Designing a multi-period credit portfolio optimization model a nonlinear multi-objective fuzzy mathematical modeling approach (Case study: Ansar banks affiliated to Sepah Bank)

Document Type : Research Paper


1 Department of Financial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran

2 Department of Financial Management, Alzahra University, Tehran, Iran

3 Department of Accounting, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran


This study aims to design a multi-period credit portfolio optimization model with a nonlinear multi-objective fuzzy mathematical modeling approach. In terms of data collection, this study is a descriptive-survey research and in terms of the nature and purpose of the research, it is an applied one. The statistical population of the research includes all facility files of the last 10 years as well as the statements of financial position of Ansar Bank branches affiliated with Sepah Bank, selected by census method. The risk criteria used in the models include Average Value at Risk (AVaR), Conditional Value at Risk (CVAR) and Semi-Entropy. First, having reviewed the research literature, the objectives and indices of the portfolio optimization issue were investigated based on the practical character of this issue and the main indices were selected. Then, each of the objectives and constraints were specified in a state of uncertainty and ambiguity, based on the principles of fuzzy credibility theory, for a state in which the expected rate of stock return is a triangular fuzzy number. Finally, three multi-objective fuzzy models were designed based on the selected criteria. Research models were implemented using MOPSO algorithm. The software used in conducting the research was MATLAB software. The results indicated that the CVAR model performed better than the other two models, i.e. AVAR and Semi-Entropy, in evaluating optimal portfolios.


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Volume 12, Issue 1
May 2021
Pages 1261-1277
  • Receive Date: 11 December 2020
  • Revise Date: 05 March 2021
  • Accept Date: 15 March 2021