Fuzzy Data Envelopment Analysis Approach for Ranking of Stocks with an Application to Tehran Stock Exchange
الموضوعات :
Pejman Peykani
1
(
Faculty of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
)
Emran Mohammadi
2
(
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
)
Mohsen Rostamy-Malkhalifeh
3
(
Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
)
Farhad Hosseinzadeh Lotfi
4
(
Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
)
الکلمات المفتاحية: Fuzzy DEA, Stocks Ranking, Insurance Companies,
ملخص المقالة :
The main goal of this paper is to propose a new approach for efficiency measurement and ranking of stocks. Data envelopment analysis (DEA) is one of the popular and applicable techniques that can be used to reach this goal. However, there are always concerns about negative data and uncertainty in financial markets. Since the classical DEA models cannot deal with negative and imprecise values, in this paper, possibilistic range directional measure (PRDM) model is proposed to measure the efficiencies of stocks in the presence of negative data and uncertainty with input/output parameters. Using the data from insurance industry, this model is also implemented for a real case study of Tehran stock exchange (TSE) in order to analyse the performance of the proposed method.
[1] Charnes, A., Cooper, W.W., Chance-constrained programming, Management Science, 1959, 6(1), P. 73-79. Doi: 10.1287/mnsc.6.1.73.
[2] Charnes, A., Cooper, W.W. and Rhodes, E., Measuring the efficiency of decision making units, European Journal of Operational Research, 1978, 2(6), P. 429-444. Doi: 10.1016/0377-2217(78)90138-8.
[3] Cheng, G., Zervopoulos, P., Qian. Z., A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis, European Journal of Operational Research, 2013, 225(1), P. 100-105. Doi: 10.1016/j.ejor.2012.09.031.
[4] Emrouznejad, A. and Tavana, M., Performance measurement with fuzzy data envelopment analysis. Berlin and Heidelberg: Springer, 2014. Doi: 10.1007/978-3-642-41372-8.
[5] Emrouznejad, A., Anouze, A.L. and Thanassoulis, E., A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA, European Journal of Operational Research, 2010, 200(1), P. 297-304. DOI: 10.1016/j.ejor.2009.01.001.
[6] Esfandiar, M., Saremi, M., Jahangiri Nia, H., Assessment of the efficiency of banks accepted in Tehran Stock Exchange using the data envelopment analysis technique, Advances in Mathematical Finance and Applications, 2018, 3(2), P. 1-11. Doi: 10.22034/amfa.2018.540815.
[7] Farrell, M., The measurement of Productive Efficiency, Journal of the Royal Statistics Society, 1957, 120(3), P. 253-281. Doi: 10.2307/2343100.
[8] Guo, P., Tanaka, H. and Inuiguchi, M., Self-organizing fuzzy aggregation models to rank the objects with multiple attributes, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2000, 30(5), P. 573-580. Doi: 10.1109/3468.867864.
[9] Hatami-Marbini, A., Emrouznejad, A. and Tavana, M., A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making, European Journal of Operational Research, 2011, 214(3), P. 457-472. Doi: 10.1016/j.ejor.2011.02.001.
[10] Hossainzadeh, F., Jahanshahloo, G.R., Kodabakhshi, M. and Moradi, F., A fuzzy chance constraint multi objective programming method in data envelopment analysis, African Journal of Business Management, 2011, 5(33), P. 12873-12881. Doi: 10.5897/ajbm11.1875.
[11] Izadikhah, M., Improving the banks shareholder long term values by using data envelopment analysis model, Advances in Mathematical Finance and Applications, 2018, 3(2), P. 27-41. Doi: 10.22034/amfa.2018 .540829.
[12] Izadikhah, M., and Khoshroo, A., Energy management in crop production using a novel fuzzy data envelopment analysis model, RAIRO-Operations Research, 2018, 52(2), P. 595-617. Doi: 10.1051/ro/2017082.
[13] Jafari, M., and Mousavi, M., Performance analysis and rating of insurance companies using DEA in Iran capital market, Advances in Mathematical Finance and Applications, 2017, 2(3), P. 41-50. Doi: 10.22034/amfa. 2017.533100.
[14] Lertworasirikul, S., Fang, S.C., Joines, J.A. and Nuttle, H.L., Fuzzy data envelopment analysis (DEA): a possibility approach, Fuzzy sets and Systems, 2003, 139(2), P. 379-394. Doi: 10.1016/S0165-0114(02)00484-0.
[15] Lertworasirikul, S., Fang, S.C., Nuttle, H.L. and Joines, J.A., Fuzzy BCC model for data envelopment analysis, Fuzzy Optimization and Decision Making, 2003, 2(4), P. 337-358. Doi: 10.1023/b: fodm.0000003953 .39947. b4.
[16] Lertworasirikul, S., Fang, SC., Joines, J.A. and Nuttle, H.L., Fuzzy data envelopment analysis: A credibility approach, Fuzzy Sets based Heuristics for Optimization, Springer, Berlin, Heidelberg, 2003, P. 141-158. Doi: 10.1007/978-3-540-36461-0_10.
[17] Matin, R.K., Amin, G.R. and Emrouznejad, A., A modified semi-oriented radial measure for target setting with negative data, Measurement, 2014, 54, P. 152-158. Doi: 10.1016/j.measurement.2014.04.018.
[18] Payan, A. and Shariff, M., Scrutiny Malmquist productivity index on fuzzy data by credibility theory with an application to social security organizations, Journal of Uncertain Systems, 2013, 7(1), P. 36-49.
[19] Peykani, P., Mohammadi, E., Pishvaee, M.S., Rostamy-Malkhalifeh, M. and Jabbarzadeh, A., A novel fuzzy data envelopment analysis based on robust possibilistic programming: possibility, necessity and credibility-based approaches, RAIRO-Operations Research, 2018, 52(4), P. 1445-1463. Doi: 10.1051/ro/2018019.
[20] Portela, M.S., Thanassoulis, E. and Simpson, G., Negative data in DEA: A directional distance approach applied to bank branches, Journal of the Operational Research Society, 2004, 55(10), P. 1111-1121. DOI: 10.1057/palgrave.jors.2601768.
[21] Ruiz, J.L. and Sirvent, I., Fuzzy cross-efficiency evaluation: a possibility approach, Fuzzy Optimization and Decision Making, 2017, 16(1), P. 111-126. Doi: 10.1007/s10700-016-9240-1.
[22] Saberi, M., Rostami, M. R., Hamidian, M., and Aghami, N., Forecasting the profitability in the firms listed in Tehran Stock Exchange using data envelopment analysis and artificial neural network, Advances in Mathematical Finance and Applications, 2016, 1(2), P. 95-104. Doi: 10.22034/amfa.2016.527823.
[23] Sharp, J.A., Meng, W. and Liu, W., A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs, Journal of the Operational Research Society, 2007, 58(12), P. 1672-1677. Doi: 10.1057/palgrave.jors.2602318.
[24] Zadeh, L.A., Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, 1978, P. 3-28. Doi: 10.1016/0165-0114(78)90029-5.
