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Efficiency and Risk in Commercial Banks - Hybrid DEA Estimation

Author

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  • Mu-Jen Chen
  • Yung-ho Chiu
  • Chyanlong Jan
  • Yu-Chuan Chen
  • Hsiang-Hsi Liu

Abstract

The hybrid DEA model can solve the difference between radial inputs and non-radial inputs and evaluate efficiency. This is a pioneering study that uses the hybrid DEA model, evaluating the proportionate inputs with a radial measure and the non-proportionate inputs with a non-radial measure, in order to examine the impact of non-performing loans (NPLs) on the efficiency of Taiwan's banking sector from 2006 to 2010. In summary, this research demonstrates the following: (1) Only nine banks remained in the top efficiency list during these years: China Development Industrial Bank, Mega International Commercial Bank, Chinatrust Commercial Bank, Cathay United Bank, Bank of Kaohsiung, Industrial Bank of Taiwan, Taiwan Cooperative Bank, Land Bank, and Bank of Taiwan. (2) Risk is an important factor that should be taken into consideration when evaluating banking efficiency. (3) From the hybrid DEA model, we find that most of the inefficient banks have an inefficiency factor caused primarily by too many NPLs (risk). (4) The efficiency of Taiwan's large-scale banks is significantly better than the small-scale bank. By looking at the inefficiency index, the large-scale bank's inefficiency is caused by NPLs. For the small-scale bank, both radial variables and non-radial variables have equal importance in improving its efficiency.

Suggested Citation

  • Mu-Jen Chen & Yung-ho Chiu & Chyanlong Jan & Yu-Chuan Chen & Hsiang-Hsi Liu, 2015. "Efficiency and Risk in Commercial Banks - Hybrid DEA Estimation," Global Economic Review, Taylor & Francis Journals, vol. 44(3), pages 335-352, September.
    • Handle: RePEc:taf:glecrv:v:44:y:2015:i:3:p:335-352
    DOI: 10.1080/1226508X.2015.1067865
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    Citations

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    Cited by:

    1. Ying Li & Yung‐ho Chiu & Ying Yu Chen & Lihua Wang & Yi‐Nuo Lin & Su‐Wan Wang, 2022. "The impact of market share on efficiency of commercial banks: Resampling slacks‐based measure data envelopment analyses model," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(5), pages 1621-1634, July.
    2. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
    3. Ying Li & Yung-ho Chiu & Tai-Yu Lin & Yun Yuan Huang, 2019. "Market share and performance in Taiwanese banks: min/max SBM DEA," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 233-252, July.
    4. Li‐Ting Yeh & Dong‐Shang Chang & Huei‐Min Li, 2022. "Developing a network data envelopment analysis model to measure the efficiency of banking with the governance, innovation, and operations," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(7), pages 2863-2874, October.
    5. Xiao-Ning Li & Ying Feng & Pei-Ying Wu & Yung-Ho Chiu, 2021. "An Analysis of Environmental Efficiency and Environmental Pollution Treatment Efficiency in China’s Industrial Sector," Sustainability, MDPI, vol. 13(5), pages 1-25, February.
    6. Youchao Tan & Yang Zhang & Roohollah Khodaverdi, 2017. "Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry," Annals of Operations Research, Springer, vol. 248(1), pages 449-470, January.

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