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In this paper, we propose a new methodology based on the Negative SelectionAlgorithm that belongs to the field of Computational Intelligence,specifically, Artificial Immune Systems to identify takeover targets. Althoughconsiderable research based on customary statistical techniques and somecontemporary Computational Intelligence techniques have been devoted toidentify takeover targets, most of the existing studies are based upon multipleprevious mergers and acquisitions. Contrary to previous research, the noveltyof this proposal lies in its ability to suggest takeover targets for novicefirms that are at the beginning of their merger and acquisition spree. We firstdiscuss the theoretical perspective and then provide a case study with detailsfor practical implementation, both capitalizing from unique generalizationcapabilities of artificial immune systems algorithms.

Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification