Portfolio Optimization: Theory and Application

This comprehensive guide to the world of financial data modeling and portfolio design is a must-read for anyone looking to understand and apply portfolio optimization in a practical context. It bridges the gap between mathematical formulations and the design of practical numerical algorithms. It explores a range of methods, from basic time series models to cutting-edge financial graph estimation approaches. The portfolio formulations span from Markowitz's original 1952 mean–variance portfolio to more advanced formulations, including downside risk portfolios, drawdown portfolios, risk parity portfolios, robust portfolios, bootstrapped portfolios, index tracking, pairs trading, and deep-learning portfolios. Enriched with a remarkable collection of numerical experiments and more than 200 figures, this is a valuable resource for researchers and finance industry practitioners. With slides, R and Python code examples, and exercise solutions available online, it serves as a textbook for portfolio optimization and financial data modeling courses, at advanced undergraduate and graduate level.

Table of Contents:
Preface; 1. Introduction; I. Financial Data: 2. Financial data: stylized facts; 3. Financial data: IID modeling; 4. Financial data: time series modeling; 5. Financial data: graphs; II. Portfolio Optimization: 6. Portfolio basics; 7. Modern portfolio theory; 8. Portfolio backtesting; 9. High-order portfolios; 10. Portfolios with alternative risk measures; 11. Risk parity portfolios; 12. Graph-based portfolios; 13. Index tracking portfolios; 14. Robust portfolios; 15. Pairs trading portfolios; 16. Deep learning portfolios; Appendices: Appendix A. Convex optimization theory; Appendix B. Optimization algorithms.

About the Author :
Daniel P. Palomar is a Professor at the Hong Kong University of Science and Technology. He is recognized as EURASIP Fellow, IEEE Fellow, and Fulbright Scholar, and recipient of numerous research awards. His current research focus is on convex optimization applications in signal processing, machine learning, and finance. He is the author of many research articles and books, including 'Convex Optimization in Signal Processing and Communications'.

Review :
'Daniel Palomar's book is a hands-on guide to portfolio optimization at the research frontier. By integrating financial data modeling, code, equations, and real-world data, it bridges theory and practice. A must-read for aspiring data-driven portfolio managers and researchers seeking to stay updated with the latest advancements.' Kris Boudt, Ghent University, Vrije Universiteit Brussel and Vrije Universiteit Amsterdam 'An invaluable reference for single period portfolio optimization under heavy tails. Palomar emphasizes the connections between portfolio methods as well as their differences, and explores tools for ameliorating their flaws rather than glossing over them.' Peter Cotton, Author of Microprediction: Building an Open AI Network 'Dan Palomar's book is a comprehensive treatment of portfolio optimization, covering the complete range from traditional optimization to more sophisticated methods of robust portfolio construction and machine learning algorithms. Directed towards graduate students and quantitative asset managers, any practitioner who builds financial portfolios would be well served by knowing everything in this book.' Dev Joneja, Chief Risk Officer, ExodusPoint Capital Management 'Professor Palomar's Portfolio Optimization: Theory and Application is a remarkable contribution to the field, bridging advanced optimization techniques with real-world portfolio design. Unlike traditional texts, it integrates heavy-tailed modeling, graph-based methods, and robust optimization with a practical, algorithmic focus. This book is an invaluable resource for those seeking a cutting-edge, computationally sound approach to portfolio management.' Marcos Lopez de Prado, OMC PhD, Global Head of Quantitative R&D at Abu Dhabi Investment Authority, and Professor of Practice at Cornell University 'Daniel P. Palomar's Portfolio Optimization: Theory and Application is a definitive guide at the nexus of financial data modeling and optimal portfolio design. This text is distinguished by its extensive and systematic exploration of a wide array of portfolio strategies, ranging from traditional mean-variance models to state-of-the-art graph-based and deep-learning methods. Palomar masterfully integrates rigorous optimization theory with practical numerical algorithms. This approach renders complex mathematical methods and algorithms accessible to a wide audience and applicable to real-world scenarios. With supplemental resources like slides, R and Python code examples, and exercise solutions available online, this book is indispensable for researchers and practitioners eager to translate theoretical concepts into practical, effective algorithm development.' Gesualdo Scutari, Purdue University 'I highly recommend Palomar's book Portfolio Optimization to anyone interested in constructing good portfolios using computational optimization. It collects methods from an enormous literature into one volume, using common notation accessible to any STEM researcher, with clear explanations, discussion, and comparisons of different methods. It is required reading for all of my finance-curious students.' Stephen Boyd, Stanford University