Devising the Best Prospective Merger Plan for a Banking Sector Through a Hybrid DEA-Based Methodology: An Inverse DEA Perspective
Amar Oukil
Chapter 9 in Handbook on Data Envelopment Analysis in Business, Finance, and Sustainability:Recent Trends and Developments, 2024, pp 273-306 from World Scientific Publishing Co. Pte. Ltd.
Abstract:
Mergers & acquisitions (M&A) are strategic decisions that have long been associated with the banking sector, entailing the consolidation of assets among a group of banks through various types of financial transactions. Though a lot of research has been dedicated to the application of data envelopment analysis (DEA) to multiple aspects of M&A within this particular sector, little or even no significant attention has been paid to investigating the optimal matchings among banks, i.e., what should be the best partners of prospective bank mergers that are more likely to maximize the overall performance of the whole banking sector? To answer this question, we propose a hybrid DEA methodology that operates over two levels. The first level entails solving an inverse DEA (IDEA) model to evaluate the optimal gains that could potentially be generated out of pairwise consolidations among banks. As a result, all productive post-merger banks, i.e., those mergers that have real potential for gains’ generation, are duly discerned. In the second level, a DEA procedure integrating a standard DEA model with a greedy heuristic is devised to select the best pairs of merging banks based on the post-merger banks’ expected outcomes. Here, the best prospective merger plan is derived for the whole banking sector out of the entire sample of banks. Using data from the Office of the Superintendent of Financial Institutions (OSFI) database, the pertinence of the proposed methodology is shown by evaluating the potential merger gains of 28 Canadian banks prior to building the associated best prospective merger plan.
Keywords: Data Envelopment Analysis; Business; Finance; Banking; Accounting; Sustainability; Efficiency; Performance; Productivity; Total Factor Productivity; Frontier Analysis (search for similar items in EconPapers)
JEL-codes: C44 C5 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.worldscientific.com/doi/pdf/10.1142/9781800615786_0009 (application/pdf)
https://www.worldscientific.com/doi/abs/10.1142/9781800615786_0009 (text/html)
Ebook Access is available upon purchase.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9781800615786_0009
Ordering information: This item can be ordered from
Access Statistics for this chapter
More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().