Financial Assessment of Banks and Financial Institutes in Stock Exchange by Means of an Enhanced Two stage DEA Model
محورهای موضوعی : Financial Mathematics
Mohammad Izadikhah
1
(Department of Mathematics, College of Science, Arak Branch, Islamic Azad University, Arak, Iran
P. O. Box: 38135/567)
کلید واژه: Financial Assessment, Two-stage DEA, Financial ratios, stock exchange, Efficiency,
چکیده مقاله :
A stock exchange is an entity which provides ‘‘trading’’ facilities for stock brokers and traders to trade stocks and other securities. How to invest in stock exchange is one of the important issues in investment, and one of the factors that can help investors in the process of investment is the efficiency of the corporation under consideration. Data envelopment analysis is a mathematical methodology that has been widely applied to assess the performance of banks and financial institutes. The main feature of this method is that this methodology evaluate firms by considering multiple inputs and outputs. Conventional DEA models consider each firms as black box and don’t note into the inner activities. Two-stage data envelopment analysis has been researched by a number of authors that evaluate each firm by considering the inner operations. This paper proposes a new two stage BAM model and further evaluates the banks and financial institutes in Tehran stock exchange by considering the financial ratios. Conventional DEA models consider each firms as black box and don’t note into the inner activities. Two-stage data envelopment analysis has been researched by a number of authors that evaluate each firm by considering the inner operations. This paper proposes a new variant of two stage DEA models and further evaluates the banks and financial institutes in Tehran stock exchange by considering the financial ratios.
[1] Ramezanian, R., A. Peymanfar, and S.B. Ebrahimi 2019 An integrated framework of genetic network programming and multi-layer perceptron neural network for prediction of daily stock return: An application in Tehran stock exchange market Applied Soft Computing 82: 105551.
[2] Jalilvand, A., M.R. Noroozabad, and J. Switzer 2018 Informed and uninformed investors in Iran: Evidence from the Tehran Stock Exchange Journal of Economics and Business 95: 47-58.
[3] Shahrestani, P. and M. Rafei 2020 The impact of oil price shocks on Tehran Stock Exchange returns: Application of the Markov switching vector autoregressive models Resources Policy 65: 101579.
[4] Khoshroo, A., M. Izadikhah, and A. Emrouznejad 2018 Improving energy efficiency considering reduction of CO2 emission of turnip production: A novel data envelopment analysis model with undesirable output approach Journal of Cleaner Production 187: 605-615.
[5] Tone, K., M. Toloo, and M. Izadikhah 2020 A modified slacks-based measure of efficiency in data envelopment analysis European Journal of Operational Research 287: 560-571.
[6] Sherman, H.D. and F. Gold 1985 Bank branch operating efficiency Journal of Banking and Finance 9: 297-315.
[7] Ohsato, S. and M. Takahashi 2015 Management Efficiency in Japanese Regional Banks: A Network DEA Procedia - Social and Behavioral Sciences 172: 511-518.
[8] Herrera-Restrepo, O., et al. 2016 Bank branch operational performance: A robust multivariate and clustering approach Expert Systems with Applications 50: 107-119.
[9] Diallo, B. 2017 Bank efficiency and industry growth during financial crises Economic Modelling.
[10] Zhu, N., et al. 2019 Ranking Chinese commercial banks based on their expected impact on structural efficiency Omega 102049.
[11] Aliakbarpoor, Z. and M. Izadikhah 2012 Evaluation and ranking DMUs in the presence of both undesirable and ordinal factors in data envelopment analysis Int. J. Autom. Comput. 9: 609-615.
[12] Izadikhah, M. and R. Farzipoor Saen 2015 A new data envelopment analysis method for ranking decision making units: an application in industrial parks Expert Systems 32: 598-608.
[13] Charnes, A., W.W. Cooper, and E. Rhodes 1978 Measuring the efficiency of decision making units European Journal of Operational Research 2: 429-444.
[14] Izadikhah, M. and R. Farzipoor Saen 2016 Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data Transportation Research Part D: Transport and Environment 49: 110-126.
[15] Izadikhah, M. and R. Farzipoor Saen 2016 A new preference voting method for sustainable location planning using geographic information system and data envelopment analysis Journal of Cleaner Production 137: 1347-1367.
[16] Cooper, W.W., et al. 2011 BAM: A Bounded Adjusted Measure of Efficiency for use with Bounded Additive Models Journal of Productivity Analysis 35: 85-94.
[17] Kao, C. and S.-N. Hwang 2008 Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan European Journal of Operational Research 185: 418-429.
[18] Li, Y., et al. 2012 DEA models for extended two-stage network structures Omega 40: 611–618.
[19] Avkiran, N.K. 2015 An illustration of dynamic network DEA in commercial banking including robustness tests Omega 55: 141-150.
[20] Sánchez-González, C., J.L. Sarto, and L. Vicente 2017 The efficiency of mutual fund companies: Evidence from an innovative network SBM approach Omega 71: 114-128.
[21] Tavana, M., et al. 2018 A new dynamic range directional measure for two-stage data envelopment analysis models with negative data Computers & Industrial Engineering 115: 427-448.
[22] Yin, P., et al. 2019 A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective Omega In press.
[23] Avkiran, N.K. 2006 Developing foreign bank efficiency models for DEA grounded in finance theory Socio-Economic Planning Sciences 40: 275-296.
[24] Toloo, M. and A. Kresta 2014 Finding the best asset financing alternative: A DEA–WEO approach Measurement 55: 288-294.
[25] Chang, Y.-T., et al. 2016 Passenger facility charge vs. airport improvement program funds: A dynamic network DEA analysis for U.S. airport financing Transportation Research Part E: Logistics and Transportation Review 88: 76-93.
[26] Esfandiar, M., M. Saremi, and H. Jahangiri Nia 2018 Assessment of the efficiency of banks accepted in Tehran Stock Exchange using the data envelopment analysis technique %J Advances in Mathematical Finance and Applications 3: 1-11.
[27] Izadikhah, M. 2018 Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model %J Advances in Mathematical Finance and Applications 3: 27-41.
[28] Goyal, J., et al. 2019 Efficiency and technology gaps in Indian banking sector: Application of meta-frontier directional distance function DEA approach The Journal of Finance and Data Science 5: 156-172.
[29] Peykani, P., et al. 2019 Fuzzy Data Envelopment Analysis Approach for Ranking of Stocks with an Application to Tehran Stock Exchange %J Advances in Mathematical Finance and Applications 4: 31-43.
[30] Wasiaturrahma, et al. 2020 Financial performance of rural banks in Indonesia: A two-stage DEA approach Heliyon 6: e04390.
[31] Mohsin, M., et al. 2020 Developing Low Carbon Finance Index: Evidence From Developed and Developing Economies Finance Research Letters 101520.
[32] Henriques, I.C., et al. 2020 Two-stage DEA in banks: Terminological controversies and future directions Expert Systems with Applications 161: 113632.
[33] Shuai, S. and Z. Fan 2020 Modeling the role of environmental regulations in regional green economy efficiency of China: Empirical evidence from super efficiency DEA-Tobit model Journal of Environmental Management 261: 110227.
[34] Li, H., X. Zhu, and J. Chen 2020 Total factor waste gas treatment efficiency of China’s iron and steel enterprises and its influencing factors: An empirical analysis based on the four-stage SBM-DEA model Ecological Indicators 119: 106812.
[35] Wanke, P., et al. 2020 Dynamic network DEA and SFA models for accounting and financial indicators with an analysis of super-efficiency in stochastic frontiers: An efficiency comparison in OECD banking International Review of Economics & Finance 69: 456-468.
[36] Cooper, W., K. Park, and J. Pastor 1999 RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA Journal of Productivity Analysis 11: 5-42.
[37] Pastor, J.T., et al. 2015 An enhanced BAM for unbounded or partially bounded CRS additive models Omega 56: 16-24.
[38] Rashidi, K. and R. Farzipoor Saen 2015 Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement Energy Economics 50: 18-26.
[39] Haghighi, H.Z. and M. Rostamy-Malkhalifeh 2017 A bounded adjusted measure of efficiency for evaluating environmental performance International Journal of Environment and Waste Management 19: 148-163.
[40] Qin, Q., et al. 2018 Unified energy efficiency in China's coastal areas: A virtual frontier-based global bounded adjusted measure Journal of Cleaner Production 186: 229-240.
[41] Ebrahimnejad, A., et al. 2014 A three-stage Data Envelopment Analysis model with application to banking industry Measurement 49: 308-319.
[42] Kleindorfer, P.R., H.C. Kunreuther, and P.J.H. Schoemaker 1993 Decision Sciences: An Integrative Perspective New York: Cambridge University Press.
[43] Saaty, T.L. 2006 Rank from comparisons and from ratings in the analytic hierarchy/network processes European Journal of Operational Research 168: 557-570.
[44] Jahangoshai Rezaee, M., M. Jozmaleki, and M. Valipour 2018 Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange Physica A: Statistical Mechanics and its Applications 489: 78-93.
[45] Rezaie, K., et al. 2013 Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: a case study of Tehran stock exchange Neural Computing and Applications 23: 1-17.
[46] Mohtashami, A. and B.M. Ghiasvand 2020 Z-ERM DEA integrated approach for evaluation of banks & financial institutes in stock exchange Expert Systems with Applications 147: 113218.
[47] Izadikhah, M., Khoshroo, A., Energy management in crop production using a novel Fuzzy Data Envelopment Analysis model, RAIRO - Operations Research, 2017, 52 (2), P. 595 – 617, Doi: 10.1051/ro/2017082
[48] Sudarsanam, P.S. and R.J. Taffler 1995 Financial ratio proportionality and inter-temporal stability: An empirical analysis Journal of Banking & Finance 19: 45-60.
[49] Izadikhah, M., Using goal programming method to solve DEA problems with value judgments, Yugoslav Journal of Operations Research, 2016, 24 (2), P. 267 – 282, Doi: 10.2298/YJOR121221015I
[50] Lewellen, J. 2004 Predicting returns with financial ratios Journal of Financial Economics 74: 209-235.
[51] Dibachi, H., Behzadi. MH, Izadikhah, M., Stochastic Modified MAJ Model for Measuring the Efficiency and Ranking of DMUs, Indian Journal of Science and Technology, 2015, 8 (8), P. 549–555, Doi: 10.17485/ijst/2015/v8iS8/71505
[52] Arnold, T., et al. 2019 Linear Smoothers. A Computational Approach to Statistical Learning London, United Kingdom: Taylor & Francis Ltd.
