Predicting Mergers and Acquisitions Success with Machine Learning

Abstract

Mergers and acquisitions (M&As) are critical strategies for organizational growth and expansion in modern business environments. However, predicting the success or failure of these processes is a complex challenge due to the multiple factors involved. Traditional models often fall short in accurately forecasting outcomes because they cannot analyze multidimensional and complex data. Machine learning has emerged as an advanced analytical tool that can handle large datasets and improve the accuracy of predictions. To explore the application of machine learning in predicting the success of M&As and analyze its advantages and challenges. This study is a narrative review that draws upon credible academic sources and databases to collect relevant information. Various machine learning models, such as neural networks, support vector machines, and decision trees, are examined and compared with traditional prediction models. The collected data from previous studies and relevant research were comprehensively analyzed. Machine learning demonstrates the ability to analyze multidimensional data and uncover hidden patterns that traditional methods overlook. This analytical tool significantly improves the accuracy of M&A success predictions and helps reduce risks associated with these processes. However, challenges such as data quality and the need for sufficient resources remain prevalent. Machine learning has proven to be an effective tool in predicting the success of M&As by providing more precise analyses of complex data. Implementing this tool, along with improving data quality and proper post-merger management, can lead to greater success in M&A processes.