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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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    1. Emrouznejad, Ali & Yang, Guo-liang, 2018. "A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 4-8.
    2. S Lozano & G Villa, 2010. "DEA-based pre-merger planning tool," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1485-1497, October.
    3. Niepmann, Friederike & Schmidt-Eisenlohr, Tim, 2017. "International trade, risk and the role of banks," Journal of International Economics, Elsevier, vol. 107(C), pages 111-126.
    4. Xiao Shi & Yongjun Li & Ali Emrouznejad & Jianhui Xie & Liang Liang, 2017. "Erratum to: Estimation of potential gains from bank mergers: A novel two-stage cost efficiency DEA model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(8), pages 983-983, August.
    5. Hadi-Vencheh, Abdollah & Foroughi, Ali Asghar & Soleimani-damaneh, Majid, 2008. "A DEA model for resource allocation," Economic Modelling, Elsevier, vol. 25(5), pages 983-993, September.
    6. Morris Knapp & Alan Gart & David Becher, 2005. "Post‐Merger Performance of Bank Holding Companies, 1987–1998," The Financial Review, Eastern Finance Association, vol. 40(4), pages 549-574, November.
    7. Wei, Quanling & Zhang, Jianzhong & Zhang, Xiangsun, 2000. "An inverse DEA model for inputs/outputs estimate," European Journal of Operational Research, Elsevier, vol. 121(1), pages 151-163, February.
    8. Jens Hagendorff & Kevin Keasey, 2009. "Post‐merger strategy and performance: evidence from the US and European banking industries," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 49(4), pages 725-751, December.
    9. Fabio Panetta & Fabiano Schivardi & Matthew Shum, 2009. "Do Mergers Improve Information? Evidence from the Loan Market," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(4), pages 673-709, June.
    10. Jahanshahloo, G.R. & Soleimani-damaneh, M. & Ghobadi, S., 2015. "Inverse DEA under inter-temporal dependence using multiple-objective programming," European Journal of Operational Research, Elsevier, vol. 240(2), pages 447-456.
    11. Peter Bogetoft & Dexiang Wang, 2005. "Estimating the Potential Gains from Mergers," Journal of Productivity Analysis, Springer, vol. 23(2), pages 145-171, May.
    12. Mojtaba Ghiyasi, 2018. "Efficiency improvement and resource estimation: a tradeoff analysis," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 25(2), pages 151-169.
    13. Yongbo Li & Amir-Reza Abtahi & Mahya Seyedan, 2019. "Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry," Annals of Operations Research, Springer, vol. 275(2), pages 461-484, April.
    14. Timothy H. Hannan & Steven J. Pilloff, 2009. "Acquisition Targets and Motives in the Banking Industry," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(6), pages 1167-1187, September.
    15. Lim, Dong-Joon, 2016. "Inverse DEA with frontier changes for new product target setting," European Journal of Operational Research, Elsevier, vol. 254(2), pages 510-516.
    16. LaPlante, A.E. & Paradi, J.C., 2015. "Evaluation of bank branch growth potential using data envelopment analysis," Omega, Elsevier, vol. 52(C), pages 33-41.
    17. David J. Mayston, 2017. "Data envelopment analysis, endogeneity and the quality frontier for public services," Annals of Operations Research, Springer, vol. 250(1), pages 185-203, March.
    18. Gholam R. Amin & Amar Oukil, 2019. "Flexible target setting in mergers using inverse data envelopment analysis," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 35(3), pages 301-317.
    19. Xiao Shi & Yongjun Li & Ali Emrouznejad & Jianhui Xie & Liang Liang, 2017. "Estimation of potential gains from bank mergers: A novel two-stage cost efficiency DEA model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1045-1055, September.
    20. Fang, Lei, 2013. "A generalized DEA model for centralized resource allocation," European Journal of Operational Research, Elsevier, vol. 228(2), pages 405-412.
    21. Guo, Chuanyin & Abbasi Shureshjani, Roohollah & Foroughi, Ali Asghar & Zhu, Joe, 2017. "Decomposition weights and overall efficiency in two-stage additive network DEA," European Journal of Operational Research, Elsevier, vol. 257(3), pages 896-906.
    22. Huang, Chin-wei, 2018. "Assessing the performance of tourism supply chains by using the hybrid network data envelopment analysis model," Tourism Management, Elsevier, vol. 65(C), pages 303-316.
    23. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2018. "Environmental efficiency and abatement efficiency measurements of China's thermal power industry: A data envelopment analysis based materials balance approach," European Journal of Operational Research, Elsevier, vol. 269(1), pages 35-50.
    24. M. Eyni & G. Tohidi & S. Mehrabeian, 2017. "Applying inverse DEA and cone constraint to sensitivity analysis of DMUs with undesirable inputs and outputs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 34-40, January.
    25. Hankir, Yassin & Rauch, Christian & Umber, Marc P., 2011. "Bank M&A: A market power story?," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2341-2354, September.
    26. Yan, Hong & Wei, Quanling & Hao, Gang, 2002. "DEA models for resource reallocation and production input/output estimation," European Journal of Operational Research, Elsevier, vol. 136(1), pages 19-31, January.
    27. Sepideh Kaffash & Marianna Marra, 2017. "Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds," Annals of Operations Research, Springer, vol. 253(1), pages 307-344, June.
    28. Yongjun Li & Feng Li & Ali Emrouznejad & Liang Liang & Qiwei Xie, 2019. "Allocating the fixed cost: an approach based on data envelopment analysis and cooperative game," Annals of Operations Research, Springer, vol. 274(1), pages 373-394, March.
    29. Mojtaba Ghiyasi, 2019. "Novel criterion models in the inverse DEA problem," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 35(1), pages 20-36.
    30. Mario Seccareccia, 2012. "Financialization and the transformation of commercial banking: understanding the recent Canadian experience before and during the international financial crisis," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 35(2), pages 277-300.
    31. Ali Emrouznejad & Guo-liang Yang & Gholam R. Amin, 2019. "A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(7), pages 1079-1090, July.
    32. Magdalena Kapelko, 2019. "Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union," Annals of Operations Research, Springer, vol. 278(1), pages 215-234, July.
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    2. Gholam R. Amin & Mustapha Ibn Boamah, 2021. "A two‐stage inverse data envelopment analysis approach for estimating potential merger gains in the US banking sector," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1454-1465, September.
    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.
    5. Hirofumi Fukuyama & Mike Tsionas & Yong Tan, 2024. "Incorporating causal modeling into data envelopment analysis for performance evaluation," Annals of Operations Research, Springer, vol. 342(3), pages 1865-1904, November.
    6. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
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    13. 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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