Deep Diving into Traditional Machine Learning and Deep Learning Models Applicable for Mergers and Acquisitions

Abstract

Mergers and Acquisitions (M&A) have become crucial strategies for business growth, expansion, and optimization. However, the complex nature of M&A transactions with vast amounts of data to process and analyze, makes it increasingly challenging to make informed decisions. From deal sourcing and due diligence to post-merger integration, traditional machine learning (ML) and deep learning (DL) approaches have enormous potential for automating and enhancing the M&A process.

This research investigates how classic machine learning models and deep learning architectures can be effectively applied to different stages of the M&A function and judicial cases. We explore the applications, challenges, and the future potential of these models in enhancing decision-making and efficiency in M&A activities.