hopefully, our results will be of relevance to other oligopolistic banking markets around the world. Using DEA techniques with three inputs and two outputs, we find that Singapore banks have higher average X-efficiency scores and there is less dispersion in these scores compared to Taiwanese banks and those in Western Europe and North America. There is also not much difference between average pure technical X- efficiency and average scale X-efficiency. We further highlights the relevance and value of the concept of P-efficiency. The slightly lower average P-efficiency scores are found to be caused more by the inefficient utilisation of resources rather than by the wrong scale of operations. Furthermore, the larger banks do have better X- and P- efficiency scores than the smaller banks. We also ties the literature on banking efficiency to the vast body of empirical findings on the pricing efficiency of the stock market. We showed that changes in share prices reflect changes in profit rather than cost efficiencies. Finally, the market tends to over-react to year-on-year changes in P-efficiency. This offers an alternative explanation for fluctuations in share prices. 19
20 7. References Andersen, P. and N. C. Petersen, 1993, A procedure for ranking efficient units in data envelopment analysis, Management Science 39, Banker, R. D. and A. Charnes and W. W. Cooper, 1984, Models for the estimation of technical and scale efficiencies in data envelopment analysis, Management Science 30, Berg, S. A., F. R. Forsund, L. Hjalmarsson, and M. Suominen, 1993, Banking efficiency in the Nordic countries, Journal of Banking and Finance 17, Berger, A. N. and D. D. Humphrey, 1991, The dominance of inefficiencies over scale and product mix economies in banking, Journal of Monetary Economics 28, Berger, A. N., W. C. Hunter and S. G. Timme, 1993a, The efficiency of financial institutions: A review and preview of research past, present and future, Journal of Banking and Finance 17, Berger, A. N., D. Hancock and D. D. Humphrey, 1993b, Bank efficiency derived from the profit function, Journal of Banking and Finance 17, Charnes, A., W. W. Cooper and E. Rhodes, 1978, Measuring the efficiency of decision making units, European Journal of Operations Research 2, Ferrier, G. D. and C. A. K. Lovell, 1990, Measuring cost efficiency in banking: Econometric and linear programming evidence, Journal of Econometrics 46, Grabowski, R., N. Rangan, and R. Rezvanian, 1994, The effects of deregulation on the efficiency of U.S. banking firms, Journal of Economics and Business 46, Hughes, J. P. and L. J. Mester, 1993, A quality and risk-adjusted cost function for banks: Evidence on the too-big-to-fail doctrine, Journal of Productivity Analysis 4, Hunter, W. C., S. G. Timme and W. K. Yang, 1990, An examination of cost subadditivity and multiproduct production in large U.S. banks, Journal of Money, Credit, and Banking 22, McAllister, P. H. and D. McManus, 1993, Resolving the scale efficiency puzzle in banking, Journal of Banking and Finance 17,
21 Mester, L. J., 1996, A study of bank efficiency taking into account risk-preferences, Journal of Banking and Finance 20, Miller, S. M. and A. G. Noulas, 1996, The technical efficiency of large bank production, Journal of Banking and Finance 20, Noulas, A. G., S. C. Ray and S. M. Miller, 1990, Returns to scale and input substitution for large U.S. banks, Journal of Money, Credit, and Banking 22, Pi, L. and S. G. Timme, 1993, Corporate control and bank efficiency, Journal of Banking and Finance 17, Resti, A., 1997, Evaluating the cost-efficiency of the Italian banking system: What can be learned from the joint application of parametric and non-parametric techniques, Journal of Banking and Finance 21, Sealey, C. W. and J. T. Lindley, 1977, Inputs, outputs and theory of production cost at depository financial institutions, Journal of Finance 32, Yeh, Q. J., 1996, The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation, Journal of the Operational Research Society 47, Zhu, J. and Z. H. Shen, 1995, A discussion on testing DMUs returns to scale. European Journal of Operational Research 81,
22 Table 1 Size and Average Efficiencies of the Six Singapore Local Banks over the period TLB KB OUB UOB OCBC DBS Overall Size (S$b) X-efficiency PTXE SXE P-efficiency PTPE SPE Size refers to the simple average of the size of the bank s total assets (excluding contra items) on December 31, 1995 and that on December 31, X-efficiency and P- efficiency refer to cost efficiency and profit efficiency respectively. PTXE = pure technical X-efficiency while PTPE is pure technical P-efficiency. SXE and SPE refer respectively to scale X- and scale P-efficiencies. The six banks are Tat Lee (TLB), Keppel (KB), Overseas Union Bank (OUB), United Overseas Bank (UOB), Overseas- Chinese Banking Corporation (OCBC) and DBS Bank (DBS). 22
23 Table 2 Modified X-efficiency (first row) and P-efficiency (second row)* Year TLB KB OUB UOB OCBC DBS * These are modified according to Andersen and Petersen (1993). Only original X- efficiency and P-efficiency scores equal to are modified. The six banks are Tat Lee (TLB), Keppel (KB), Overseas Union Bank (OUB), United Overseas Bank (UOB), Overseas-Chinese Banking Corporation (OCBC) and DBS Bank (DBS). 23
Share performance and profit efficiency of banks in an oligopolistic market: evidence from Singapore
Journal of Multinational Financial Management 8 (1998) 155 168 Share performance and profit efficiency of banks in an oligopolistic market: evidence from Singapore Sing Fat Chu, Guan Hua Lim * Graduate
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