Practitioner Briefs
AI in Asset Management: Tools, Applications, and Frontiers
This collection of Practitioner Briefs distills each chapter of the AI in Asset Management: Tools, Applications, and Frontiers book into insights that support better research, portfolio construction, risk management, and operational decision-making. Each brief highlights essential concepts, real-world applications, and implementation considerations for professionals evaluating how AI can enhance investment workflows as well as broader organizational capabilities.
Built for portfolio managers, analysts, CIOs, and risk leaders, this series offers a concise way to engage with advanced concepts and sets the stage for deeper investigation into the chapters that matter most to their specific roles and investment responsibilities.
Explore the Briefs
Practitioner Brief
AI in Asset Management: Tools, Applications, and Frontiers — Book
Practitioner Brief
Chapter 1: Unsupervised Learning I: Overview of Techniques
Practitioner Brief
Chapter 2: Unsupervised Learning II: Network Theory
Practitioner Brief
Chapter 3: Support Vector Machines
Practitioner Brief
Chapter 4: Ensemble Learning in Investment: An Overview
Practitioner Brief
Chapter 5: Deep Learning
Practitioner Brief
Chapter 6: Reinforcement Learning and Inverse Reinforcement Learning
Practitioner Brief
Chapter 7: Natural Language Processing
Practitioner Brief
Chapter 8: Machine Learning in Commodity Futures, Bridging Data, Theory, and Return Predictability
Practitioner Brief
Chapter 9: Quantum Computing for Finance
Practitioner Brief