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Bank Performance Analysis

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Abstract

The goal of this chapter is to overview the current state of the art analysis of banking performance. For this, we navigate through the literature that has been prospering during the recent decades. In particular, we start with a brief discussion of the ratio analysis for measuring bank performance, which is still very popular in practice. Then we consider such popular productivity and efficiency analysis methods as data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Then, we provide a brief review of other econometric methods that became leading in the recent finance literature that involve techniques of casual inference, including difference-in-differences (DD) and regression discontinuity design (RDD).

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  • Natalya Zelenyuk & Valentin Zelenyuk, 2021. "Bank Performance Analysis," CEPA Working Papers Series WP022021, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:156
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    File URL: https://economics.uq.edu.au/files/24750/WP022021.pdf
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    References listed on IDEAS

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    1. Worthington, Andrew C. & Zelenyuk, Valentin, 2018. "Data envelopment analysis, truncated regression and double-bootstrap for panel data with application to Chinese bankingAuthor-Name: Du, Kai," European Journal of Operational Research, Elsevier, vol. 265(2), pages 748-764.
    2. Ray,Subhash C., 2012. "Data Envelopment Analysis," Cambridge Books, Cambridge University Press, number 9781107405264, January.
    3. Léopold Simar & Paul Wilson, 2011. "Two-stage DEA: caveat emptor," Journal of Productivity Analysis, Springer, vol. 36(2), pages 205-218, October.
    4. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
    5. Nanda, Ramana & Nicholas, Tom, 2014. "Did bank distress stifle innovation during the Great Depression?," Journal of Financial Economics, Elsevier, vol. 114(2), pages 273-292.
    6. Paradi, Joseph C. & Zhu, Haiyan, 2013. "A survey on bank branch efficiency and performance research with data envelopment analysis," Omega, Elsevier, vol. 41(1), pages 61-79.
    7. Léopold Simar & Valentin Zelenyuk, 2011. "Stochastic FDH/DEA estimators for frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(1), pages 1-20, August.
    8. Neuhann, Daniel & Saidi, Farzad, 2018. "Do universal banks finance riskier but more productive firms?," Journal of Financial Economics, Elsevier, vol. 128(1), pages 66-85.
    9. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    10. Grosskopf, S, 1986. "The Role of the Reference Technology in Measuring Productive Efficiency," Economic Journal, Royal Economic Society, vol. 96(382), pages 499-513, June.
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    12. Nakabayashi, Ken & Tone, Kaoru, 2006. "Egoist's dilemma: a DEA game," Omega, Elsevier, vol. 34(2), pages 135-148, April.
    13. Resti, Andrea, 1997. "Evaluating the cost-efficiency of the Italian Banking System: What can be learned from the joint application of parametric and non-parametric techniques," Journal of Banking & Finance, Elsevier, vol. 21(2), pages 221-250, February.
    14. Schepens, Glenn, 2016. "Taxes and bank capital structure," Journal of Financial Economics, Elsevier, vol. 120(3), pages 585-600.
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    Cited by:

    1. M.V. Leonov, 2021. "Review of Modern Approaches for Assessing the Effectiveness of Banking," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 20(2), pages 294-326.

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    More about this item

    Keywords

    Banking; Performance Analysis; Productivity; Efficiency; Data Envelopment Analysis; Stochastic Frontier Analysis; Econometrics; Panel Data; Causal Inference.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets

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