Unlock sophisticated investment strategies with Quantitative Portfolio Optimization: Theory and Practice by Miquel Noguer, Alberto Bueno Guerrero, and Julian Antolin Camarena. This essential guide from The Bookish Owl explores advanced mathematical techniques like mean-variance optimization, Black-Litterman Model, risk parity, factor investing, and robust optimization. Discover how to apply machine learning and reinforcement learning to build a systematic, objective approach to investment decisions in complex financial markets. Understand the underlying models, statistical analyses, and computational algorithms to identify optimal asset allocation, maximize returns, and minimize risk. Essential for finance practitioners and individual investors seeking to master modern portfolio theory and enhance investment outcomes.