Quantitative Portfolio Optimization: Advanced Techniques and Applications (Wiley Finance) - Hardcover

Noguer Alonso, Miquel; Antolin Camarena, Julian; Bueno Guerrero, Alberto

Hardcover

ISBN 10: 1394281315 ISBN 13: 9781394281312

Publisher: Wiley, 2025

Synopsis

Expert guidance on implementing quantitative portfolio optimization techniques

In Quantitative Portfolio Optimization: Theory and Practice, renowned financial practitioner Miquel Noguer, alongside physicists Alberto Bueno Guerrero and Julian Antolin Camarena, who possess excellent knowledge in finance, delve into advanced mathematical techniques for portfolio optimization. The book covers a range of topics including mean-variance optimization, the Black-Litterman Model, risk parity and hierarchical risk parity, factor investing, methods based on moments, and robust optimization as well as machine learning and reinforcement technique. These techniques enable readers to develop a systematic, objective, and repeatable approach to investment decision-making, particularly in complex financial markets.

Readers will gain insights into the associated mathematical models, statistical analyses, and computational algorithms for each method, allowing them to put these techniques into practice and identify the best possible mix of assets to maximize returns while minimizing risk. Topics explored in this book include:

• Specific drivers of return across asset classes
• Personal risk tolerance and it#s impact on ideal asses allocation
• The importance of weekly and monthly variance in the returns of specific securities

Serving as a blueprint for solving portfolio optimization problems, Quantitative Portfolio Optimization: Theory and Practice is an essential resource for finance practitioners and individual investors It helps them stay on the cutting edge of modern portfolio theory and achieve the best returns on investments for themselves, their clients, and their organizations.

About this title

About the Author

MIQUEL NOGUER ALONSO is a financial markets practitioner with 25+ years of experience in asset management. He is the Founder of the Artificial Intelligence Finance Institute and serves as Head of Development at Global AI. He is also the co-editor of the Journal of Machine Learning in Finance.

JULIÁN ANTOLÍN CAMARENA holds a Bachelor’s, Master’s and a PhD in physics. For his Master’s he worked on the foundations of quantum mechanics examining alternative quantization schemes and their application to exotic atoms to discover new physics. His PhD dissertation work was on computational and theoretical optics, electromagnetic scattering from random surfaces, and nonlinear optimization. He then went on to a postdoctoral stint with the U.S. Army Research Laboratory working on inverse reinforcement learning for human-autonomy teaming.

ALBERTO BUENO GUERRERO has two Bachelor’s degrees in physics and economics, and a PhD in banking and finance. Since he got his doctorate, he has dedicated himself to research in mathematical finance. His work has been presented at various international conferences and published in journals such as Quantitative Finance, Journal of Derivatives, Journal of Mathematics, and Chaos, Solitons and Fractals. His article “Bond Market Completeness Under Stochastic Strings with Distribution-Valued Strategies” has been considered a feature article in Quantitative Finance.

From the Back Cover

PRAISE FOR
QUANTITATIVE PORTFOLIO OPTIMIZATIONOPTIMIZATION

“This book provides an excellent exposition on portfolio optimization, serving not only as a self-contained guide to this important topic, but also modernizing the field with the latest advances in battle-tested machine learning approaches. The book is well structured and application centric. This is a must read for every quantitative portfolio manager.”
― Matthew Dixon, FRM, Ph.D., Associate Professor of Applied Math at the Illinois Institute of Technology and an Affiliate Associate Professor of the Stuart School of Business

“Quantitative Portfolio Optimization: Advanced Techniques and Applications is an essential guide for anyone seeking to navigate the complex world of modern portfolio management. This book masterfully blends the foundational principles of portfolio theory with cutting-edge advancements in risk management, dynamic models, and control systems. Its integration of machine learning and deep learning offers readers a forward-looking perspective on leveraging AI-driven techniques for optimization. What truly sets this book apart is its comprehensive approach. From theoretical insights to practical backtesting applications, it equips professionals, researchers, and students with the tools to design and refine robust investment strategies. Whether you're delving into the nuances of risk modelling or exploring dynamic portfolio control with the latest AI methodologies, this text is an invaluable resource. This book isn’t just about managing portfolios―it’s about mastering the art and science behind it. Highly recommended for anyone aiming to achieve excellence in quantitative finance and portfolio optimization.”
―Daniel Bloch, Director, Quant Finance Limited

From the Inside Flap

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.

Edition details

• Publisher: Wiley
• Publication date: 2025
• Language: English
• ISBN 10: 1394281315
• ISBN 13: 9781394281312
• Binding: Hardcover
• Edition number: 1
• Number of pages: 384