Tải bản đầy đủ (.pdf) (16 trang)

A hybrid unsupervised learning and multi-criteria decision making approach for performance evaluation of Indian banks

Bạn đang xem bản rút gọn của tài liệu. Xem và tải ngay bản đầy đủ của tài liệu tại đây (775.5 KB, 16 trang )

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:
/>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, 379-423
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

/>
© 2019 by the authors; licensee Growing Science, Canada. This is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license ( />


×