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Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis.

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  • 1School of Finance, Southwestern University of Finance and Economics, 555 Liutai Avenue, Chengdu, 611130 Sichuan China.

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Summary

This study introduces Malmquist Data Envelopment Analysis (DEA) to dynamically predict bank failure risk. The method effectively forecasts potential bank collapse by analyzing productivity and efficiency over time.

Keywords:
Bank efficiencyBank failureBankruptcy predictionData Envelopment AnalysisDynamic model

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Area of Science:

  • Financial Economics
  • Quantitative Finance
  • Risk Management

Background:

  • Bank failure prediction is a critical area in credit risk and banking studies.
  • While statistical and machine learning methods exist, recent focus highlights bank efficiency as a key failure indicator.
  • Previous studies linking efficiency (Data Envelopment Analysis - DEA) to bank failure were limited to static, cross-sectional analyses.

Purpose of the Study:

  • To propose and validate a dynamic, nonparametric method for assessing bank bankruptcy risk over multiple periods.
  • To address the limitations of previous cross-sectional analyses in bank failure prediction.
  • To utilize updated samples for more accurate bankruptcy prediction.

Main Methods:

  • Development and application of Malmquist Data Envelopment Analysis (DEA) with a Worst Practice Frontier.
  • Dynamic assessment of bank bankruptcy risk across multiple periods.
  • Empirical testing on a large sample of 4426 US banks from 2002-2016, including the subprime financial crisis period.

Main Results:

  • Malmquist DEA proved effective in estimating productivity growth and providing early warnings of potential bank collapse.
  • The dynamic approach demonstrated superior predictive performance compared to static benchmark models.
  • Extended DEA models with varied reference sets and orientations also exhibited strong predictive power.

Conclusions:

  • Malmquist DEA is a valuable tool for dynamic bank bankruptcy risk assessment and early warning systems.
  • Dynamic efficiency analysis offers significant advantages over static methods for predicting bank failures.
  • The proposed methodology enhances the understanding of factors contributing to bank instability.