Predictive assessment of M&A success: A Logit model and trading strategy on expected returns

This thesis analyzes historical M&A transactions to develop a predictive model, employing logistic regression to estimate the probability of deal success based on selected economic and financial indicators. Using the probabilities derived from this model, a trading strategy is formulated to evaluate the expected returns achievable from investments guided by these forecasts. The empirical analysis provides evidence of the predictive accuracy of the logistic model and the effectiveness of the proposed strategy in generating abnormal market returns.