Title: Machine learning-based financial analysis of merger and acquisitions

Authors: S. Kalaivani; K. Sivakumar; J. Vijayarangam

Addresses: Department of Mathematics, Sathyabama Institute of Science and Technology, Tamil Nadu, India ' Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, India ' Department of Mathematics, Sri Venkateshwara College of Engineering, Tamil Nadu, India

Abstract: Stock market analysis and forecasting is one of the most sought-after areas of study. As anyone who has observed stock market movements, even an outsider, knows very well the enormous amount of risk involved, with numerous factors affecting it, its study is quite intriguing and interesting, let alone a profitable one. So, it is imperative we look for prediction tools to help us through the process. As we dwell into already available tools in the fields of economics and statistics, we can sense a need of innovation from other evolving domains and the immediate one is the field of machine learning. This paper is a stock market price forecasting one, using neural network model, employed on financial data concerning pre- and post-mergers of companies. We have collected data of pre-merger and post-merger states, formed a neural network model to fit it and used the model to forecast. The predictions were reasonably accurate.

Keywords: neural network; financial forecasting; merger; acquisitions.

DOI: 10.1504/IJESMS.2024.138286

International Journal of Engineering Systems Modelling and Simulation, 2024 Vol.15 No.3, pp.106 - 111

Received: 26 Oct 2021
Accepted: 28 Jan 2022
Published online: 01 May 2024 *

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