Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification

TL;DR

This paper introduces a novel approach using the Negative Selection Algorithm from Artificial Immune Systems to identify merger and acquisition targets, especially for novice firms, demonstrating its practical application and theoretical basis.

Contribution

It presents a new methodology leveraging Artificial Immune Systems for M&A target identification, focusing on novice firms and providing both theoretical insights and a practical case study.

Findings

Effective identification of M&A targets for novice firms

Demonstrated generalization capabilities of the algorithm

Practical implementation through a detailed case study

Abstract

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.