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A new inverse DEA cost efficiency model for estimating potential merger gains: a case of Canadian banks

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  • Gholam R. Amin

    (University of New Brunswick at Saint John)

  • Mustapha Ibn Boamah

    (University of New Brunswick at Saint John)

Abstract

Estimating potential gains from mergers is an important strategic decision-making problem. This paper introduces a new inverse data envelopment analysis (DEA) based on a cost efficiency model for estimating potential gains from mergers. There are restructuring scenarios for firms that want to minimize cost. The existing inverse DEA technical efficiency models are not appropriate for estimating merger gains in these situations. It is also shown that the proposed inverse DEA cost efficiency model can reveal more merger gains than the inverse DEA technical efficiency model. The applicability of the proposed method is shown through an application in Canada’s banking sector to determine the required level of inputs and outputs for a merged bank to achieve target levels of cost and technical efficiencies. The results highlight the potential financial gains to improving both technical and cost efficiencies as efficiency-seeking banks increasingly become large and complex institutions through growth, mergers and acquisitions in a financial environment that is being shaped by reforms and technological innovation.

Suggested Citation

  • Gholam R. Amin & Mustapha Ibn Boamah, 2020. "A new inverse DEA cost efficiency model for estimating potential merger gains: a case of Canadian banks," Annals of Operations Research, Springer, vol. 295(1), pages 21-36, December.
    • Handle: RePEc:spr:annopr:v:295:y:2020:i:1:d:10.1007_s10479-020-03667-9
    DOI: 10.1007/s10479-020-03667-9
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    3. Shao-Yin Hsu & Ching-Cheng Lu & Yan-Hui Xiao & Yung-ho Chiu, 2024. "Two-Stage Evaluation of the Pre-merger Potential Gains of Taiwan Financial Holding Companies: Dynamic Network Slack-Based Measure Analysis Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2131-2178, October.
    4. Yung‐ho Chiu & Tai‐Yu Lin & Tzu‐Han Chang & Yi‐Nuo Lin & Shih‐Yung Chiu, 2021. "Prevaluating efficiency gains from potential mergers and acquisitions in the financial industry with the Resample Past–Present–Future data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(2), pages 369-384, March.
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    6. Faten Ben Bouheni & Hassan Obeid & Elena Margarint, 2022. "Nonperforming loan of European Islamic banks over the economic cycle," Annals of Operations Research, Springer, vol. 313(2), pages 773-808, June.
    7. Chang, Tsung-Sheng & Lin, Ji-Gang & Ouenniche, Jamal, 2023. "DEA-based Nash bargaining approach to merger target selection," European Journal of Operational Research, Elsevier, vol. 305(2), pages 930-945.
    8. Sabri Boubaker & T.D.Q. Le & T. Ngo, 2023. "Managing Bank Performance under COVID-19: A Novel Inverse DEA Efficiency Approach," Post-Print hal-04435441, HAL.
    9. Amin, Gholam R. & Ibn Boamah, Mustapha, 2023. "Modeling business partnerships: A data envelopment analysis approach," European Journal of Operational Research, Elsevier, vol. 305(1), pages 329-337.
    10. Contreras, I. & Lozano, S., 2022. "Size efficiency, splits and merger gains, and centralized resource reallocation of Spanish public universities," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    11. Lin, Sheng-Wei & Lu, Wen-Min, 2024. "Using inverse DEA and machine learning algorithms to evaluate and predict suppliers’ performance in the apple supply chain," International Journal of Production Economics, Elsevier, vol. 271(C).
    12. Aranha, Meera Laetitia B & Mahapatra, Mrutyunjay & Jacob, Remya Tressa, 2024. "Mergers of public sector banks: Best partner selection using a data-driven approach," Finance Research Letters, Elsevier, vol. 63(C).

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