https://don-huang.com/tags/course/CourseHugo Blox Builder (https://hugoblox.com)enThu, 03 Sep 2026 00:00:00 +0000https://don-huang.com/media/icon_hu_982c5d63a71b2961.pnghttps://don-huang.com/tags/course/https://don-huang.com/events/hkust-msc-fm-orientation-2026-09-03/Thu, 03 Sep 2026 00:00:00 +0000https://don-huang.com/events/hkust-msc-fm-orientation-2026-09-03/<h2 id="about-the-course">About the Course</h2> <p><strong>MAFS6010H Quantitative Investment Systems: Data, AI, Portfolio Engineering and Execution</strong> (Winter 2026-27, Jan 2027) is taught by Dr. Don Huang at HKUST. The course equips students with a practical and systematic understanding of how mathematical, statistical, and computational models are transformed into real-world investment decisions in modern asset management. Rather than treating quantitative investing as a collection of isolated techniques, the course follows the complete investment decision chain:</p> <p><strong>Information → model → forecast → portfolio → trade → monitoring and validation.</strong></p> <p>A central theme is the gap between an idealized mathematical model and a deployable investment system: expected returns, covariance matrices, and machine-learning forecasts are estimated with error; portfolio solutions may be unstable; and an attractive theoretical strategy may perform very differently once turnover, liquidity, transaction costs, taxes, and execution are taken into account. Students learn not only how to construct a model, but how to challenge it, understand when it can be trusted, and determine whether it can survive real-world implementation.</p> <h2 id="key-topics">Key Topics</h2> <ul> <li>Point-in-time financial data and prevention of research leakage</li> <li>Data quality, survivorship bias, and look-ahead bias</li> <li>Factor investing and factor risk models</li> <li>Covariance estimation and risk modelling</li> <li>Estimation-aware, robust, and constrained portfolio optimization</li> <li>Benchmark-relative portfolio engineering</li> <li>Direct indexing and tax-loss harvesting</li> <li>Multi-period rebalancing under transaction costs and tax considerations</li> <li>Financial machine learning methods</li> <li>Large Language Models (LLMs) and AI-generated investment signals</li> <li>AI-assisted quantitative investment workflows</li> <li>Market impact, liquidity, and transaction cost analysis</li> <li>Optimal execution and implementation strategies</li> </ul> <h2 id="quantitative-investment-system-project">Quantitative Investment System Project</h2> <p>A major component of the course is a Quantitative Investment System Project, in which students design and develop a complete quantitative investment process integrating data management, signal generation, risk modelling, portfolio construction, implementation, and performance evaluation. Assessment emphasizes methodological soundness, robustness, transparency of assumptions, and practical deployability.</p> <p>The course is designed for students interested in quantitative finance, asset management, financial data science, and AI-driven investment systems, and is suitable for students with backgrounds in mathematics, statistics, computer science, engineering, or finance. <strong>We warmly encourage eligible students to enroll and join us in this journey from data to deployable investment systems.</strong></p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ><img src="https://don-huang.com/20260903/HKUST_MAFS6010H_Orientation_Photo.jpg" alt="Orientation Photo" loading="lazy" data-zoomable /></div> </div></figure> </p> <h2 id="downloads">Downloads</h2> <ul> <li> </li> </ul>