Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification
This work addresses a specific gap in merger and acquisition target identification for firms new to such activities, representing an incremental advancement in applying existing computational methods to a niche domain.
The paper tackles the problem of identifying merger and acquisition targets for novice firms by proposing a new methodology based on the Negative Selection Algorithm, a computational intelligence technique from artificial immune systems, and demonstrates its application through a case study.
In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence, specifically, Artificial Immune Systems to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in its ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of artificial immune systems algorithms.