A new method for determining bankruptcy using DEA and Rough set theory

Document Type : Original Paper


Faculty of Mathematics, Sistan and Baluchestan University, Zahedan, Iran


In the changing economic conditions and volatility in financial environments in commercial is very important for predict financial performance, One of these is predict financial crisis and bankruptcy assessment. Data envelopment analysis (DEA) is a powerful tool available to managers that Benchmark your company's performance in their business activities. The conventional data envelopment DEA models are to evaluate each DMU optimistically. DEA is evaluation model from the optimistic viewpoint. In fact it will be evaluated in optimistic state. However, other models have been introduced in the DEA to measure the efficiency with which the pessimistic viewpoint of their specific applications such as assessing the failures and bankruptcy. In this paper combine Rough set theory and a new model in DEA about bankruptcy and it measure the efficiency and bankruptcy with establishment of an information system and the use of indicators calculates bankruptcy and efficiency using DEA Rough concepts and Fuzzy Rough. The results of the model in determining bankruptcies reviewed the number of organization.


Main Subjects

[1] Premachandra, I.M., Gurmeeet, S.B. and Toshiyuki S. (2009). DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research, 193, 412-424.
[2] Altman, E.I. (1968). Financial ratios, discriminated analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609.
[3] Dimitras A.I., Susmag R. and Zopounidis C. (1999). Business failure prediction using rough sets. European Journal of Operational Research, 114, 263-280.
[4] Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
[5] Kaufmann, A. (1975). Introduction to the theory of fuzzy subsets. New York, AcademicPress.
[6] Zadeh, L.A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, 3–28.
[7] Nahmias, S. (1978). Fuzzy variables. Fuzzy Sets and Systems, 1, 97–110.
[8] Dubois, D., and Prade, H. (1988a). Fuzzy numbers: An overview, Analysis of Fuzzy Information, 2, 3–39.
[9] Dubois, D., and Prade, H. (1990). Rough fuzzy sets and fuzzy rough sets, International Journal of General Systems, 17, 191–208.
[10] Morsi, N.N., and Yakout, M.M. (1998). Axiomatics for fuzzy rough sets, Fuzzy Sets and Systems, 100, 327–342.
[11] Wang, Y. (2003). Mining stock price using fuzzy rough set system. Expert Systems with Applications, 24, 13–23.
[12] Khanjani Shiraz, R., Vincent, C. and Jalalzadeh, L. (2014). Fuzzy Rough DEA model: A possibility and expected value approaches, Expert Systems with Applications, 41, 434-444.

[13] Leobardo Plata-Pérez and Joss Sánchez-Pérez. (2011). Convexity and marginal contributions in bankruptcy games. EconoQuantum, 8, 61-72.

[14] Dagan, N., and Voliji, O. (1993). the bankruptcy problem: A cooperative bargaining approach. Mathematical Social Sciences, 26, 287-297.
[15] Charnes, A. Cooper, W.W. and Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429-444.
 [16] Chambers, R.G., Chung, Y. and Fare, R. (1998). Profit directional distance function and Nerlovian efficiency. Journal of Optimization and Theory and Application, 12, 233-247
[17] Udaya, S., Pakala T.P.M. and Mallikarjunappa T. (2012). A modified directional distance formulation of DEA to assess bankruptcy: An application to IT/ITES companies in India. Expert Lists with Applications, 39, 1988-1997.
[18] Francis E.H. and Tay, L.S. (2002). Economic and Financial prediction using rough sets model. European journal of Operational Research, 141, 641-659.
[19] Malcolm J., Beynon. M. and Peel, J. (2001). Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega, 29, 561-576.
[20] Jiuping Xu, Bin Li and Desheng Wu. (2009). Rough data envelopment analysis and its application to supply chain performance International Journal of Production Economics. 122, 628-638.
[21] Entani, T., Maeda, Y. and Hideo, T. (2002). Dual models of interval DEA and it’s extension to interval data. European Journal of Operational Research, 136, 32-45.
Volume 6, Issue 1
April 2016
Pages 1-22
  • Receive Date: 10 May 2015
  • Revise Date: 04 April 2016
  • Accept Date: 08 September 2016
  • First Publish Date: 08 September 2016