Advanced Portfolio Optimization

Advanced Portfolio Optimization

Quantitative Portfolio Optimization

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.

Advanced Portfolio Optimization with Excel & Python

Reactive Publishing Advanced Portfolio Optimization with Excel & Python Master Quantitative Investing with Real-World Applications Unlock the full power of modern portfolio theory, machine learning, and quantitative finance using two of the most accessible tools in your arsenal: Excel and Python. This advanced guide is designed for serious investors, analysts, and finance professionals who want to go beyond basic models and learn how to engineer high-performance portfolios. Inside, you'll find a deep dive into risk-adjusted strategies, multi-factor models, regime switching, Monte Carlo simulations, Black-Litterman adjustments, and more-anchored by code and practical Excel frameworks you can apply immediately. Whether you're managing capital or building algorithms, this book offers you the tools to: Construct robust portfolios with modern optimization techniques Combine fundamental and technical factors in allocation decisions Apply risk-parity, volatility targeting, and regime-based tilts Leverage Python for backtesting and Excel for scenario analysis Bridge academic theory with real-world portfolio management With a dual emphasis on financial insight and hands-on execution, this book is ideal for those who want more than just theory-it's for builders, quants, and future fund managers.