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A DEA and random forest regression approach to studying bank efficiency and corporate governance

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  • Keyur Thaker
  • Vincent Charles
  • Abhay Pant
  • Tatiana Gherman

Abstract

We employ Data Envelopment Analysis to estimate the new technical, new cost, and new profit efficiency of Indian banks over the period 2008–2018. Then, we use Random Forest Regression to examine the impact of corporate governance (Board Size, Board Independence, Duality, Gender Diversity, and Board Meetings), bank characteristics (Return on Assets, Size, and Equity to Total Assets), and other characteristics (Ownership and Years) on bank efficiency. Among others, we found that board characteristics play a significant role particularly in new profit efficiency; therefore, policymakers and regulators should consider Board Size, Board Independence, Board Meetings, and Duality while framing guidelines for enhancing bank new profit efficiency. We also found that Board Independence plays a vital role in bank new cost efficiency, while Gender Diversity contributes to both new technical and new cost efficiency. This study makes methodological contributions by employing Machine Learning based Random Forest Regression in tandem with Data Envelopment Analysis under a two-phase model to examine corporate governance and bank efficiency, which is a pioneering attempt.

Suggested Citation

  • Keyur Thaker & Vincent Charles & Abhay Pant & Tatiana Gherman, 2022. "A DEA and random forest regression approach to studying bank efficiency and corporate governance," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(6), pages 1258-1277, June.
    • Handle: RePEc:taf:tjorxx:v:73:y:2022:i:6:p:1258-1277
    DOI: 10.1080/01605682.2021.1907239
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    Cited by:

    1. Georgios Tsaples & Jason Papathanasiou & Andreas C. Georgiou, 2022. "An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU," Mathematics, MDPI, vol. 10(13), pages 1-27, June.
    2. Jorge Antunes & Peter Wanke & Thiago Fonseca & Yong Tan, 2023. "Do ESG Risk Scores Influence Financial Distress? Evidence from a Dynamic NDEA Approach," Sustainability, MDPI, vol. 15(9), pages 1-32, May.
    3. Dominika Gajdosikova & Katarina Valaskova & Tomas Kliestik & Maria Kovacova, 2023. "Research on Corporate Indebtedness Determinants: A Case Study of Visegrad Group Countries," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    4. Ying Zhou & Xia Lin & Guotai Chi & Peng Jin & Mengtong Li, 2024. "EWT‐SMOTE to improve default prediction performance in imbalanced data: Analysis of Chinese data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 615-643, April.
    5. Mirza, Nawazish & Umar, Muhammad & Afzal, Ayesha & Firdousi, Saba Fazal, 2023. "The role of fintech in promoting green finance, and profitability: Evidence from the banking sector in the euro zone," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 33-40.
    6. Xuan Thi Thanh Mai & Ha Thi Nhu Nguyen & Thanh Ngo & Tu D. Q. Le & Lien Phuong Nguyen, 2023. "Efficiency of the Islamic Banking Sector: Evidence from Two-Stage DEA Double Frontiers Analysis," IJFS, MDPI, vol. 11(1), pages 1-14, February.
    7. Pejman Peykani & Mostafa Sargolzaei & Amir Takaloo & Shahla Valizadeh, 2023. "The Effects of Monetary Policy on Macroeconomic Variables through Credit and Balance Sheet Channels: A Dynamic Stochastic General Equilibrium Approach," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    8. Antônio Carlos Pacagnella Júnior & Henrique Luiz da Silva & Wagner Wilson Bortoletto & Paulo Sergio de Arruda Ignacio, 2023. "Financial and environmental efficiency of CDM projects: Analysis and classification for investment decisions," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 926-941, March.

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