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How do Greek banking institutions react after significant events?--A DEA approach

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  • Siriopoulos, Costas
  • Tziogkidis, Panagiotis

Abstract

The efficiency of Greek commercial banks is considered through the period 1995-2003 using the data envelopment analysis technique. Two approaches are used to measure efficiency: one using financial ratios as outputs only and the other viewing banks as credit generation and transaction institutions. The empirical results are used to examine the reaction of banking institutions after significant events such as M&As, privatizations and the crisis of the Athens Stock Exchange in 1999. In most cases performance deteriorates for the next 1 to 2 years, while increases thereafter, forming specific patterns of efficiency. In the last part we introduce an index to measure the management's efficiency through a process of change. The results suggest that the Greek banking sector operated efficiently on average during the destabilization periods. The contribution of the article is that it comprises the first study which examines empirically the performance behavior of banking institutions within the scope of change management theory.

Suggested Citation

  • Siriopoulos, Costas & Tziogkidis, Panagiotis, 2010. "How do Greek banking institutions react after significant events?--A DEA approach," Omega, Elsevier, vol. 38(5), pages 294-308, October.
  • Handle: RePEc:eee:jomega:v:38:y:2010:i:5:p:294-308
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    Cited by:

    1. You, Yan Q. & Jie, Tao, 2016. "A study of the operation efficiency and cost performance indices of power-supply companies in China based on a dynamic network slacks-based measure model," Omega, Elsevier, vol. 60(C), pages 85-97.
    2. Sambracos, Evangelos & Maniati, Marina, 2015. "Technical Efficiency of Shipping Banks: A DEA Approach," MPRA Paper 63131, University Library of Munich, Germany.
    3. Mohsen Afsharian & Mohammadreza Alirezaee & Peter Reichling, 2012. "The master Malmquist index measurement using DEA-based weighted average efficiency," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 21-42.
    4. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "Data envelopment analysis 1978–2010: A citation-based literature survey," Omega, Elsevier, vol. 41(1), pages 3-15.
    5. Partovi, Fariborz Y., 2011. "Corporate philanthropic selection using data envelopment analysis," Omega, Elsevier, vol. 39(5), pages 522-527, October.
    6. repec:spr:annopr:v:253:y:2017:i:1:d:10.1007_s10479-016-2294-1 is not listed on IDEAS
    7. Udhayakumar, A. & Charles, V. & Kumar, Mukesh, 2011. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems," Omega, Elsevier, vol. 39(4), pages 387-397, August.
    8. Amy H. I. Lee & Chun Yu Lin & He-Yau Kang & Wen Hsin Lee, 2012. "An Integrated Performance Evaluation Model for the Photovoltaics Industry," Energies, MDPI, Open Access Journal, vol. 5(4), pages 1-21, April.
    9. Halkos, George E. & Tzeremes, Nickolaos G., 2013. "Estimating the degree of operating efficiency gains from a potential bank merger and acquisition: A DEA bootstrapped approach," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1658-1668.
    10. Paradi, Joseph C. & Rouatt, Stephen & Zhu, Haiyan, 2011. "Two-stage evaluation of bank branch efficiency using data envelopment analysis," Omega, Elsevier, vol. 39(1), pages 99-109, January.

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