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Decomposing banking performance into economic and credit risk efficiencies

Author

Listed:
  • Jean-Philippe Boussemart

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Herve Leleu

    (UCL FGES - Université Catholique de Lille - Faculté de gestion, économie et sciences - ICL - Institut Catholique de Lille - UCL - Université catholique de Lille, LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Zhiyang Shen

    (Export‐Import Bank of Chin - Anhui University of Finance and Economics)

  • Michael Vardanyan

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Ning Zhu

Abstract

This paper proposes a non-parametric approach of a banking production technology that decomposes performance into economic and credit risk efficiencies. The basis of our approach is to separate the production technology into two sub-technologies. The former is the production of non-interest income and loans from a set of traditional inputs. The latter is attached to the production of interest income from loans where an explicit distinction between good and non-performing loans is introduced. Economic efficiency comes from the production of good outputs, namely interest and non-interest income, while credit risk management efficiency is related to the minimization of the non-performing loans that can be considered as an unintended or bad output. The model is applied to Chinese financial data covering 30 banks from 2005 to 2012 and different scenarios are considered. The results indicate that income could be increased by an average rate of 16% while non-performing loans could be decreased by an average rate of 33%. According to our results, banking managers could strike a balance between economic performance and credit risk management and make more appropriate decisions in line with their preferences.

Suggested Citation

  • Jean-Philippe Boussemart & Herve Leleu & Zhiyang Shen & Michael Vardanyan & Ning Zhu, 2019. "Decomposing banking performance into economic and credit risk efficiencies," Post-Print hal-02497996, HAL.
  • Handle: RePEc:hal:journl:hal-02497996
    DOI: 10.1016/j.ejor.2019.03.006
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    Cited by:

    1. Fukuyama, Hirofumi & Tan, Yong, 2022. "Implementing strategic disposability for performance evaluation: Innovation, stability, profitability and corporate social responsibility in Chinese banking," European Journal of Operational Research, Elsevier, vol. 296(2), pages 652-668.
    2. Su‐Chuan Liao & Tai‐Yu Lin & Tzu‐Han Chang & Yung‐ho Chiu, 2024. "Non‐performance loans, operation, and recycle efficiency analysis—Dynamic Two‐stage Directional Distance Function Recycle with Assurance Regions model," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(2), pages 952-974, March.
    3. Niu, Yiran & Boussemart, Jean-Philippe & Shen, Zhiyang & Vardanyan, Michael, 2024. "Performance evaluation using multi-stage production frameworks: Assessing the tradeoffs among the economic, environmental, and social well-being," European Journal of Operational Research, Elsevier, vol. 318(3), pages 1000-1013.
    4. Xiong, Beibei & Chen, Haoxun & An, Qingxian & Wu, Jie, 2019. "A multi-objective distance friction minimization model for performance assessment through data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(1), pages 132-142.
    5. Qingxian An & Xuyang Liu & Yongli Li & Beibei Xiong, 2019. "Resource planning of Chinese commercial banking systems using two-stage inverse data envelopment analysis with undesirable outputs," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-20, June.
    6. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2023. "Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1360-1373.
    7. Zhou, Xun & Kuosmanen, Timo, 2020. "What drives decarbonization of new passenger cars?," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1043-1057.
    8. Pejman Peykani & Mostafa Sargolzaei & Negin Sanadgol & Amir Takaloo & Hamidreza Kamyabfar, 2023. "The application of structural and machine learning models to predict the default risk of listed companies in the Iranian capital market," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-24, November.
    9. Zhu, Ning & Hougaard, Jens Leth & Yu, Zhiqian & Wang, Bing, 2020. "Ranking Chinese commercial banks based on their expected impact on structural efficiency," Omega, Elsevier, vol. 94(C).
    10. K. Hervé Dakpo & Yann Desjeux & Laure Latruffe, 2023. "Cost of abating excess nitrogen on wheat plots in France: An assessment with multi‐technology modelling," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(3), pages 800-815, September.
    11. Hirofumi Fukuyama & Yong Tan, 2022. "Deconstructing three‐stage overall efficiency into input, output and stability efficiency components with consideration of market power and loan loss provision: An application to Chinese banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 953-974, January.
    12. Sunil Mohanty & Hong-Jen Lin, 2021. "Efficiency in China’s Banking Sector: A Comparative Analysis of Pre- and Post-Basel II Eras," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 24(02), pages 1-29, June.
    13. Zhao, Linlin & Zhu, Qingyuan & Zhang, Lin, 2021. "Regulation adaptive strategy and bank efficiency: A network slacks-based measure with shared resources," European Journal of Operational Research, Elsevier, vol. 295(1), pages 348-362.
    14. Hugo E. Caceres & Ben Moews, 2024. "Evaluating utility in synthetic banking microdata applications," Papers 2410.22519, arXiv.org.
    15. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    16. Jiawei Yang, 2023. "Disentangling the sources of bank inefficiency: a two-stage network multi-directional efficiency analysis approach," Annals of Operations Research, Springer, vol. 326(1), pages 369-410, July.
    17. Bansal, Pooja & Kumar, Sunil & Mehra, Aparna & Gulati, Rachita, 2022. "Developing two dynamic Malmquist-Luenberger productivity indices: An illustrated application for assessing productivity performance of Indian banks," Omega, Elsevier, vol. 107(C).
    18. Rim Boussaada & Abdelaziz Hakimi & Majdi Karmani, 2022. "Is there a threshold effect in the liquidity risk–non‐performing loans relationship? A PSTR approach for MENA banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1886-1898, April.
    19. Ozili, Peterson K, 2022. "Economic policy uncertainty, bank nonperforming loans and loan loss provisions: are they correlated?," MPRA Paper 112381, University Library of Munich, Germany.
    20. Xiaodong Chen & Anda Guo & Jiahao Zhu & Fang Wang & Yanqiu He, 2022. "Accessing performance of transport sector considering risks of climate change and traffic accidents: joint bounded-adjusted measure and Luenberger decomposition," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 115-138, March.
    21. Mehmood, Asad & De Luca, Francesco, 2023. "How does non-interest income affect bank credit risk? Evidence before and during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 53(C).
    22. Tsionas, Mike G., 2023. "Bayesian learning in performance. Is there any?," European Journal of Operational Research, Elsevier, vol. 311(1), pages 263-282.

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