Convex Optimization for Portfolio Construction

Reactive Publishing

Convex Optimization for Portfolio Construction provides a structured introduction to the use of optimization methods in modern portfolio design. Written for quantitative finance readers, portfolio analysts, and technically oriented investors, this book explains how convex methods can be used to model allocation problems with clearer assumptions, measurable constraints, and practical trade-offs.

The book covers core portfolio construction concepts including risk budgeting, factor exposure, transaction costs, turnover limits, and large-scale allocation models. Rather than treating optimization as a black box, it focuses on the logic behind model formulation, constraint design, objective functions, and implementation decisions.

Topics include mean-variance optimization, constrained allocation, regularization, risk parity concepts, factor-aware portfolios, cost-aware rebalancing, and scalable approaches for larger investment universes. The emphasis is on building models that are interpretable, testable, and suitable for real-world portfolio research.

Designed as a technical guide, this book is best suited for readers with some background in finance, statistics, or programming who want a deeper understanding of how optimization frameworks are applied to portfolio construction.