Quarterly Publication

Document Type : Original Article


Department of Accounting, Chalous Branch, Islamic Azad University, Chalous, Iran.



Banks as financial and service-provider institutions in a society have a decisive role in the circulation of money and wealth of a country, so they have a special place in the economy of each country. Considering the importance of lending in quality of banks return and econom-ic decisions and increasing competition in banks, analysis and research in this area is neces-sary. Accordingly, in the present study, a data envelopment analysis model was used to evaluate and select the optimal portfolio of banks' listed loans in Tehran Stock Exchange for 2017. Bank loans were evaluated using data envelopment analysis method and based on a set of banking criteria whith the use Gams software. In this method, banks are ranked based on the highest score and then the most efficient banks are selected. Finally, in order to evaluate the effectiveness and usefulness of the proposed method, a case study has been used in Iranian banks, which the results of performance evaluation of each type of loans and bank has been extracted. The results of the present study indicated that out of all the lending, Pasar-gad, Parsian and Saman Banks had the best performance and the Sarmayeh, Dey and Gardeshgari Banks had the poorest performance.


  1. Agarana, M. C., Bishop, S. A., & Odetunmibi, O. (2014). Optimization of banks loan portfolio management using goal programming technique. International journal of research in applied natural and social sciences (IMPACT: IJRANSS), 2(8), 43-52.
  2. Azizi, S. M. E. P., & Neisy, A. (2017). Mathematic modelling and optimization of bank asset and liability by using fractional goal programing approach. International journal of modeling and optimization7(2), 85-91. DOI: 7763/IJMO.2017.V7.564
  3. Baharvandi, A., Ranjbar Fallah, M. R., Abolhasani Hastiani, A., (2016). Determining the relation between the problem of non-performing loans and riba -free banking in Iran. Islamic financial research, 5(2), 39-74. (In Persian). https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=602805
  4. Bozorg Asl, M., Akbari Masule, A., Mohaghegh Nia, M., & Taqhavi Fard, M. (2017). Investigating the impact of diversification strategy in assets and loans on bank return (case study: private banks in Iran). Financial engineering and securities management, 8(30), 201-212. (In Persian). http://fej.iauctb.ac.ir/article_529588.html?lang=en
  5. Boďa, M., Dlouhý, M., & Zimková, E. (2020). Modeling a shared hierarchical structure in data envelopment analysis: an application to bank branches. Expert systems with applications162, 113700. https://doi.org/10.1016/j.eswa.2020.113700
  6. Brei, M., & Schclarek, A. (2015). A theoretical model of bank lending: does ownership matter in times of crisis?. Journal of banking & finance50, 298-307. https://doi.org/10.1016/j.jbankfin.2014.03.038
  7. Fekri, R., Amiri, M., Sajjad, R., & Golestaneh, R. (2016). Optimization of bank portfolio investment decision considering resistive economy. Journal of money and economy11(4), 375-400. http://jme.mbri.ac.ir/article-1-263-en.html
  8. Firouzdehghan, M., Saeidi, H., Mohammadi, S., & Elahi, G. (2019). Portfolio choice with high frequency data: constant relative risk aversion preferences and the liquidity effect. Financial engineering and securities management, 10(38), 180-214. (In Persian). http://fej.iauctb.ac.ir/article_664737.html
  9. Fitrianti, R., & Nurbayani, S. U. (2021). The efficiency of Islamic banks and conventional banks in Indonesia using data envelopment analysis approach. Psychology and education journal58(1), 375-381. https://doi.org/10.17762/pae.v58i1.784
  10. Hadi Nejad, M., Nazarian, R., & Piri, F. (2013). Investigating the performance of public and private banks based on e-banking indicators using data envelopment analysis (DEA) method. Financial economics, 7(23), 177-202. (In Persian). http://ecj.iauctb.ac.ir/article_512495_4c508726c900274de2f1562d25836b71.pdf
  11. Jat, D. S., & Xoagub, A. J. (2016). Fuzzy logic-based expert system for assessment of bank loan applications in Namibia. Proceedings of the international congress on information and communication technology (pp. 645-652). Springer, Singapore. https://doi.org/10.1007/978-981-10-0755-2_67
  12. Louzis, D. P., Vouldis, A. T., & Metaxas, V. L. (2012). Macroeconomic and bank-specific determinants of non-performing loans in Greece: a comparative study of mortgage, business and consumer loan portfolios. Journal of banking & finance36(4), 1012-1027. https://doi.org/10.1016/j.jbankfin.2011.10.012
  13. Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm based model for optimizing bank lending decisions. Expert systems with applications80, 75-82. https://doi.org/10.1016/j.eswa.2017.03.021
  14. Moghadam, M. S., Ohadi, F., (2018). Investigation of portfolio matching based on behavioural pattern at mean-variance boundary. Financial engineering and securities management, 9(37), 375-398. (In Persian). http://fej.iauctb.ac.ir/article_663488.html?lang=fa
  1. Ahadzadeh Namin, M., Khamseh, E., & Mohamadi, F. (2019). Evaluate the performance of bank branches using the control approach in analyzing the data cover weight. Financial engineering and securities management, 10(40), 1-28. (In Persian). https://www.sid.ir/fa/journal/ViewPaper.aspx?ID=482429
  2. Shen, W. F., Zhang, D. Q., Liu, W. B., & Yang, G. L. (2016). Increasing discrimination of DEA evaluation by utilizing distances to anti-efficient frontiers. Computers & operations research75, 163-173. https://doi.org/10.1016/j.cor.2016.05.017
  3. Sheskin, D. J. (2003). Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC. https://doi.org/10.1201/9781420036268
  4. Shikh-hasani, D., Alifarri, M., & Karimi, B. (2020). Measuring efficiency score by cross-efficiency method in data envelopment analysis and its relation to profitability and risk in banks admitted to Tehran stock exchange. Management accounting13(46), 103-119. https://jma.srbiau.ac.ir/article_16418_en.html?lang=fa
  5. Sudani, A. (2017). Ranking of banks and financial institutions based on Cummins international indices. Monetary and banking research, 10(31), 141-171. https://jmbr.mbri.ac.ir/article-1-713-fa.pdf
  6. Tan, Y. (2016). The impacts of risk and competition on bank profitability in China. Journal of international financial markets, institutions and money40, 85-110. https://doi.org/10.1016/j.intfin.2015.09.003
  7. Tamatam, R., Dutta, P., Dutta, G., & Lessmann, S. (2019). Efficiency analysis of Indian banking industry over the period 2008–2017 using data envelopment analysis. Benchmarking: an international journal, 26(8), 2417-2442. https://doi.org/10.1108/BIJ-12-2018-0422
  8. Yu, M. M., Lin, C. I., Chen, K. C., & Chen, L. H. (2021). Measuring Taiwanese bank performance: a two-system dynamic network data envelopment analysis approach. Omega98, 102145. https://doi.org/10.1016/j.omega.2019.102145
  9. Vo, X. V. (2018). Bank lending behavior in emerging markets. Finance research letters27, 129-134. https://doi.org/10.1016/j.frl.2018.02.011
  10. Ali Heidari Boyouki, T., & Khademi Zare, H. (2015). Development of data envelopment analysis method in order to cluster banks' credit customers. Journal of modeling in engineering, 13(41), 59-74. (In Persian). https://www.sid.ir/fa/Journal/ViewPaper.aspx?id=255537