Quantitative Portfolio Optimization: Advanced Techniques and Applications is an authoritative guide on using mathematical models, statistical analyses, and computational algorithms to optimize the composition of an investment portfolio and allow for a systematic, objective, and repeatable approach to investment decision-making, especially in complex financial markets. In this book, readers will learn to identify the best possible mix of assets that can maximize returns while minimizing risk based on the investor's specific objectives and constraints. Written by Miquel Noguer Alonso, an experienced financial markets practitioner and pioneer in the field, Julián Antolín Camarena, experienced AI researcher and physicist, and Alberto Bueno Guerrero, accomplished researcher in mathematical finance, this book takes a deep dive into various methods in quantitative portfolio optimization, including mean-variance optimization, the Black-Litterman Model, risk parity and hierarchical risk parity, factor investing, machine learning models, methods based on moments, and robust optimization. Readers will learn about the unique approach and application of each of these methods and receive a variety of tools that can be used in their efforts to practically construct and manage their portfolios. Providing key knowledge on advanced mathematical techniques for portfolio optimization to solve one of the central problems in finance, Quantitative Portfolio Optimization: Advanced Techniques and Applications earns a well-deserved spot on the bookshelves of finance practitioners and academics interested in portfolio management, along with all investors looking to stay on the cutting edge of modern investment techniques.