Quantitative Portfolio Optimization
Advanced Techniques and Applications
- 386 pages
- English
- PDF
- Available on iOS & Android
Quantitative Portfolio Optimization
Advanced Techniques and Applications
About this book
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.
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Information
Table of contents
- Cover
- Half Title Page
- Title Page
- Copyright
- Contents
- Preface
- Acknowledgements
- About the Authors
- CHAPTER 1: Introduction
- CHAPTER 2: History of Portfolio Optimization
- PART ONE: Foundations of Portfolio Theory
- PART TWO: Risk Management
- PART THREE: Dynamic Models and Control
- PART FOUR: Machine Learning and Deep Learning
- PART FIVE: Backtesting
- Appendix
- Bibliography
- Index
- EULA