This study aims to explore the key financial metrics influencing the likelihood of companies attracting Mergers and Acquisitions (M&A) by applying a neural network model. The primary objective is to analyze how factors such as company size, return on equity (ROE), debt-to-equity ratio (DE), market capitalization (MCAP), and liquidity impact the probability of M&A. The neural network model is applied to a dataset of companies from over the period. The findings show that larger companies and those with high profitability and leverage are more likely to be involved in mergers and acquisitions, while high market capitalization and better liquidity reduce this likelihood. The model effectively captures the relationship between these financial factors and mergers and acquisition activity, though further refinement is recommended to improve prediction accuracy. This study highlights the strategic importance of financial health metrics in predicting corporate M&A behaviour.
Dr. Shravani (Sun,) studied this question.