An Integrated Hypothesis Testing and Machine Learning Based Forecasting Approach for Macroeconomic Drivers of Mergers and Acquisitions in England

Mergers and Acquisitions play a pivotal role in corporate strategy and economic restructuring. The present study investigates the effect of major macroeconomic indicators such as Gross Value Added (GVA), Exchange Rate, Unemployment Rate, Consumer Price Index (CPI), and Bank Rate on M&A activity in the United Kingdom by using 238 monthly observations for the period January 2004 to October 2023. A hybrid econometric-machine learning framework is implemented for research hypothesis tests and enhanced prediction, including OLS regression, ridge regression, and lasso regression. Empirical results indicate that GVA and Exchange Rate exert positive influences on M&A activity, while Unemployment Rate, as well as Bank Rate, negatively affect it, with CPI playing a very minimal role in this respect. Especially, performance evaluation shows that lasso slightly outperforms OLS and ridge regressions. In addition to establishing statistical significance, the hybrid approach brings the supplementary value of machine learning regularisation techniques in the clarification of the association between macroeconomic factors and M&A. This study establishes a scalable foundation framework for policy-orientated forecasting and a methodological blueprint to extend in the future with analysis of structural breaks and sectoral or cross-country contexts by combining advanced machine learning architectures.