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Identifying distress among banks prior to a major crisis using non-oriented super-SBM

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  • Necmi Avkiran
  • Lin Cai

Abstract

We illustrate how data envelopment analysis (DEA) can be used as a forward-looking method to flag bank holding companies (BHCs) likely to become distressed. Various financial performance models are tested in the period leading up to the recent global financial crisis. Results generally support DEA’s discriminatory and predictive power, suggesting that it can identify distressed banks up to 2 years in advance. Robustness tests reveal that DEA has a stable efficient frontier and its discriminatory and predictive powers prevail even after data perturbations. DEA can be used as a preliminary off-site screening tool by regulators, by business managers to ascertain their standing among competitors, and by investors. Attention by regulators can be further directed at potentially distressed banks as some of them would be candidates for closer monitoring. In conclusion, DEA may be useful in making economic decisions because there is an identifiable link between inefficiency and financial distress. To the best of our knowledge, application of DEA to predict financial distress among BHCs prior to a major crisis has not been published. Copyright Springer Science+Business Media New York 2014

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  • Necmi Avkiran & Lin Cai, 2014. "Identifying distress among banks prior to a major crisis using non-oriented super-SBM," Annals of Operations Research, Springer, vol. 217(1), pages 31-53, June.
    • Handle: RePEc:spr:annopr:v:217:y:2014:i:1:p:31-53:10.1007/s10479-014-1568-8
    DOI: 10.1007/s10479-014-1568-8
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    8. Wasi Ul Hassan Shah & Gang Hao & Nan Zhu & Rizwana Yasmeen & Ihtsham Ul Haq Padda & Muhammad Abdul Kamal, 2022. "A cross-country efficiency and productivity evaluation of commercial banks in South Asia: A meta-frontier and Malmquist productivity index approach," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-17, April.
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    10. Tu D. Q. Le & Tin H. Ho & Thanh Ngo & Dat T. Nguyen & Son H. Tran, 2022. "A Dataset for the Vietnamese Banking System (2002–2021)," Data, MDPI, vol. 7(9), pages 1-8, August.

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    More about this item

    Keywords

    Data envelopment analysis; Distress prediction; Bank holding companies; Financial crisis; C67; C21;
    All these keywords.

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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