Merger arbitrage looks simple from the outside: buy the target, hedge the acquirer, and wait for the deal to close. In practice, it is a demanding event-driven discipline shaped by legal terms, regulatory friction, financing risk, timing uncertainty, and abrupt break losses. This book is written for quant practitioners and hedge fund analysts who want to move beyond discretionary deal judgment and build a systematic, production-ready merger arbitrage strategy grounded in rigorous modeling and executable portfolio design. The book takes the reader from first principles to an institutional framework for trading announced public M&A transactions. It covers deal mechanics, state-machine representations of the deal lifecycle, payoff replication across cash, stock, and mixed consideration structures, spread definition and decomposition, completion-probability modeling, survival models for time-to-close, Bayesian event updates, and expected value formation that integrates p(close), timing, and loss-given-break. It then shows how to translate deal-level forecasts into position sizing, portfolio optimization, clustered-risk controls, stress testing, execution logic, and realistic event-driven backtesting. A distinguishing strength of the book is its insistence on research discipline: timestamp hygiene, as-of data integrity, borrow and financing realism, and validation standards that match live trading conditions. Readers should be comfortable with probability, statistics, and basic machine learning, but the focus throughout is practical and economic rather than abstract. The resul