Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification: Theory and Case Study

Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification: Theory and Case Study

Abstract

In this paper, we propose a new model based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence (specifically, Artificial Immune Systems - AIS) 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 acquisitions. Contrary to previous researches, the novelty of this proposal lies in the model’s 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 AIS algorithms.

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Authors
  • Paul, Satyakama
  • Janecek, Andreas
  • Buarque de Lima Neto, Fernando
  • Marwala, Tshilidzi
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Full Paper in Proceedings)
Event Title
1st BRICS Countries Congress (BRICS-CCI) and 11th Brazilian Congress (CBIC) on Computational Intelligence
Divisions
Theory and Applications of Algorithms
Subjects
Kuenstliche Intelligenz
Angewandte Informatik
Event Location
Recife, Brazil
Event Type
Conference
Event Dates
08-11 Sept, 2013
Date
September 2013
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