How to cite this paper
Laha, S & Biswas, S. (2019). A hybrid unsupervised learning and multi-criteria decision making approach for performance evaluation of Indian banks.Accounting, 5(4), 169-184.
Refrences
Abbott, M., Wu, S., & Wang, W. (2013). The productivity and performance of Australia's major banks since deregulate. Journal of Economics & Finance, 37(1), 122- 135.
Akkoç, S., & Vatansever, K. (2013). Fuzzy performance evaluation with AHP and TOPSIS methods: Evidence from Turkish banking sector after the global financial crisis. Eurasian Journal of Business and Economics, 6 (11), 53-74.
Albayrak, E., & Erensal, Y. C. (2005). A study bank selection decision in Turkey using the extended fuzzy AHP method. In 35th International conference on computers and industrial engineering, Istanbul, Turkey.
Altman, I. E. (1968). Financial ratios, discriminant analysis and prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Amile, M., Sedaghat, M., & Poorhossein, M. (2013).Performance evaluation of banks using fuzzy AHP and TOPSIS case study: state-owned banks, partially private and private banks In Iran. Caspian Journal of Applied Sciences Research, 3, 128-138.
Amirzadeh, R., & Shoorvarzy, M.R. (2013). Prioritizing service quality factors in Iranian Islamic banking using a fuzzy approach. International Journal of Islamic and Middle Eastern Finance and Management, 6(1), 64-78.
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
Ayadi, O.F., Adebayo, A. O., & Omolehinwa, E. (1998). Bank performance measurement in a developing economy: An application of data envelopment analysis. Managerial Finance, 24(7), 5-16.
Baležentis, A., Baležentis, T., & Misiunas, A. (2012). An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy mcdm methods. Technological and Economic Development of Economy, 18(1), 34-53.
Bayyurt, N. (2013). Ownership effect on bank's performance: Multi criteria decision making approaches on foreign and domestic Turkish banks. Procedia - Social and Behavioral Sciences 99, 919 - 928. https://doi.org/10.1016/j.sbspro.2013.10.565
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 5, 71-111.
Beaver, W. H. (1968). Market prices, financial ratios, and the prediction of failure. Journal of Accounting Research, 6(2), 179-192.
Bhattacharyay, B.N. (2011). Macro-prudential monitoring of financial crisis: An empirical framework. in Chatterji, M., Gopal, D., and Singh, S. (eds.) Governance, Development and Conflict (Contributions to Conflict Management, Peace Economics and Development, Emerald Group Publishing Limited, 18, 71-121.
Celen, A. (2014). Evaluating the financial performance of Turkish banking sector: A fuzzy mcdm approach. Journal of Economic Cooperation and Development, 35(2), 43-70.
Chatterjee, D., Chowdhury, S., & Mukherjee, B. (2010). A study of the application of fuzzy analytical hierarchical process (FAHP) in the ranking of Indian banks. International Journal of Engineering Science and Technology, 7, 2511-2520.
Dash, M., & Das, A. (2013). Performance appraisal of Indian banks using CAMELS rating. IUP Journal of Bank Management, 12(2), 31-42.
Dash, M., & Charles, C. (2012). An Analysis of the technical efficiency of banks in India. IUP Journal of Bank Management, 11(4), 100-109.
Dash, M., & Vegesna, S. (2014). Efficiency of public and private sector banks in India. Journal of Applied Management and Investments, 3(3), 183-187.
Datta Chaudhuri, T., & Ghosh, I. (2015). Application of multi-criteria decision making models in regulatory evaluation of commercial banks in India and its consistency with public perception. Available at SSRN: https://ssrn.com/abstract=2546647 or http://dx.doi.org/10.2139/ssrn.2546647
De young, R., Flannery, M.J., Lang, W.W., & Sorescu, S.M. (2001). The information content of bank exam ratings and subordinated debt prices. Journal of Money, Credit and Banking, 33 (4), 900-925.
Doumpos, M., & Zopounidis, C. (2015). A multicriteria approach to bank rating. In Bisdorf, R., Dias (eds), Evaluation and Decision Models with Multiple Criteria (pp. 563-587). Springer, Berlin, Heidelberg.
Doumpos, M., & Zopounidis, C. (2013). Efficiency and performance evaluation of European cooperative banks. in Efficiency and Productivity Growth: Modelling in the Financial Services Industry, ed. Pasiouras, F., John Wiley & Sons, Ltd, Chichester, UK.
Faber, V. (1994). Clustering and the continuous k-means algorithm. Los Alamos Science, 22(138144.21).
Fallah, M., Aryanezhadb, M.B., Najafi, S.E., & Shahsavaripour, N. (2011). An empirical study on measuring the relative efficiency using DEA method: A case study of bank industry. Management Science Letters, 1(1), 49-56. https://doi.org/10.5267/j.msl.2010.01.005
Fairfield, P. M. (1994). P/E, P/B and the present value of future dividends. Financial Analysts Journal, 50(4), 23-31.
Foster, E. M. (1970). Price-Earnings Ratio and corporate growth. Financial Analysts Journal, 26(1), 96-99.
Ghorabaee, M.K., Zavadskas, E.K., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research, 50(3), 25-41.
Ginevičius, R., & Podviezko, A. (2013). The evaluation of financial stability and soundness of lithuanian banks. Ekonomska Istraživanja-Economic Researc, 26(2), 191-208.
Gowri, C.A., & Malepati, V. (2017). Evaluation of financial performance of selected banks. Decision, 44(1), 3-14.
Grigoroudis, E., Tsitsiridi, E., & Zopounidis, C. (2013). Linking customer satisfaction, employee appraisal, and business performance: An evaluation methodology in the banking sector. Annals of Operations Research, 205(1), 5-27.
Gupta, R. (2008). A CAMEL model analysis of private sector banks in India. Journal of Gyan Management, 2(1), 3-8.
Halkos, G., & Salamouris, D. (2004). Efficiency measurement of Greek commercial banks with the use of financial ratios: A Data Envelopment Analysis approach. Management Account Research 15, 201-224. https://doi.org/10.1016/j.mar.2004.02.001
Hays, F. H., De Lurgio, S. A., & Gilbert, A. H. (2009). Efficiency ratios and community bank performance. Journal of Finance and Accountancy, 1, 1–15.
Ho, C.B., & Wu, D.D. (2009). Online banking performance evaluation using data envelopment analysis and principal component analysis. Computers & Operations Research, 36, 1835—1842
Kao, C., & Liu, S.-T. (2004). Predicting bank performance with financial forecasts: A case of Taiwan commercial banks. Journal of Banking and Finance, 28(10), 2353-2368. https://doi.org/10.1016/j.jbankfin.2003.09.008
Kaplan, R. S., & Norton, D. P. (1996). The Balanced Scorecard: Translating Strategy into Action. Harvard Business Press.
Klomp, J., & de Haan, J. (2011). Banking risk and regulation: Does one size fit all? DNB Working Paper No.323, available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=197723.
Kumar, P.R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques: a review. European Journal of Operational Research, 180 (1), 1–28.
Kwan, S., & Eisenbeis, R. A. (1997). Bank risk, capitalization, and operating efficiency. Journal of Financial Services Research, 12(2/3), 117-131.
Li, X., Wang, K., Liu, L., Xin, J., Yang, H., & Gao, C. (2011). Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Engineering, 26, 2085-2091.
Maishanu, M. M. (2004). A univariate approach to predicting failure in the commercial banking sub-sector. Nigerian Journal of Accounting Research, 1(1), 70-84.
Maliszewski, K. (2009). Measuring Stability of the Polish financial system by means of a synthetic index. Presented at the 12th International Conference on Finance and Banking, held on Oct. 28-29, 2009, organised by Silesian University in Opava.
Marie, A., Al-Nasser, A., & brahim, M. (2013). Operational-profitability-quality performance of Dubai's banks. Journal of Management Research, 13(1), 25-34.
Minh, N. K., Long, G. T., & Hung, N. V. (2013). Efficiency and super-efficiency of commercial banks in Vietnam: Performances and determinants. Asia-Pacific Journal of Operational Research, 30(1), 1- 19.
Momeni, M., Maleki, M. H., Afshari, M. A., Moradi, J. S., & Mohammadi, J. (2011). A fuzzy MCDM approach for evaluating listed private banks in Tehran stock exchange based on balanced scorecard. International Journal of Business Administration, 2(1), 80-97.
Mous, L. (2005). Predicting bankruptcy with discriminant analysis and decision tree using financial ratios. Working Paper Series, University of Rotterdam.
Njoku, J. (2011). Anatomic assessment of CAMEL in Nigerian banking. International Journal of Economics and Accounting, 2(1), 76-99. https://doi.org/10.1504/IJEA.2011.038964
Njoku, J., & Inanga, E.L. (2012). Underlying nature of the 2008-2009 banking crises. African Journal of Accounting, Auditing and Finance, 1(2), 190-208.
Ohlson, J. A. (2001). Earnings, book values, and dividends in equity valuation: An empirical perspective. Contemporary accounting research, 18(1), 107-120.
Önder, E., & Hepşen, A. (2013). Combining time series analysis and multi criteria decision making techniques for forecasting financial performance of banks in turkey. International Journal of Latest Trends in Financial & Economic Sciences, 3(3), 530-555.
Pal, M., & Choudhury, K. (2009). Exploring the dimensionality of service quality: An application of TOPSIS in the Indian banking industry. Asia-Pacific Journal of Operational Research, 26(1), 115- 133
Penman, S. H. (1996). The articulation of price-earnings ratios and market-to-book ratios and the evaluation of growth. Journal of accounting research, 235-259.
Poghosyan, T., & Cihák, M. (2011). Distress in European banks: an analysis based on a new dataset. Journal of Financial Services Research, 40 (3), 163–184.
Popovska, J. (2014). Modelling financial stability: The case of the banking sector in Macedonia. Journal of Applied Economics and Business, 2(1), 68-91.
Said, M., & Saucier, P. (2003). Liquidity, solvency, and efficiency: An empirical analysis of the Japanese banks’ distress. Journal of Oxford, 5(3), 354-358.
Samir, D., & Kamra, D. (2013). A comparative analysis of non-performing assets (NPAs) of selected commercial banks in india. Opinion: International Journal of Management, 3(1), 68-80. Available at SSRN: https://ssrn.com/abstract=2629587
Sarker, A. (2005). CAMELS rating system in the context of islamic banking: A proposed ‘S’ for Shariah framework. Journal of Islamic Economics and Finance, 1(1), 78-84.
Sayed, G. J., & Sayed, N. S. (2013). Comparative analysis of four private sector banks as per CAMEL rating. Business Perspectives & Research, 1(2), 31-46.
Shannon, C.E. (1948). The mathematical theory of communication. Bell System Technical Journal, 27, 379423
Shaverdi, M., Akbari, M., & Tafti, S.F. (2011). Combining fuzzy MCDM with BSC approach in performance evaluation of Iranian private banking sector. Advances in Fuzzy Systems, 12, 12-27
Stankevičienė, J., & Mencaitė, E. (2012). The evaluation of bank performance using a multicriteria decision making model: a case study on Lithuanian commercial banks. Technological and Economic Development of Economy, 18(1), 189-205
Toloie-Eshlaghy, A., Ghafelehbashi, S., & Alaghebandha, M. (2011). An investigation and ranking public and private islamic banks using dimension of service quality (SERVQUAL) based on TOPSIS fuzzy technique. Applied Mathematical Sciences, 5, 3031 – 3049.
Wu, H.Y., Tzeng, G.H., & Chen, Y.H. (2009). A fuzzy MCDM approach for evaluating banking performance based on balanced scorecard. Expert Systems with Applications, 36, 10135-10147
www.moneycontrol.com
www.rbi.org.in
www.corporatefinanceinstitute.com
https://www.valueresearchonline.com
https://www.morningstar.in/
Akkoç, S., & Vatansever, K. (2013). Fuzzy performance evaluation with AHP and TOPSIS methods: Evidence from Turkish banking sector after the global financial crisis. Eurasian Journal of Business and Economics, 6 (11), 53-74.
Albayrak, E., & Erensal, Y. C. (2005). A study bank selection decision in Turkey using the extended fuzzy AHP method. In 35th International conference on computers and industrial engineering, Istanbul, Turkey.
Altman, I. E. (1968). Financial ratios, discriminant analysis and prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Amile, M., Sedaghat, M., & Poorhossein, M. (2013).Performance evaluation of banks using fuzzy AHP and TOPSIS case study: state-owned banks, partially private and private banks In Iran. Caspian Journal of Applied Sciences Research, 3, 128-138.
Amirzadeh, R., & Shoorvarzy, M.R. (2013). Prioritizing service quality factors in Iranian Islamic banking using a fuzzy approach. International Journal of Islamic and Middle Eastern Finance and Management, 6(1), 64-78.
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
Ayadi, O.F., Adebayo, A. O., & Omolehinwa, E. (1998). Bank performance measurement in a developing economy: An application of data envelopment analysis. Managerial Finance, 24(7), 5-16.
Baležentis, A., Baležentis, T., & Misiunas, A. (2012). An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy mcdm methods. Technological and Economic Development of Economy, 18(1), 34-53.
Bayyurt, N. (2013). Ownership effect on bank's performance: Multi criteria decision making approaches on foreign and domestic Turkish banks. Procedia - Social and Behavioral Sciences 99, 919 - 928. https://doi.org/10.1016/j.sbspro.2013.10.565
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 5, 71-111.
Beaver, W. H. (1968). Market prices, financial ratios, and the prediction of failure. Journal of Accounting Research, 6(2), 179-192.
Bhattacharyay, B.N. (2011). Macro-prudential monitoring of financial crisis: An empirical framework. in Chatterji, M., Gopal, D., and Singh, S. (eds.) Governance, Development and Conflict (Contributions to Conflict Management, Peace Economics and Development, Emerald Group Publishing Limited, 18, 71-121.
Celen, A. (2014). Evaluating the financial performance of Turkish banking sector: A fuzzy mcdm approach. Journal of Economic Cooperation and Development, 35(2), 43-70.
Chatterjee, D., Chowdhury, S., & Mukherjee, B. (2010). A study of the application of fuzzy analytical hierarchical process (FAHP) in the ranking of Indian banks. International Journal of Engineering Science and Technology, 7, 2511-2520.
Dash, M., & Das, A. (2013). Performance appraisal of Indian banks using CAMELS rating. IUP Journal of Bank Management, 12(2), 31-42.
Dash, M., & Charles, C. (2012). An Analysis of the technical efficiency of banks in India. IUP Journal of Bank Management, 11(4), 100-109.
Dash, M., & Vegesna, S. (2014). Efficiency of public and private sector banks in India. Journal of Applied Management and Investments, 3(3), 183-187.
Datta Chaudhuri, T., & Ghosh, I. (2015). Application of multi-criteria decision making models in regulatory evaluation of commercial banks in India and its consistency with public perception. Available at SSRN: https://ssrn.com/abstract=2546647 or http://dx.doi.org/10.2139/ssrn.2546647
De young, R., Flannery, M.J., Lang, W.W., & Sorescu, S.M. (2001). The information content of bank exam ratings and subordinated debt prices. Journal of Money, Credit and Banking, 33 (4), 900-925.
Doumpos, M., & Zopounidis, C. (2015). A multicriteria approach to bank rating. In Bisdorf, R., Dias (eds), Evaluation and Decision Models with Multiple Criteria (pp. 563-587). Springer, Berlin, Heidelberg.
Doumpos, M., & Zopounidis, C. (2013). Efficiency and performance evaluation of European cooperative banks. in Efficiency and Productivity Growth: Modelling in the Financial Services Industry, ed. Pasiouras, F., John Wiley & Sons, Ltd, Chichester, UK.
Faber, V. (1994). Clustering and the continuous k-means algorithm. Los Alamos Science, 22(138144.21).
Fallah, M., Aryanezhadb, M.B., Najafi, S.E., & Shahsavaripour, N. (2011). An empirical study on measuring the relative efficiency using DEA method: A case study of bank industry. Management Science Letters, 1(1), 49-56. https://doi.org/10.5267/j.msl.2010.01.005
Fairfield, P. M. (1994). P/E, P/B and the present value of future dividends. Financial Analysts Journal, 50(4), 23-31.
Foster, E. M. (1970). Price-Earnings Ratio and corporate growth. Financial Analysts Journal, 26(1), 96-99.
Ghorabaee, M.K., Zavadskas, E.K., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research, 50(3), 25-41.
Ginevičius, R., & Podviezko, A. (2013). The evaluation of financial stability and soundness of lithuanian banks. Ekonomska Istraživanja-Economic Researc, 26(2), 191-208.
Gowri, C.A., & Malepati, V. (2017). Evaluation of financial performance of selected banks. Decision, 44(1), 3-14.
Grigoroudis, E., Tsitsiridi, E., & Zopounidis, C. (2013). Linking customer satisfaction, employee appraisal, and business performance: An evaluation methodology in the banking sector. Annals of Operations Research, 205(1), 5-27.
Gupta, R. (2008). A CAMEL model analysis of private sector banks in India. Journal of Gyan Management, 2(1), 3-8.
Halkos, G., & Salamouris, D. (2004). Efficiency measurement of Greek commercial banks with the use of financial ratios: A Data Envelopment Analysis approach. Management Account Research 15, 201-224. https://doi.org/10.1016/j.mar.2004.02.001
Hays, F. H., De Lurgio, S. A., & Gilbert, A. H. (2009). Efficiency ratios and community bank performance. Journal of Finance and Accountancy, 1, 1–15.
Ho, C.B., & Wu, D.D. (2009). Online banking performance evaluation using data envelopment analysis and principal component analysis. Computers & Operations Research, 36, 1835—1842
Kao, C., & Liu, S.-T. (2004). Predicting bank performance with financial forecasts: A case of Taiwan commercial banks. Journal of Banking and Finance, 28(10), 2353-2368. https://doi.org/10.1016/j.jbankfin.2003.09.008
Kaplan, R. S., & Norton, D. P. (1996). The Balanced Scorecard: Translating Strategy into Action. Harvard Business Press.
Klomp, J., & de Haan, J. (2011). Banking risk and regulation: Does one size fit all? DNB Working Paper No.323, available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=197723.
Kumar, P.R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques: a review. European Journal of Operational Research, 180 (1), 1–28.
Kwan, S., & Eisenbeis, R. A. (1997). Bank risk, capitalization, and operating efficiency. Journal of Financial Services Research, 12(2/3), 117-131.
Li, X., Wang, K., Liu, L., Xin, J., Yang, H., & Gao, C. (2011). Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Engineering, 26, 2085-2091.
Maishanu, M. M. (2004). A univariate approach to predicting failure in the commercial banking sub-sector. Nigerian Journal of Accounting Research, 1(1), 70-84.
Maliszewski, K. (2009). Measuring Stability of the Polish financial system by means of a synthetic index. Presented at the 12th International Conference on Finance and Banking, held on Oct. 28-29, 2009, organised by Silesian University in Opava.
Marie, A., Al-Nasser, A., & brahim, M. (2013). Operational-profitability-quality performance of Dubai's banks. Journal of Management Research, 13(1), 25-34.
Minh, N. K., Long, G. T., & Hung, N. V. (2013). Efficiency and super-efficiency of commercial banks in Vietnam: Performances and determinants. Asia-Pacific Journal of Operational Research, 30(1), 1- 19.
Momeni, M., Maleki, M. H., Afshari, M. A., Moradi, J. S., & Mohammadi, J. (2011). A fuzzy MCDM approach for evaluating listed private banks in Tehran stock exchange based on balanced scorecard. International Journal of Business Administration, 2(1), 80-97.
Mous, L. (2005). Predicting bankruptcy with discriminant analysis and decision tree using financial ratios. Working Paper Series, University of Rotterdam.
Njoku, J. (2011). Anatomic assessment of CAMEL in Nigerian banking. International Journal of Economics and Accounting, 2(1), 76-99. https://doi.org/10.1504/IJEA.2011.038964
Njoku, J., & Inanga, E.L. (2012). Underlying nature of the 2008-2009 banking crises. African Journal of Accounting, Auditing and Finance, 1(2), 190-208.
Ohlson, J. A. (2001). Earnings, book values, and dividends in equity valuation: An empirical perspective. Contemporary accounting research, 18(1), 107-120.
Önder, E., & Hepşen, A. (2013). Combining time series analysis and multi criteria decision making techniques for forecasting financial performance of banks in turkey. International Journal of Latest Trends in Financial & Economic Sciences, 3(3), 530-555.
Pal, M., & Choudhury, K. (2009). Exploring the dimensionality of service quality: An application of TOPSIS in the Indian banking industry. Asia-Pacific Journal of Operational Research, 26(1), 115- 133
Penman, S. H. (1996). The articulation of price-earnings ratios and market-to-book ratios and the evaluation of growth. Journal of accounting research, 235-259.
Poghosyan, T., & Cihák, M. (2011). Distress in European banks: an analysis based on a new dataset. Journal of Financial Services Research, 40 (3), 163–184.
Popovska, J. (2014). Modelling financial stability: The case of the banking sector in Macedonia. Journal of Applied Economics and Business, 2(1), 68-91.
Said, M., & Saucier, P. (2003). Liquidity, solvency, and efficiency: An empirical analysis of the Japanese banks’ distress. Journal of Oxford, 5(3), 354-358.
Samir, D., & Kamra, D. (2013). A comparative analysis of non-performing assets (NPAs) of selected commercial banks in india. Opinion: International Journal of Management, 3(1), 68-80. Available at SSRN: https://ssrn.com/abstract=2629587
Sarker, A. (2005). CAMELS rating system in the context of islamic banking: A proposed ‘S’ for Shariah framework. Journal of Islamic Economics and Finance, 1(1), 78-84.
Sayed, G. J., & Sayed, N. S. (2013). Comparative analysis of four private sector banks as per CAMEL rating. Business Perspectives & Research, 1(2), 31-46.
Shannon, C.E. (1948). The mathematical theory of communication. Bell System Technical Journal, 27, 379423
Shaverdi, M., Akbari, M., & Tafti, S.F. (2011). Combining fuzzy MCDM with BSC approach in performance evaluation of Iranian private banking sector. Advances in Fuzzy Systems, 12, 12-27
Stankevičienė, J., & Mencaitė, E. (2012). The evaluation of bank performance using a multicriteria decision making model: a case study on Lithuanian commercial banks. Technological and Economic Development of Economy, 18(1), 189-205
Toloie-Eshlaghy, A., Ghafelehbashi, S., & Alaghebandha, M. (2011). An investigation and ranking public and private islamic banks using dimension of service quality (SERVQUAL) based on TOPSIS fuzzy technique. Applied Mathematical Sciences, 5, 3031 – 3049.
Wu, H.Y., Tzeng, G.H., & Chen, Y.H. (2009). A fuzzy MCDM approach for evaluating banking performance based on balanced scorecard. Expert Systems with Applications, 36, 10135-10147
www.moneycontrol.com
www.rbi.org.in
www.corporatefinanceinstitute.com
https://www.valueresearchonline.com
https://www.morningstar.in/