[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.14317/jami.2022.563

STATISTICAL MODELLING USING DATA MINING TOOLS IN MERGERS AND ACQUISITION WITH REGARDS TO MANUFACTURE & SERVICE SECTOR

KALAIVANI, S. (Department of Mathematics, Sathyabama Institute of Science and Technology)
SIVAKUMAR, K. (Department of Mathematics, Saveetha Institute of Medical and Technical Sciences)
VIJAYARANGAM, J. (Department of Applied Mathematics, Sri Venkateshwara College of Engineering)

Publication Information

Journal of applied mathematics & informatics / v.40, no.3_4, 2022 , pp. 563-575 More about this Journal

Abstract

Many organizations seek statistical modelling facilitated by data analytics technologies for determining the prediction models associated with M&A (Merger and Acquisition). By combining these data analytics tool alongside with data collection approaches aids organizations towards M&A decision making, followed by achieving profitable insights as well. It promotes for better visibility, overall improvements and effective negotiation strategies for post-M&A integration. This paper explores on the impact of pre and post integration of M&A in a standard organizational setting via devising a suitable statistical model via employing techniques such as Naïve Bayes, K-nearest neighbour (KNN), and Decision Tree & Support Vector Machine (SVM).

Keywords

Mergers & Acquisition; data analytics; manufacturing sector; service sector;

Citations & Related Records

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