The relation between efficiency of credit operation and non-performing loans—An application of network DEA model with undesirable outputs

Manh-Trung Phung, Van-Thi Dao

Article ID: 5372
Vol 8, Issue 6, 2024

VIEWS - 151 (Abstract) 49 (PDF)

Abstract


To evaluate the efficiency of decision-making units, researchers continually develop models simulating the production process of organizations. This study formulates a network model integrating undesirable outputs to measure the efficiency of Vietnam’s banking industry. Employing methodologies from the data envelopment analysis (DEA) approach, the efficiency scores for these banks are subsequently computed and comparatively analyzed. The empirical results indicate that the incorporation of undesirable output variables in the efficiency evaluation model leads to significantly lower efficiency scores compared to the conventional DEA model. In practical terms, the study unveils a deterioration in the efficiency of banking operations in Vietnam during the post-Covid era, primarily attributed to deficiencies in credit risk management. These findings contribute to heightening awareness among bank managers regarding the pivotal importance of credit management activities.


Keywords


data envelopment analysis; undesirable outputs; credit operation; non-performing loans

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References


Berger, A. N., & DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking & Finance, 21(6), 849–870. https://doi.org/10.1016/S0378-4266(97)00003-4

Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2), 175–212. https://doi.org/10.1016/S0377-2217(96)00342-6

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

Dang, N. T. T., Nguyen, T. Q., Kok, S. K., et al. (2023). Multi-level challenges in talent management in a post COVID-19 future: Examining the banking sector in Vietnam. Cogent Business & Management, 10(3), 2251198. https://doi.org/10.1080/23311975.2023.2251198

Fries, S., & Taci, A. (2005). Cost efficiency of banks in transition: Evidence from 289 banks in 15 post-communist countries. Journal of Banking & Finance, 29(1), 55–81. https://doi.org/10.1016/J.JBANKFIN.2004.06.016

Fukuyama, H., & Matousek, R. (2017). Modelling bank performance: A network DEA approach. European Journal of Operational Research, 259(2), 721–732. https://doi.org/10.1016/J.EJOR.2016.10.044

Fukuyama, H., & Weber, W. L. (2015). Measuring Japanese bank performance: a dynamic network DEA approach. Journal of Productivity Analysis, 44(3), 249–264. https://doi.org/10.1007/s11123-014-0403-1

Halkos, G., & Petrou, K. N. (2019). Treating undesirable outputs in DEA: A critical review. Economic Analysis and Policy, 62, 97–104. https://doi.org/10.1016/j.eap.2019.01.005

Holod, D., & Lewis, H. F. (2011). Resolving the deposit dilemma: A new DEA bank efficiency model. Journal of Banking & Finance, 35(11), 2801–2810. https://doi.org/10.1016/J.JBANKFIN.2011.03.007

Hunter, W. C., & Timme, S. G. (1995). Core Deposits and Physical Capital: A Reexamination of Bank Scale Economies and Efficiency with Quasi-Fixed Inputs. Journal of Money, Credit and Banking, 27(1), 165–185. https://doi.org/10.2307/2077857

Kumbhakar, S. C., & Horncastle, A. P. (2015). A practitioner’s guide to stochastic frontier analysis using Stata. Cambridge University Press.

Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics (NRL), 55(7), 643–653. https://doi.org/10.1002/nav.20308

Liang, L., Yang, F., Cook, W. D., et al. (2006). DEA models for supply chain efficiency evaluation. Annals of Operations Research, 145(1), 35–49. https://doi.org/10.1007/s10479-006-0026-7

Partovi, E., & Matousek, R. (2019). Bank efficiency and non-performing loans: Evidence from Turkey. Research in International Business and Finance, 48, 287–309. https://doi.org/10.1016/j.ribaf.2018.12.011

Podpiera, J., & Weill, L. (2008). Bad luck or bad management? Emerging banking market experience. Journal of Financial Stability, 4(2), 135–148. https://doi.org/10.1016/J.JFS.2008.01.005

Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400–410. https://doi.org/10.1016/S0377-2217(00)00160-0

Sealey Jr., C. W., & Lindley, J. T. (1977). Inputs, outputs, and a theory of production and cost at depository financial institutions. The Journal of Finance, 32(4), 1251–1266. https://doi.org/10.1111/j.1540-6261.1977.tb03324.x

Staub, R. B., da Silva e Souza, G., & Tabak, B. M. (2010). Evolution of bank efficiency in Brazil: A DEA approach. European Journal of Operational Research, 202(1), 204–213. https://doi.org/10.1016/J.EJOR.2009.04.025

Takahashi, F. L., & Vasconcelos, M. R. (2024). Bank efficiency and undesirable output: An analysis of non-performing loans in the Brazilian banking sector. Finance Research Letters, 59, 104651. https://doi.org/10.1016/j.frl.2023.104651

Zhu, J. (2014). Modeling Undesirable Measures. In: Zhu, J. (editor). Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets. Springer International Publishing. pp. 141–151. https://doi.org/10.1007/978-3-319-06647-9_8




DOI: https://doi.org/10.24294/jipd.v8i6.5372

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