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Financial efficiency of commercial banks listed in Egyptian stock exchange using data envelopment analysis

Author

Listed:
  • Mahmoud Abdelrahman Kamel
  • Mohamed El-Sayed Mousa
  • Randa Mohamed Hamdy

Abstract

Purpose - This study used data envelopment analysis (DEA) models to measure financial efficiency of twelve commercial banks listed in the Egyptian stock exchange (CBLSE), along with evaluating changes to the financial efficiency during the period 2017–2019. Design/methodology/approach - The study used BCC-I, cross-efficiency, super-efficiency models, and Malmquist productivity index (MPI) to assess financial efficiency of the examined banks. The available data from both inputs and outputs were analyzed using R. studio V.I.3. 1056 software. Findings - Out of twelve banks examined, only four banks were efficient under BCC-I model over different years of the study period; however, only one bank (CIB) appeared to be the most efficient compared to other peers in the study sample. Moreover, MPI results revealed decreased financial efficiency during the study period, due to the decreased technological innovation, except for HDB. Tobit regression results confirmed that total assets and total equity are significant factors impacted financial efficiency of CBLSE. Practical implications - This study sheds light on the importance of evaluating financial efficiency of CBLSE to all stakeholders, to pinpoint weaknesses in banks' performance, and for evaluating financial policies and investment decisions. Originality/value - Several studies sought to implement different models of DEA to assess banking performance in different regions of the world, but very few studies examined financial efficiency of banks. To the best of authors’ knowledge, this study is one of those few that addressed financial efficiency of banks in Egypt.

Suggested Citation

  • Mahmoud Abdelrahman Kamel & Mohamed El-Sayed Mousa & Randa Mohamed Hamdy, 2021. "Financial efficiency of commercial banks listed in Egyptian stock exchange using data envelopment analysis," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 71(8), pages 3683-3703, May.
  • Handle: RePEc:eme:ijppmp:ijppm-10-2020-0531
    DOI: 10.1108/IJPPM-10-2020-0531
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    Cited by:

    1. Cheng, WeiJin & Ming, Kai & Ullah, Mirzat, 2024. "Oil price volatility prediction using out-of-sample analysis – Prediction efficiency of individual models, combination methods, and machine learning based shrinkage methods," Energy, Elsevier, vol. 300(C).

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