Microsoft Word - 11073-new Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 310 A Model for Bank Performance Measurement Integrating Multivariate Factor Structure with Multi-Criteria PROMETHEE Methodology Mihir Dash Head of Department, Department of Quantitative Methods School of Business, Alliance University Chikkahagade Cross, Anekal, Bangalore, India-562106 Tel: 91-99-518 2465 E-mail: mihirda@rediffmail.com Received: April 16, 2017 Accepted: May 24, 2017 Published: June 1, 2017 doi:10.5296/ajfa.v9i1.11073 URL: https://doi.org/10.5296/ajfa.v9i1.11073 Abstract The global financial crisis and the subsequent Euro-zone crises have resulted in widespread failure of banking systems worldwide. The Indian banking system, which was initially hailed to be unaffected by the crises, was affected indirectly, mainly on account of growing trade and financial integration with the global economy. Although Indian banks were not pushed to the point of insolvency, bank performance benchmarking and evaluation have become important in the dynamic banking environment in India in order to ensure sustained profitability and avoid undue risks. The CAMELS model is one of the most widely-used frameworks for bank performance evaluation (Sahajwala and van der Bergh, 2000). The CAMELS methodology provides a broader view of bank performance than single ratios such as return on equity, particularly as it takes account of both profitability and risk factors in representing bank performance. Several studies have proposed multi-criteria decision models for bank performance measurement (Doumpos and Zopounidis, 2011). The objective of the present study is to integrate multivariate and multi-criteria decision models in bank performance measurement. The study uses the factor structure of the CAMELS model to derive weights for the different criteria in the PROMETHEE methodology. The resulting PROMETHEE scores are used to rank banks under different dimensions, and to compare the performance of public sector and private sector banks in India. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 311 Keywords: bank performance measurement, CAMELS model, factor structure, PROMETHEE methodology JEL Classification: G20 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 312 Introduction Bank performance evaluation has gained greatly in importance in recent years. The global financial crisis and the subsequent Euro-zone crises have resulted in widespread failure of banking systems worldwide. The collapse of some of the most prominent banks in the world, including the Lehman Brothers and Washington Mutual Bank, along with several near-failures which had to be bailed out of crisis by the U.S. Government, highlighted the inadequacy of bank evaluation systems in detecting/predicting bank insolvency. The Indian banking system, which was initially hailed to be unaffected by the crises, was affected indirectly, mainly on account of growing trade and financial integration with the global economy. Though Indian banks were not pushed to the point of insolvency, bank performance benchmarking and evaluation have become important in the dynamic banking environment in India in order to ensure sustained profitability and avoid undue risks. There are several systems used for bank performance evaluation. The CAMELS model is one of the most widely-used frameworks for bank performance evaluation (Sahajwala and van der Bergh, 2000). Originally, the CAMEL framework was used by regulators in the U.S. to determine when to conduct on-site examination of a bank; it is still used by regulators to evaluate bank performance. The five CAMEL parameters, viz. Capital Adequacy, Asset Quality, Management Soundness, Earnings and Profitability, and Liquidity, are critical for the survival of banks - inadequacy in any parameter would result in increased likelihood of bank failure. The sixth parameter, Sensitivity to Market Risk, was added to these former parameters in order to make this method more comprehensive. The present study attempts to integrate two approaches in bank performance measurement: multivariate methods and multi-criteria decision models. The multivariate approach examines the dimensionality of the CAMELS system. The multi-criteria decision modeling approach focuses on ranking the banks according to the dimensions inherent in the CAMELS system. Further, the factor structure of the CAMELS model is used to derive weights for the different criteria in the multi-criteria decision model. The resulting scores are used to rank banks under different dimensions, and compare the performance of public sector and private sector banks in India. Literature Review There is extensive literature addressing banking performance evaluation. The CAMELS framework in particular is a widely-used methodology for bank performance assessment, using particular financial ratios to reflect different aspects of a bank’s performance (Sahajwala and van der Bergh, 2000). Barr et al (2002) found that the CAMEL ratings were consistent with the efficiency scores obtained through Data Envelopment Analysis. Beaver (1966, 1968) and Altman (1968) initiated the use of financial ratios for bankruptcy prediction. Maishanu (2004) extended Altman’s z-score model for banks, suggesting eight financial ratios to assess the financial health of a bank. Mous (2005) applied decision tree models and multiple discriminant models for bankruptcy prediction in banks, using profitability, liquidity, leverage, and turnover ratios, and suggested that the decision tree Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 313 approach performed better than the discriminant model approach. Dash and Das (2013) compared the performance of public sector banks with private/foreign banks under the CAMELS framework. They found that private/foreign banks fared better than public sector banks on most of the CAMELS factors in the study period, and that the two contributing factors for the better performance of private/foreign banks were Management Soundness and Earnings and Profitability. Njoku (2011) studied the factor structure of CAMEL in order to develop an anatomic model of bank performance, using factor weights. The anatomy framework modelled a bank’s financial situation in seven structural parameters, including market presence, macro-economic condition, deposit mobilisation, prudence, earnings quality, market power and capital confidence. Njoku and Inanga (2012) applied the anatomic model in interpreting critical issues commonly reported in the 2008-2009 global banking crises. Several studies have applied factor analysis to develop rating methods for life insurance service providers. Hsiao (2006, 2008) developed the CAMEL-S model based on fourteen financial variables and reported its consistency with DEA efficiency scores. Yakob et al (2012) applied factor analysis to a set of twenty-three financial ratios to develop a CAMEL model for rating life insurance service providers. Klomp and de Haan (2011) applied dynamic factor analysis with a set of twenty-five financial variables under the CAMELS framework in order to construct measures for bank risk. Popovska (2014) applied factor analysis to the six CAMELS dimensions in order to develop a measure for bank stability. Maliszewski (2009) and Bhattacharyay (2011) had also proposed such a measure. Several multi-criteria decision methods have been applied widely in banking performance measurement. Some of the most commonly-applied techniques include Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), ELimination Et Choix Traduisant la REalité (ELECTRE), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Preference Ranking Organization METHod for Enrichment of Evaluation (PROMETHEE). Some of the literature closely linked with the present study is reviewed in the following. Several studies have applied DEA models to measure bank efficiency (Parkan & Liu, 1999; Halkos & Salamouris, 2004; Kao & Liu, 2004; Avkiran, 2010; Fallah et al, 2011; Dash and Charles, 2012; Minh et al, 2013; Doumpos and Zopounidis, 2013; Dash and Vegesna, 2014). Hunjak and Jakovcevic (2001) proposed a methodology for bank performance measurement based on multi-criteria AHP, enabling the consideration of both quantitative factors (viz. financial ratios) and qualitative factors (internal and external) in the evaluation process. They applied their model in the context of Croatian banks. Seçme et al (2009) proposed a fuzzy AHP model for the banking system using both financial and non-financial performance criteria. Stankeviciene and Mencaite (2012) used the AHP model to evaluate the performance of Lithuanian commercial banks. They used a system of indicators and assigned each indicator a different weight reflecting its significance based on the needs and priorities of Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 314 both internal and external evaluators. Cetin & Cetin (2010) used multi-criteria VIKOR to rate Turkish banks according to their overall financial performance. Rezaei and Gheibdoust (2014) used VIKOR to rank banks based on capital adequacy, asset quality, liquidity, structure of capital expenditures and profitability. Several studies have used the PROMETHEE methodology for measuring bank performance (Mareschal & Brans, 1991; Mareschal & Mertens, 1992; Babic et al, 1999; Kosmidou & Zopounidis, 2008; Doumpos & Zopounidis, 2011; Ginevičius and Podviezko, 2013). Bayyurt (2013) compared the performance of the foreign and domestic deposit banks in Turkey using several MCDM methods, viz. DEA, TOPSIS, and ELECTRE III, using the Mann-Whitney U-test and the independent samples t-test. The results of the study showed that foreign-owned banks performed better than domestic banks, as foreign banks could find cheaper international funds, and domestic banks had more employees than foreign banks for similar banking functions, resulting in lower employee productivity. Önder and Hepşen (2013) proposed a performance evaluation model for Turkish banks using time series forecasting methods and multi-criteria AHP and TOPSIS methodology. They applied the model under ten performance categories as prescribed by the Bank Association of Turkey: capital ratios, balance sheet ratios, assets quality, liquidity, profitability, income-expenditure structure, share in sector, share in group, branch ratios, and activity ratios. Several other methods have also been applied, including disaggregation techniques (Zopounidis et al, 1995; Spathis et al, 2002), co-plot method (Raveh, 2000), grey relational analysis (Ho, 2006), classification techniques (Ioannidis et al, 2010), balanced scorecard approach (Wua et al, 2009), COPRAS (Ginevičius and Podviezko, 2013), and several others. Rosenzweig et al (2013) used a goal programming model for business strategies of commercial banks. The criteria for the model were profitability, security/risk and liquidity. The indicators were aggregated into a score which reduced all the relevant information about bank operations into an index using which the banks can be compared and ranked. Thus, several studies have used factor analysis to develop composite measures of bank performance and risk, particularly in the context of the CAMELS model, and several studies have employed multi-criteria decision models in bank performance measurement. The present study examines the factor structure of the CAMELS model in bank performance in India. Data and Methodology The objective of the present study is to integrate multivariate and multi-criteria decision models in bank performance measurement. The study uses the factor structure of the CAMELS model to derive weights for the different criteria in the PROMETHEE methodology. The resulting PROMETHEE scores are used to rank banks under different dimensions, and compare the performance of public sector and private sector banks in India. The variables used in the analysis pertain to the financial ratios corresponding to the CAMELS parameters. These are discussed in the following (refer Dash and Das, 2013). Capital Adequacy represents the capacity of a bank in terms of sufficient capital to absorb unexpected losses. It is required in order to maintain depositors’ confidence and to prevent Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 315 the bank from going insolvent. In the current study, it is measured with the help of three ratios: the Debt- Equity ratio, the Coverage ratio, and the Capital Adequacy ratio. Asset Quality represents the nature of loans and advances the bank has made to generate interest income. Highly-rated companies generally tend to be given lower interest rate terms than lower-rated, doubtful companies. Thus asset quality reflects the type of debtors of the bank. The ratio used to capture this parameter in this study is Net NPA to Total Advances ratio. Management Soundness is the parameter used to evaluate management quality, assigning premium to better-managed banks and discounting poorly-managed banks. It involves analysis of efficiency of management in generating business (top-line) and in maximizing profits (bottom-line). In this study, it is measured through four ratios, viz. Total Investments to Total Assets ratio, Total Advances to Total Deposits ratio, Business per Employee, and Profit per Employee. Earnings Performance emphasises on how a bank earns its profits. This in turn explains the sustainability and growth in earnings in the future. In this study, it is measured via three ratios, namely Return on Net Worth, Interest Spread to Total Assets ratio, and Profit after Tax to Total Assets. Liquidity position is of prime importance in the banking business. In the study, it is measured using two ratios: Government Securities to Total Investment and Government Securities to Total Assets. Sensitivity to Market Risk considers the ability of a bank to identify, measure, monitor, and control market risk. In the study, it is measured by Beta, i.e. the systematic risk of the bank’s stock returns. The data for the study pertained to a sample of thirty-five banks operating in India, of which nineteen were public sector banks, and sixteen were private sector banks, listed in Table 1 below. The research period for the study was 2007-2011. The data for the study consists of financial ratios based on the CAMELS framework described above, obtained from the Capitaline database1. 1 www.capitaline.com Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 316 Table 1. list of sample banks public sector banks private sector banks 1 Allahabad Bank 1 Axis Bank 2 Andhra Bank 2 YES Bank 3 Bank of Baroda 3 Standard Chartered 4 Bank of India 4 South Indian Bank 5 Canara Bank 5 Kotak Mahindra 6 Corporation Bank 6 HDFC Bank 7 Central Bank of India 7 Federal Bank 8 Dena Bank 8 Dhanalaxmi Bank 9 Indian Overseas Bank 9 Development Credit Bank 10 Indian Bank 10 Karnataka Bank 11 Oriental Bank of Commerce 11 J &K Bank 12 Punjab National Bank 12 ING Vysya 13 State Bank of India 13 Bank of Rajasthan 14 IDBI Bank 14 Citi Bank 15 Syndicate Bank 15 Tamilnad Mercantile Bank 16 UCO Bank 16 ICICI Bank 17 Union Bank of India 18 United Bank 19 Vijaya Bank The CAMELS variables, averaged across the five-year period, were taken for the factor analysis, and the subsequent factor weights were used in conjunction with a multi-criteria procedure, PROMETHEE II (Doumpos and Zopounidis, 2011). Following the PROMETHEE methodology, the partial preference indices were computed using the linear function , = 0 ≤ 0 ≤ − ≤ 1 − > , where the preference threshold pk was taken to be equal to one standard deviation of the underlying variable. The partial evaluation scores were computed as ( ) = ∑ ( , ) and ( ) = ∑ ( , ) , with ( ) = ( ) − ( ), and the final PROMETHEE score was computed as Φ( ) = ∑ ( ). The PROMETHEE scores computed as above were used to identify the good performers and the bad performers. Consistency of the PROMETHEE scores was analysed using Spearman rank correlation. Also, the PROMETHEE scores were used to compare the performance of public sector and private sector banks using the non-parametric Mann-Whitney U-test. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 317 Findings The descriptive statistics of the CAMELS parameters is presented in Table 2 below. Table 2. Descriptive Statistics of CAMELS parameters public sector private sector mean std dev mean std dev Debt/Equity ratio 18.8253 3.3498 13.4292 3.4603 Coverage ratio 4.262% 1.240% 7.312% 2.150% Capital Adequacy ratio 12.232% 0.857% 13.615% 2.303% Net NPA/Total Advances 0.921% 0.436% 0.938% 0.516% Total Investment/Total Assets 27.217% 2.186% 29.392% 4.123% Total Advances/Total Deposits 72.007% 8.334% 72.948% 11.279% Business per Employee 8.1363 3.3084 7.4585 3.0529 Profit per Employee 0.0507 0.0188 0.0703 0.0615 Return on Net Worth 19.474% 3.954% 14.800% 5.052% Interest Spread/Total Assets 0.372% 0.081% 0.578% 0.315% PAT/Total Assets 0.858% 0.254% 1.005% 0.550% Govt Sec/Total Investment 1.195% 0.108% 0.754% 0.092% Govt sec/Total Asset 0.324% 0.026% 0.218% 0.025% Beta 1.1637 0.2266 0.8538 0.5905 The average CAR was well above the Basel II required level of 9%, and within the Basel III required level of 11%-13.5%2. Asset quality was generally stable across the research period, with the Net NPA ratio controlled to below 1%, significantly lower than its 2004 levels (about 7%). There was also a marked improvement in Management Soundness, especially in Business per Employee and Profit per Employee. However, Earnings Performance was relatively stable, especially Profit after Tax to Total Assets at around 1%, with some improvement in Return on Net Worth and Interest Spread in 2011. There was a decrease in Liquidity, with respect to Government Securities to both Total Investments and Total Assets. Sensitivity to Market Risk was also generally stable, with the average beta at around 1. The results of the factor analysis are presented in Table 3 below. 2 http://en.wikipedia.org/wiki/Basel_II Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 318 Table 3. Factor Analysis: Rotated Component Matrix Components F1 F2 F3 F4 F5 Debt/Equity ratio 0.686 Coverage ratio 0.688 Capital Adequacy ratio 0.768 Net NPA/Total Advances 0.738 Total Investment/Total Assets 0.876 Total Advances/Total Deposits 0.846 Business per Employee 0.899 Profit per Employee 0.888 Return on Net Worth 0.930 Interest Spread/Total Assets 0.560 PAT/Total Assets 0.626 Govt Sec/Total Investment 0.957 Govt sec/Total Asset 0.941 Beta 0.734 %age of variance explained 26.03% 16.88% 15.22% 14.61% 13.73% Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. K.M.O. Measure of Sampling Adequacy: 0.652 Bartlett’s Test of Sphericity: Chi-sq = 544.99, p = 0.000** Total Variance Explained: 86.46% The K.M.O. measure of sampling adequacy was moderate, and Bartlett’s test was statistically significant, suggesting multi-collinearity of the variables. The results of the factor analysis identified five underlying factors, together explaining 86.46% of the overall variation in the variables. The first factor (F1) loaded highly on four variables, viz. Total Investments to Total Assets, Total Advances to Total Deposits, Business per Employee, and Profit per Employee. Thus, this factor captures the Management Soundness dimension, and explains the maximum percentage of the overall variation in the variables. The second factor (F2) loaded highly on Government Securities to Total Investments and Government Securities to Total Assets. Thus, this factor reflects the Liquidity dimension. The third factor (F3) loaded highly on the Coverage ratio, the CAR, and the Interest Spread to Total Assets ratio. The first two variables relate to Capital Adequacy, while the third relates to Earnings Performance. This suggests that the Capital Adequacy of banks should also be measured in light of the Interest Spread to Total Assets ratio, as they are closely correlated. The fourth factor (F4) combined three critical ratios. They were the Debt-Equity ratio, the Net NPA to Total Advances ratio, and Beta. The Debt-Equity ratio reflects Capital Adequacy, the Net NPA ratio indicates Asset Quality of banks, and Beta represents Sensitivity to Market Risk. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 319 Thus, although these variables represent different parameters of the CAMELS framework, yet they are closely correlated. In fact, they all measure different types of risk; the Debt-Equity ratio measures financial risk, Net NPA ratio measures exposure to credit risk, and Beta measures systematic risk. Thus, the fourth factor may be interpreted as the Risk factor. Lastly, the fifth factor (F5) loaded highly on Return on Net Worth and PAT to Total Assets ratio. Thus, this factor reflects the Earnings Performance dimension. Based on the identified factors and their corresponding factor coefficients, the following weights were derived (insignificant coefficients were dropped). Management Soundness Total Investment/Total Assets 0.2496 Total Advances/Total Deposits 0.2411 Business per Employee 0.2562 Profit per Employee 0.2531 Liquidity Govt Sec/Total Investment 0.5042 Govt Sec/Total Asset 0.4958 Capital Adequacy Coverage ratio 0.3413 Capital Adequacy ratio 0.3810 Interest Spread/Total Assets 0.2778 Risk Debt/Equity ratio 0.3179 Net NPA/Total Advances 0.3420 Beta 0.3401 Earnings Performance Return on Net Worth 0.5977 PAT/Total Assets 0.4023 The final PROMETHEE scores for these dimensions are presented in Tables 4-8 in the Appendix. The top five and bottom five performers for each dimension and for each criterion within each dimension is highlighted in green and red, respectively. The sample banks exhibited mixed performance along the Management Soundness dimension. Overall, the best performing banks were: IDBI Bank, Yes Bank, Axis Bank, Citi Bank, and ICICI Bank, while the worst performing banks were: Dhanalaxmi Bank, Central Bank of India, United Bank, Bank of Rajasthan, and Development Credit Bank. The overall PROMETHEE scores for Management Soundness were significantly correlated with the PROMETHEE scores for Profit per Employee (ρ = 0.913), followed by Business per Employee (ρ = 0.655), and Total Advances/Total Deposits (ρ = 0.546), and were not significantly correlated with the PROMETHEE score for Total Investment/Total Assets (ρ = 0.013). Total Investment/Total Assets was significantly negatively correlated with Business Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 320 per Employee (ρ = -0.493) and Total Advances/Total Deposits (ρ = -0.480), with most banks having reverse ranks in the latter and the former, except for Kotak Mahindra Bank. Of course, Profit per Employee and Business per Employee were significantly correlated (ρ = 0.760), and Total Advances/Total Deposits was significantly correlated with both (ρ = 0.503 and ρ = 0.448, respectively). The sample banks exhibited much more consistency along the Liquidity dimension. Overall, the best performing banks were: Allahabad Bank, Oriental Bank of Commerce, Bank of India, IDBI Bank, and Andhra Bank, while the worst performing banks were: Axis Bank, Yes Bank, Tamilnad Mercentile Bank, J & K Bank, and ICICI Bank. The overall PROMETHEE scores for Liquidity were significantly correlated with the PROMETHEE scores for Government Securities/Total Investments (ρ = 0.957) and Government Securities/Total Assets (ρ = 0.953), which in turn were significantly correlated (ρ = 0.853). The sample banks also exhibited consistency along the Capital Adequacy dimension. Overall, the best performing banks were: Kotak Mahindra Bank, Tamilnad Mercentile Bank, Federal Bank, HDFC Bank, and Yes Bank, while the worst performing banks were: Central Bank of India, Bank of Rajasthan, United Commercial Bank, Dena Bank, and Syndicate Bank. The overall PROMETHEE scores for Capital Adequacy were significantly correlated with the PROMETHEE scores for Coverage ratio (ρ = 0.910), Capital Adequacy ratio (ρ = 0.840), and with Interest Spread/Total Assets (ρ = 0.814). Further, Coverage ratio was significantly correlated with Interest Spread/Total Assets (ρ = 0.693) and Capital Adequacy ratio (ρ = 0.658), and Interest Spread/Total Assets was significantly correlated with Capital Adequacy ratio (ρ = 0.577). The sample banks also exhibited mixed performance along the Risk dimension. Overall, the best performing banks were: Tamilnad Mercentile Bank, HDFC Bank, Citi Bank, Indian Bank, and Standard Chartered Bank, while the worst performing banks were: United Commercial Bank, Dena Bank, Central Bank of India, Syndicate Bank, and State Bank of India. The overall PROMETHEE scores for Risk were significantly correlated with the PROMETHEE scores for Beta (ρ = 0.769), Debt/Equity ratio (ρ = 0.700), and Net NPA/Total Advances (ρ = 0.667). Further, Beta was significantly correlated with Net NPA/Total Advances (ρ = 0.346) and Debt/Equity ratio (ρ = 0.333), but Net NPA/Total Advances was not significantly correlated with Debt/Equity ratio (ρ = 0.172). The sample banks exhibited consistency along the Earnings Performance dimension. Overall, the best performing banks were: Standard Chartered Bank, Indian Bank, Punjab National Bank, Union Bank of India, and Canara Bank, while the worst performing banks were: Development Credit Bank, United Bank, Dhanalakshmi Bank, ING Vysya Bank, and Bank of Rajasthan. The overall PROMETHEE scores for Earnings Performance were significantly correlated with the PROMETHEE scores for Return on Net Worth (ρ = 0.907) and PAT/Total Assets (ρ = 0.611), which in turn were significantly correlated (ρ = 0.292). Further, there were significant correlations between the PROMETHEE scores of some of the dimensions. The PROMETHEE score for Risk was significantly negatively correlated with those of Management Soundness (ρ = -0.557), Capital Adequacy (ρ = -0.792), and Earnings Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 321 Performance (ρ = -0.319), and significantly positively correlated with that of Liquidity (ρ = 0.330); in turn, the PROMETHEE score of Management Soundness was significantly positively correlated with those of Capital Adequacy (ρ = 0.656) and Earnings Performance (ρ = 0.376); and the PROMETHEE score of Capital Adequacy was significantly negatively correlated with that of Liquidity (ρ = -0.454). The results of the Mann-Whitney tests comparing public sector and private sector banks are presented in Table 9 below. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 322 Table 9. Mann-Whitney tests comparing PROMETHEE scores in public sector and private sector banks mean rank z Stat p-value Total Investment/Total Assets public sector 13.9211 -2.5666 0.0103 private sector 22.8438 Total Advances/Total Deposits public sector 18.3421 -0.2153 0.8296 private sector 17.5938 Business per Employee public sector 19.7632 -1.1095 0.2672 private sector 15.9063 Profit per Employee public sector 17.4737 -0.3326 0.7395 private sector 18.6250 Management Soundness public sector 16.2895 -1.0763 0.2818 private sector 20.0313 Govt Sec/Total Investment public sector 25.1316 -4.4875 0.0000 private sector 9.5313 Govt Sec/Total Assets public sector 24.5526 -4.1232 0.0000 private sector 10.2188 Liquidity public sector 24.8684 -4.3219 0.0000 private sector 9.8438 Coverage ratio public sector 12.2895 -3.5933 0.0003 private sector 24.7813 Capital Adequacy ratio public sector 14.0263 -2.5004 0.0124 private sector 22.7188 Interest Spread/Total Assets public sector 14.0263 -2.5004 0.0124 private sector 22.7188 Capital Adequacy public sector 12.7632 -3.2952 0.0010 private sector 24.2188 Debt/Equity ratio public sector 23.5526 -3.4939 0.0005 private sector 11.4063 Net NPA/Total Advances public sector 18.5789 -0.3643 0.7156 private sector 17.3125 Beta public sector 20.4211 -1.5247 0.1273 private sector 15.1250 Risk public sector 22.2895 -2.6991 0.0070 private sector 12.9063 Return on Net Worth public sector 21.9737 -2.5004 0.0124 private sector 13.2813 PAT/Total Assets public sector 14.8158 -2.0036 0.0451 private sector 21.7813 Earnings Performance public sector 19.9211 -1.2088 0.2267 private sector 15.7188 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 323 There was found to be no significant difference between public sector banks and private sector banks with respect to Management Soundness, and in particular with respect to Total Advances/Total Deposits, Business per Employee, and Profit per Employee; however, there was found to be significant difference between public sector banks and private sector banks with respect to Total Investments/Total Assets, with private sector banks performing significantly better than public sector banks in this regard. There was found to be significant difference between public sector banks and private sector banks with respect to Liquidity, and in particular with respect to Government Securities/Total Investments and Government Securities/Total Assets, with public sector banks performing significantly better than their private sector counterparts. There was found to be significant difference between public sector banks and private sector banks with respect to Capital Adequacy, and in particular with respect to Coverage ratio, Capital Adequacy ratio, and Interest Spread/Total Assets, with private sector banks performing significantly better than their public sector counterparts. There was found to be significant difference between public sector banks and private sector banks with respect to Risk, and in particular with respect to Debt/Equity ratio, with private sector banks performing significantly better than public sector banks; however, there was no significant difference between public sector banks and private sector banks with respect to Net NPA/Total Advances and Beta. There was found to be no significant difference between public sector banks and private sector banks with respect to Earnings Performance; however, there was found to be significant difference between public sector banks and private sector banks with respect to Return on Net Worth, with public sector banks performing significantly better than private sector banks, and with respect to PAT/Total Assets, with private sector banks performing significantly better than public sector banks. Discussion The results of the study raise questions relating to the direct applicability of multi-criteria decision models in bank performance measurement. The factor structure underlying the CAMELS ratios consisted of four distinct dimensions of bank performance which were analogous to the CAMELS components, viz. Management Soundness, Liquidity, Capital Adequacy, and Earnings Performance, as well as a distinct dimension, Risk, comprising Debt/Equity ratio, Net NPA/Total Assets, and Beta, which represent the financial/insolvency risk, credit risk, and market risk aspects, respectively, of banking risk. However, the PROMETHEE scores within these dimensions were not very consistent, particularly within the Management Soundness and Risk dimensions. The ranking of banks along the dimensions also varied considerably. The comparison of the PROMETHEE scores of public sector and private sector banks along the dimensions was in accordance with the descriptive statistics. The results of the Mann-Whitney tests indicated that private sector banks performed better than public sector banks in terms of Capital Adequacy and Risk, while public sector banks performed better Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 324 than private sector banks in terms of Liquidity, while there was no significant difference between public sector and private sector banks in terms of Management Soundness and Earnings Performance. In fact, paradoxically, public sector banks performed significantly better than private sector banks with respect to Return on Net Worth, while private sector banks performed significantly better than public sector banks with respect to PAT/Total Assets. There were several limitations inherent in the current study. The study only considers a sample of thirty-five banks, over a period of only five years (2007-11), which in particular was adversely affected by the global financial crisis. Thus, the results of the study may be specific to the period considered, and may not be generalisable. Also, the current approach considers only some performance parameters, and fails to consider some qualitative aspects of banking performance, such as management performance and staff efficiency. Further, the study did not analyse the sensitivity of the PROMETHEE scores to the preference thresholds and the parameter weights. Also, the study has used factor analysis, which determines weights in order to maximize the variance explained, but which may not reflect the importance of the variables in banking performance. There is vast scope for further research in the area of bank performance and risk measurement, particularly due to the dynamic nature of the current banking environment. There are several other multi-criteria models that can be used to analyse banking performance to provide alternative perspectives to regulators and policy makers, for example, ELECTRE methodology may be used to identify banks that may be in distress, VIKOR methodology may be used to identify critical trade-offs in banking performance, and AHP methodology may be used to incorporate qualitative aspects of banking performance. References Altman, I. E. (1968). Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x Avkiran, N. K. (2010). Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks. Omega, 39(3), 323-334. https://doi.org/10.1016/j.omega.2010.08.001 Babic, Z., Belak, V., & Tomic-Plazibat, N. (1999). Ranking of Croatian Banks according to Business Efficiency. 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PROMETHEE Scores for Management Soundness Total Investment/Total Assets Total Advances/Total Deposits Business per Employee Profit per Employee ϕ+ ϕ- ϕ ϕ+ ϕ- ϕ ϕ+ ϕ- ϕ ϕ+ ϕ- ϕ Φ Allahabad Bank 0.4602 0.2199 0.2403 0.1414 0.2955 -0.1541 0.2799 0.2679 0.0120 0.2071 0.2401 -0.0330 0.0176 Andhra Bank 0.0754 0.6570 -0.5816 0.2846 0.1891 0.0955 0.3820 0.2321 0.1499 0.2776 0.1908 0.0868 -0.0618 Axis Bank 0.7443 0.0726 0.6718 0.1659 0.2684 -0.1025 0.8103 0.0571 0.7532 0.8458 0.0681 0.7776 0.5328 Bank of Baroda 0.0294 0.9156 -0.8862 0.2644 0.1972 0.0672 0.5535 0.1697 0.3838 0.3691 0.1641 0.2050 -0.0548 Bank of India 0.0411 0.8077 -0.7666 0.3659 0.1694 0.1965 0.4812 0.1943 0.2869 0.1858 0.2587 -0.0729 -0.0889 Bank of Rajasthan 0.9276 0.0057 0.9219 0.0000 0.9998 -0.9998 0.0294 0.6062 -0.5769 0.0180 0.7327 -0.7147 -0.3396 Canara Bank 0.1292 0.4939 -0.3646 0.2445 0.2093 0.0351 0.4356 0.2110 0.2246 0.2776 0.1908 0.0868 -0.0031 Central Bank of India 0.2300 0.3710 -0.1411 0.0643 0.4820 -0.4177 0.0630 0.5068 -0.4438 0.0366 0.5958 -0.5592 -0.3912 Citi Bank 0.0417 0.8039 -0.7622 0.8865 0.0616 0.8249 0.9375 0.0294 0.9080 0.9706 0.0294 0.9412 0.4794 Corporation Bank 0.3749 0.2758 0.0991 0.1671 0.2673 -0.1002 0.7696 0.0780 0.6915 0.6319 0.1081 0.5238 0.3103 Dena Bank 0.1814 0.4175 -0.2360 0.1006 0.3711 -0.2705 0.2519 0.2818 -0.0298 0.1166 0.3442 -0.2276 -0.1894 Development Credit Bank 0.6510 0.1174 0.5337 0.1396 0.2981 -0.1585 0.0038 0.7105 -0.7067 0.0000 0.9614 -0.9614 -0.3293 Dhanlaxmi Bank 0.1084 0.5573 -0.4489 0.0433 0.5940 -0.5507 0.0101 0.6779 -0.6678 0.0220 0.6867 -0.6648 -0.5841 Federal Bank 0.4958 0.2001 0.2957 0.2840 0.1893 0.0946 0.2696 0.2725 -0.0029 0.2776 0.1908 0.0868 0.1179 HDFC Bank 0.6605 0.1130 0.5475 0.1989 0.2415 -0.0427 0.0615 0.5102 -0.4487 0.2523 0.2055 0.0469 0.0233 ICICI Bank 0.1276 0.4976 -0.3700 0.8724 0.0725 0.7998 0.7301 0.0982 0.6320 0.7232 0.0868 0.6364 0.4234 IDBI Bank 0.2158 0.0000 0.2158 0.9893 0.0000 0.9893 1.0000 0.0000 1.0000 0.7232 0.0000 0.7232 0.7316 Indian Bank 0.6039 0.1419 0.4620 0.0975 0.3778 -0.2802 0.1253 0.3964 -0.2711 0.3691 0.1641 0.2050 0.0302 ING Vysya Bank 0.2984 0.3223 -0.0239 0.3622 0.1701 0.1921 0.0841 0.4665 -0.3824 0.0740 0.4481 -0.3741 -0.1523 Indian Overseas Bank 0.2585 0.3488 -0.0903 0.3203 0.1796 0.1407 0.1945 0.3171 -0.1226 0.1166 0.3442 -0.2276 -0.0776 J &K Bank 0.6250 0.1299 0.4951 0.0336 0.6708 -0.6372 0.1475 0.3644 -0.2169 0.2776 0.1908 0.0868 -0.0637 Karnataka Bank 0.9562 0.0000 0.9562 0.0292 0.7197 -0.6904 0.1458 0.3667 -0.2209 0.1166 0.3442 -0.2276 -0.0419 Kotak Mahindra 0.8448 0.0314 0.8134 0.9706 0.0187 0.9519 0.0003 0.7467 -0.7465 0.1858 0.2587 -0.0729 0.2229 Oriental Bank of Commerce 0.1723 0.4280 -0.2557 0.1462 0.2895 -0.1433 0.7947 0.0637 0.7310 0.4304 0.1508 0.2796 0.1597 Punjab National Bank 0.1256 0.5032 -0.3776 0.3690 0.1690 0.2000 0.1777 0.3319 -0.1542 0.2523 0.2055 0.0469 -0.0737 South Indian Bank 0.2122 0.3852 -0.1730 0.0854 0.4080 -0.3226 0.1905 0.3201 -0.1296 0.1166 0.3442 -0.2276 -0.2118 Standard Chartered 0.0000 1.0000 -1.0000 0.8933 0.0588 0.8345 0.7316 0.0975 0.6340 1.0000 0.0000 1.0000 0.3671 State Bank of India 0.1444 0.4654 -0.3210 0.6532 0.1390 0.5143 0.0496 0.5427 -0.4931 0.0926 0.3947 -0.3021 -0.1589 Syndicate Bank 0.0595 0.7169 -0.6574 0.2689 0.1951 0.0738 0.1922 0.3188 -0.1266 0.0740 0.4481 -0.3741 -0.2734 Tamilnad Mercentile Bank 0.8131 0.0426 0.7705 0.0823 0.4175 -0.3353 0.0051 0.7012 -0.6960 0.1858 0.2587 -0.0729 -0.0852 UCO Bank 0.2167 0.3812 -0.1645 0.1355 0.3047 -0.1693 0.2784 0.2685 0.0099 0.0433 0.5652 -0.5220 -0.2114 Union Bank of India 0.1487 0.4587 -0.3100 0.2655 0.1966 0.0689 0.2822 0.2671 0.0151 0.2776 0.1908 0.0868 -0.0350 United Bank 0.7623 0.0631 0.6992 0.0297 0.7130 -0.6833 0.0000 0.7527 -0.7527 0.0260 0.6572 -0.6312 -0.3428 Vijiya Bank 0.4664 0.2165 0.2499 0.0579 0.5102 -0.4523 0.2362 0.2914 -0.0552 0.0926 0.3947 -0.3021 -0.1373 YES Bank 0.5264 0.1857 0.3407 0.5787 0.1474 0.4313 0.8558 0.0434 0.8123 0.8941 0.0588 0.8352 0.6085 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 329 Table 5. PROMETHEE Scores for Liquidity Govt Sec/Total Investment Govt Sec/Total Assets ϕ+ ϕ- ϕ ϕ+ ϕ- ϕ Φ Allahabad Bank 0.6316 0.0258 0.6057 0.8057 0.0000 0.8057 0.7049 Andhra Bank 0.6946 0.0074 0.6872 0.5894 0.0371 0.5523 0.6203 Axis Bank 0.0000 0.8242 -0.8242 0.0142 0.7516 -0.7374 -0.7812 Bank of Baroda 0.6550 0.0143 0.6407 0.4770 0.3005 0.1765 0.4105 Bank of India 0.7953 0.0000 0.7953 0.5635 0.0727 0.4908 0.6443 Bank of Rajasthan 0.0158 0.7178 -0.7020 0.1479 0.5561 -0.4083 -0.5564 Canara Bank 0.5909 0.0619 0.5290 0.5556 0.0862 0.4693 0.4994 Central Bank of India 0.6029 0.0456 0.5573 0.6295 0.0140 0.6154 0.5861 Citi Bank 0.3124 0.4764 -0.1640 0.0953 0.5994 -0.5040 -0.3326 Corporation Bank 0.6000 0.0485 0.5515 0.6558 0.0097 0.6461 0.5984 Dena Bank 0.5973 0.0519 0.5454 0.5932 0.0328 0.5604 0.5528 Development Credit Bank 0.0867 0.6117 -0.5250 0.1591 0.5496 -0.3905 -0.4583 Dhanlaxmi Bank 0.2247 0.5192 -0.2945 0.1071 0.5872 -0.4801 -0.3865 Federal Bank 0.0625 0.6392 -0.5767 0.0801 0.6184 -0.5383 -0.5577 HDFC Bank 0.0985 0.6018 -0.5032 0.1714 0.5444 -0.3729 -0.4386 ICICI Bank 0.0609 0.6415 -0.5806 0.0159 0.7408 -0.7250 -0.6522 IDBI Bank 0.6520 0.0000 0.6520 0.6085 0.0000 0.6085 0.6304 Indian Bank 0.4917 0.2664 0.2253 0.5991 0.0274 0.5716 0.3970 ING Vysya Bank 0.1475 0.5677 -0.4203 0.1207 0.5752 -0.4545 -0.4372 Indian Overseas Bank 0.5874 0.0684 0.5189 0.6142 0.0187 0.5954 0.5569 J &K Bank 0.0140 0.7223 -0.7084 0.0276 0.7055 -0.6778 -0.6932 Karnataka Bank 0.0149 0.7197 -0.7048 0.1634 0.5474 -0.3840 -0.5458 Kotak Mahindra 0.1773 0.5479 -0.3705 0.4010 0.4313 -0.0304 -0.2019 Oriental Bank of Commerce 0.6734 0.0103 0.6631 0.6396 0.0119 0.6277 0.6456 Punjab National Bank 0.6481 0.0168 0.6313 0.6076 0.0216 0.5860 0.6088 South Indian Bank 0.0889 0.6094 -0.5205 0.0483 0.6608 -0.6125 -0.5661 Standard Chartered 0.2241 0.5195 -0.2954 0.0000 0.9253 -0.9253 -0.6077 State Bank of India 0.4439 0.3516 0.0922 0.4043 0.4279 -0.0235 0.0349 Syndicate Bank 0.4152 0.3990 0.0162 0.2753 0.5106 -0.2353 -0.1085 Tamilnad Mercentile Bank 0.0038 0.7742 -0.7704 0.0467 0.6635 -0.6168 -0.6943 UCO Bank 0.6118 0.0385 0.5733 0.5995 0.0271 0.5723 0.5728 Union Bank of India 0.5398 0.1674 0.3724 0.5104 0.1889 0.3214 0.3471 United Bank 0.3725 0.4400 -0.0675 0.5240 0.1546 0.3694 0.1491 Vijiya Bank 0.4550 0.3319 0.1232 0.5038 0.2085 0.2954 0.2086 YES Bank 0.0083 0.7449 -0.7366 0.0155 0.7423 -0.7268 -0.7317 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 330 Table 6. PROMETHEE Scores for Capital Adequacy Coverage ratio CAR Interest Spread/Total Assets ϕ+ ϕ- ϕ ϕ+ ϕ- ϕ ϕ+ ϕ- ϕ Φ Allahabad Bank 0.1788 0.4429 -0.2641 0.3434 0.2156 0.1278 0.1866 0.2799 -0.0934 -0.0674 Andhra Bank 0.3162 0.2950 0.0212 0.2211 0.2862 -0.0650 0.3966 0.2109 0.1857 0.0340 Axis Bank 0.6048 0.1621 0.4427 0.5370 0.1455 0.3915 0.9107 0.0280 0.8826 0.5454 Bank of Baroda 0.3375 0.2800 0.0576 0.2993 0.2377 0.0616 0.2449 0.2510 -0.0061 0.0414 Bank of India 0.1202 0.5225 -0.4024 0.1690 0.3363 -0.1673 0.3302 0.2273 0.1029 -0.1725 Bank of Rajasthan 0.0304 0.7308 -0.7004 0.0019 0.7780 -0.7761 0.0038 0.6674 -0.6635 -0.7190 Canara Bank 0.1234 0.5184 -0.3951 0.6732 0.1304 0.5428 0.0425 0.4376 -0.3951 -0.0378 Central Bank of India 0.0031 0.8909 -0.8878 0.0000 0.8100 -0.8100 0.0195 0.5102 -0.4907 -0.7479 Citi Bank 0.8402 0.0653 0.7749 0.0157 0.6820 -0.6663 0.9000 0.0331 0.8669 0.2514 Corporation Bank 0.3486 0.2733 0.0753 0.4704 0.1599 0.3105 0.0467 0.4292 -0.3825 0.0377 Dena Bank 0.0514 0.6643 -0.6129 0.0122 0.6973 -0.6851 0.1381 0.3144 -0.1763 -0.5191 Development Credit Bank 0.5699 0.1718 0.3981 0.4533 0.1656 0.2877 0.0819 0.3771 -0.2953 0.1634 Dhanlaxmi Bank 0.2277 0.3776 -0.1500 0.0387 0.6015 -0.5628 0.1865 0.2800 -0.0934 -0.2915 Federal Bank 0.8828 0.0238 0.8590 0.9571 0.0002 0.9569 0.6720 0.1576 0.5144 0.8006 HDFC Bank 0.7941 0.0924 0.7016 0.8236 0.1055 0.7181 0.9001 0.0329 0.8672 0.7539 ICICI Bank 0.5075 0.1953 0.3121 0.0788 0.4938 -0.4150 0.0156 0.5319 -0.5163 -0.1950 IDBI Bank 0.1161 0.0000 0.1161 0.1212 0.0000 0.1212 0.1408 0.3122 -0.1715 0.0381 Indian Bank 0.4517 0.2214 0.2302 0.3960 0.1891 0.2070 0.6134 0.1682 0.4452 0.2811 ING Vysya Bank 0.2757 0.3279 -0.0522 0.1354 0.3789 -0.2436 0.0321 0.4613 -0.4292 -0.2298 Indian Overseas Bank 0.1011 0.5574 -0.4563 0.4138 0.1812 0.2326 0.0714 0.3911 -0.3198 -0.1559 J &K Bank 0.5184 0.1900 0.3284 0.5482 0.1438 0.4044 0.2545 0.2477 0.0068 0.2680 Karnataka Bank 0.5189 0.1898 0.3291 0.1897 0.3127 -0.1230 0.0000 0.7528 -0.7528 -0.1437 Kotak Mahindra 0.9593 0.0000 0.9593 0.9574 0.0000 0.9574 0.9986 0.0000 0.9986 0.9695 Oriental Bank of Commerce 0.3750 0.2590 0.1161 0.1166 0.4077 -0.2911 0.1293 0.3226 -0.1932 -0.1250 Punjab National Bank 0.2651 0.3378 -0.0727 0.2479 0.2673 -0.0194 0.4710 0.1948 0.2762 0.0445 South Indian Bank 0.3124 0.2978 0.0146 0.4866 0.1556 0.3310 0.2221 0.2612 -0.0392 0.1202 Standard Chartered 0.8769 0.0288 0.8481 0.2095 0.2951 -0.0856 0.5858 0.1730 0.4128 0.3715 State Bank of India 0.2296 0.3753 -0.1457 0.1917 0.3107 -0.1191 0.0496 0.4243 -0.3748 -0.1992 Syndicate Bank 0.0340 0.7139 -0.6799 0.0393 0.5997 -0.5605 0.1131 0.3400 -0.2269 -0.5086 Tamilnad Mercentile Bank 0.9073 0.0115 0.8957 0.9283 0.0319 0.8964 0.8088 0.1298 0.6790 0.8358 UCO Bank 0.0000 0.9205 -0.9205 0.0082 0.7215 -0.7134 0.0316 0.4626 -0.4309 -0.7056 Union Bank of India 0.1132 0.5335 -0.4204 0.2598 0.2597 0.0001 0.2484 0.2497 -0.0013 -0.1438 United Bank 0.0354 0.7089 -0.6735 0.0859 0.4759 -0.3901 0.0279 0.4751 -0.4472 -0.5027 Vijiya Bank 0.0461 0.6776 -0.6315 0.1208 0.3996 -0.2788 0.0158 0.5303 -0.5145 -0.4646 YES Bank 0.6631 0.1493 0.5138 0.8996 0.0754 0.8242 0.8617 0.0860 0.7758 0.7048 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 331 Table 7. PROMETHEE Scores for Risk Debt/Equity ratio Net NPA/Total Advances Beta ϕ+ ϕ- ϕ ϕ+ ϕ- ϕ ϕ+ ϕ- ϕ Φ Allahabad Bank 0.3280 0.2673 0.0607 0.2509 0.3843 -0.1334 0.4005 0.1408 0.2597 0.0620 Andhra Bank 0.4194 0.1894 0.2300 0.0068 0.8541 -0.8473 0.2671 0.2826 -0.0156 -0.2219 Axis Bank 0.1997 0.4965 -0.2968 0.0569 0.7037 -0.6467 0.6509 0.0227 0.6282 -0.1018 Bank of Baroda 0.3653 0.2243 0.1410 0.0428 0.7237 -0.6809 0.1847 0.4673 -0.2826 -0.2841 Bank of India 0.5929 0.1051 0.4878 0.2642 0.3691 -0.1048 0.3802 0.1576 0.2225 0.1949 Bank of Rajasthan 0.6186 0.0982 0.5203 0.2142 0.4425 -0.2283 0.0000 0.9115 -0.9115 -0.2227 Canara Bank 0.3686 0.2213 0.1474 0.3940 0.2419 0.1521 0.2971 0.2415 0.0556 0.1178 Central Bank of India 0.9026 0.0350 0.8676 0.5213 0.1830 0.3382 0.4396 0.1096 0.3300 0.5037 Citi Bank 0.0224 0.8368 -0.8144 0.4046 0.2359 0.1688 0.0000 0.9115 -0.9115 -0.5112 Corporation Bank 0.3862 0.2093 0.1770 0.0310 0.7562 -0.7252 0.1306 0.7519 -0.6213 -0.4031 Dena Bank 0.7843 0.0649 0.7194 0.6577 0.1331 0.5246 0.6626 0.0201 0.6425 0.6266 Development Credit Bank 0.0863 0.7202 -0.6340 0.9793 0.0000 0.9793 0.7844 0.0026 0.7818 0.3993 Dhanlaxmi Bank 0.5542 0.1187 0.4354 0.3365 0.2880 0.0485 0.4948 0.0772 0.4176 0.2970 Federal Bank 0.0402 0.8003 -0.7601 0.0413 0.7262 -0.6849 0.2528 0.3047 -0.0519 -0.4935 HDFC Bank 0.0814 0.7267 -0.6454 0.0401 0.7290 -0.6889 0.1821 0.4796 -0.2975 -0.5420 ICICI Bank 0.2120 0.4761 -0.2641 0.8213 0.0564 0.7650 0.7208 0.0104 0.7104 0.4193 IDBI Bank 0.3685 0.0000 0.3685 0.4634 0.0000 0.4634 0.5194 0.0668 0.4526 0.4296 Indian Bank 0.1331 0.6484 -0.5153 0.0189 0.7991 -0.7802 0.1990 0.4204 -0.2214 -0.5059 ING Vysya Bank 0.3142 0.2925 0.0217 0.3097 0.3183 -0.0086 0.1475 0.6353 -0.4879 -0.1620 Indian Overseas Bank 0.5481 0.1218 0.4263 0.6087 0.1518 0.4569 0.2281 0.3503 -0.1222 0.2502 J &K Bank 0.1902 0.5149 -0.3247 0.2537 0.3807 -0.1271 0.1168 0.8506 -0.7338 -0.3962 Karnataka Bank 0.1704 0.5556 -0.3852 0.5935 0.1574 0.4361 0.3315 0.2030 0.1285 0.0704 Kotak Mahindra 0.0000 0.8969 -0.8969 0.8824 0.0208 0.8616 0.3224 0.2123 0.1101 0.0470 Oriental Bank of Commerce 0.2329 0.4411 -0.2081 0.2433 0.3956 -0.1523 0.2893 0.2515 0.0379 -0.1054 Punjab National Bank 0.3209 0.2792 0.0416 0.1282 0.5899 -0.4617 0.1912 0.4437 -0.2526 -0.2306 South Indian Bank 0.3375 0.2541 0.0834 0.1453 0.5594 -0.4141 0.1494 0.6254 -0.4760 -0.2770 Standard Chartered 0.0011 0.8894 -0.8883 0.4797 0.1997 0.2800 0.0000 0.9115 -0.9115 -0.4966 State Bank of India 0.3394 0.2519 0.0875 0.8742 0.0238 0.8504 0.4396 0.1096 0.3300 0.4309 Syndicate Bank 0.9406 0.0179 0.9227 0.3223 0.3032 0.0191 0.5815 0.0441 0.5374 0.4826 Tamilnad Mercentile Bank 0.0078 0.8694 -0.8616 0.3711 0.2565 0.1147 0.0000 0.9115 -0.9115 -0.5447 UCO Bank 0.9856 0.0000 0.9856 0.8618 0.0302 0.8316 0.8231 0.0000 0.8231 0.8776 Union Bank of India 0.4983 0.1474 0.3509 0.1839 0.4931 -0.3091 0.2398 0.3270 -0.0871 -0.0238 United Bank 0.5610 0.1158 0.4451 0.7835 0.0770 0.7065 0.2126 0.3858 -0.1732 0.3242 Vijiya Bank 0.6964 0.0819 0.6145 0.3711 0.2565 0.1147 0.4948 0.0772 0.4176 0.3766 YES Bank 0.1610 0.5792 -0.4182 0.0000 0.9105 -0.9105 0.6163 0.0324 0.5839 -0.2457 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 332 Table 8. PROMETHEE Scores for Earnings Performance Return on Net Worth PAT/Total Assets ϕ+ ϕ- ϕ ϕ+ ϕ- ϕ Φ Allahabad Bank 0.5930 0.0826 0.5104 0.3515 0.2074 0.1441 0.3631 Andhra Bank 0.5495 0.1151 0.4344 0.4393 0.1473 0.2920 0.3771 Axis Bank 0.4503 0.2177 0.2325 0.5446 0.0902 0.4544 0.3218 Bank of Baroda 0.3779 0.2970 0.0809 0.3125 0.2473 0.0652 0.0746 Bank of India 0.6319 0.0599 0.5720 0.2782 0.2872 -0.0090 0.3383 Bank of Rajasthan 0.1024 0.5979 -0.4956 0.0294 0.8033 -0.7739 -0.6075 Canara Bank 0.6943 0.0329 0.6615 0.3514 0.2075 0.1439 0.4532 Central Bank of India 0.4440 0.2248 0.2192 0.0394 0.7462 -0.7068 -0.1534 Citi Bank 0.3156 0.3643 -0.0487 0.7650 0.0451 0.7199 0.2605 Corporation Bank 0.4571 0.2109 0.2462 0.3889 0.1817 0.2071 0.2305 Dena Bank 0.6653 0.0448 0.6205 0.2174 0.3822 -0.1648 0.3045 Development Credit Bank 0.0000 1.0000 -1.0000 0.0000 1.0000 -1.0000 -1.0000 Dhanlaxmi Bank 0.0319 0.7548 -0.7229 0.0448 0.7233 -0.6785 -0.7051 Federal Bank 0.1002 0.6013 -0.5011 0.5159 0.1011 0.4148 -0.1326 HDFC Bank 0.3210 0.3579 -0.0369 0.6797 0.0609 0.6188 0.2269 ICICI Bank 0.0820 0.6377 -0.5558 0.2638 0.3064 -0.0425 -0.3493 IDBI Bank 0.0392 0.0000 0.0392 0.0511 0.0000 0.0511 0.0440 Indian Bank 0.8395 0.0000 0.8395 0.8397 0.0330 0.8067 0.8263 ING Vysya Bank 0.0323 0.7535 -0.7213 0.0890 0.6177 -0.5287 -0.6438 Indian Overseas Bank 0.6106 0.0714 0.5391 0.2875 0.2760 0.0115 0.3269 J &K Bank 0.2959 0.3899 -0.0940 0.4708 0.1263 0.3445 0.0824 Karnataka Bank 0.1160 0.5808 -0.4648 0.3101 0.2500 0.0601 -0.2536 Kotak Mahindra 0.0311 0.7593 -0.7282 0.5266 0.0965 0.4301 -0.2622 Oriental Bank of Commerce 0.1089 0.5891 -0.4802 0.1496 0.5078 -0.3582 -0.4311 Punjab National Bank 0.7171 0.0259 0.6912 0.5035 0.1072 0.3962 0.5725 South Indian Bank 0.2421 0.4509 -0.2088 0.2497 0.3283 -0.0786 -0.1564 Standard Chartered 0.8383 0.0001 0.8382 1.0000 0.0000 1.0000 0.9033 State Bank of India 0.1835 0.5057 -0.3222 0.2217 0.3751 -0.1534 -0.2543 Syndicate Bank 0.5567 0.1086 0.4481 0.1284 0.5446 -0.4162 0.1004 Tamilnad Mercentile Bank 0.1586 0.5306 -0.3720 0.8766 0.0294 0.8472 0.1185 UCO Bank 0.5528 0.1119 0.4409 0.0450 0.7225 -0.6775 -0.0091 Union Bank of India 0.7964 0.0078 0.7886 0.3420 0.2161 0.1259 0.5220 United Bank 0.0294 0.7804 -0.7510 0.0298 0.7989 -0.7691 -0.7583 Vijiya Bank 0.2667 0.4260 -0.1593 0.0900 0.6156 -0.5256 -0.3067 YES Bank 0.4335 0.2372 0.1962 0.5465 0.0896 0.4569 0.3011