Two Stage Fuzzy Methodology to Evaluate the Credit Risks of Investment Projects

Authors: O. Badagadze, G. Sirbiladze, I. Khutsishvili

Abstract:

The work proposes a decision support methodology for the credit risk minimization in selection of investment projects. The methodology provides two stages of projects’ evaluation. Preliminary selection of projects with minor credit risks is made using the Expertons Method. The second stage makes ranking of chosen projects using the Possibilistic Discrimination Analysis Method. The latter is a new modification of a well-known Method of Fuzzy Discrimination Analysis.

Keywords: Expert valuations, expertons, investment project risks, positive and negative discriminations, possibility distribution.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096427

References:

[1] R. E. Bellman and L. A. Zadeh, "Decision-Making in a Fuzzy Environment”, Management Science, 17(4), 1970, B-141-B-164.
[2] D. Dubois and H. Prade, Possibility Theory: An Approach to Computerized Processing of Uncertainty. New-York: Plenum Press, 1988.
[3] A. Kaufmann, "Theory of expertons and fuzzy logic”, Fuzzy Sets and Systems, 28(3), 1988, 295-304.
[4] A. Kaufmann, Expert Appraisements and Counter-Appraisements with Experton Processes, Analysis and Management of Uncertainty: Theory and Applications, North Holland, Amsterdam, 1992.
[5] G. J. Klir and M. J. Wierman, Uncertainty-Based Information. Elements of Generalized Information Theory. Second edition. Studies in Fuzziness and Soft Computing, Physica-Verlag, Heidelberg, 1999.
[6] I. Khutsishvili, "The Combined Decision Making Technology based on the Statistical and Fuzzy Analysis and its Application in Forecast’s Modeling”, WSEAS Transactions on Systems, 8(7), 2009, 891-901.
[7] I. Khutsishvili and G. Sirbiladze, "Decision Support’s Precising Technology in the Investment Project Risk Management”, In: Proceedings of the 11th WSEAS International Conference on Automatic Control, Modelling and Simulation (ACMOS ‘09), Istanbul, Turkey, 2009, 303-311.
[8] B. Liu, "Toward fuzzy optimization without mathematical ambiquity”, Fuzzy Optimization and Decision Making, 1(1), 2002, 43-63.
[9] D. Norris, B. W. Pilsworth and J. F. Baldwin, "Medical Diagnosis from patient records – A method using fuzzy discrimination and connectivity analysis”, Fuzzy Sets and Systems, 23, 1987, 73-87.
[10] D. Ruan, J. Kacprzyk and M. Fedrizzi, Soft Computing for Risk Evaluation and Management: Applications in Technology, Environmenf and Finance. Studies in Fuzziness and Soft Computing. Heidelberg, New York: Physica-Verlag, 2001.
[11] G. Sirbiladze, A. Sikharulidze and G. Korakhashvili, "Decision-making Aiding Fuzzy Informational Systems in Investments. Part I – Discrimination Analysis in Investment Projects”, Proceedings of Iv. Javakhishvili Tbilisi State University. Applied Mathematics and Computer Sciences, 353 (22-23), 2003, 77-94.
[12] G. Sirbiladze and T. Gachechiladze, "Restored fuzzy measures in expert decision-making ”, Information Sciences, 169 (1/2), 2005, 71-95.
[13] G. Sirbiladze and A. Sikharulidze, "Weighted Fuzzy Averages in Fuzzy Environment, Parts I, II”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(2), 2003, 139-157, 159- 172.
[14] G. Sirbiladze, "Decision Precising Technologies in Decision Making Systems”, Plenary Speech. In: N. Mastorakis, M. Demiralp, I. Rudas, C. A. Bulucea, L. Rogozea (Eds.), Proc.of the Applied Computing Conference 2009, ACC '09, Vouliagmeni, Athens, Greece, 2009, pp. 27- 28.
[15] G. Sirbiladze, A. Sikharulidze and N. Sirbiladze, "Generalized Weighted Fuzzy Expected Values in Uncertainty Environment”, In: L. A. Zadeh, J. Kacprzyk, N. Mastorakis, A. Kuri-Morales, P. Borne, L. Kazovsky (Eds.), Proc. 9th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, AIKED '10, University of Cambridge, UK, 2010, pp. 59-65.
[16] G. Sirbiladze, "Fuzzy dynamic programming problem for extremal fuzzy dynamic systems”, In: W. A. Lodwick and J. Kacprzyk (Eds.) Fuzzy Optimization: Recent Developments and Applications, Studies in Fuzziness and Soft Computing, 254, Heidelberg: Physica-Verlag, 231- 270, 2010.
[17] G. Sirbiladze, "Fuzzy Identification Problem for Continuous Extremal Fuzzy Dynamic System”, Fuzzy Optimization and Decision Making, 9 (3), 2010, 233-274.
[18] G. Sirbiladze, I. Khutsisvili and P. Dvalishvili, "Decision Precising Fuzzy Technology to Evaluate the Credit Risks of Investment Projects”, IEEE 10-th International Conference on Intelligent Systems Desigh and Applications (ISDA 2010), Cairo, Egypt, 2010, 103-108.
[19] G.Sirbiladze, I. Khutsishvili and B. Ghvaberidze, "Multistage decisionmaking fuzzy methodology for optimal investments based on experts’ evaluations”, European Journal of Operational Research, 232(1), 2014, 169–177.
[20] D. Wu, Y. Zhang, D. Wu and D. L. Olson, "Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach”, European Journal of Operational Research, 200(3), 2010, 774-787
[21] R. R. Yager, "On the Evalution of Uncertain Courses of Action”, Fuzzy Optimization and Decision Making, 1(1), 2002, 13-41.
[22] R. R.Yager, "Aggregation of ordinal information”, Fuzzy Optimization and Decision Making, 6(3), 2007, 199-219.
[23] L. Yu, S. Wang and K. K. Lai, "An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring”, European Journal of Operational Research, 195(3), 2009, 942-959.