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'.

• 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.

'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