Quantitative Investing

This advanced elective provides a rigorous, hands-on introduction to quantitative portfolio management and systematic investment strategies. Co-taught by Kent Daniel of Columbia Business School and Giuseppe Paleologo of Balyasny Asset Management, the course bridges academic theory and industry practice, equipping students with the analytical and computational tools used by leading asset managers.

Topics include factor model construction and estimation, mean-variance optimization, risk modeling (including BARRA-style approaches), backtesting methodology, transaction cost modeling, performance attribution, and dynamic portfolio optimization. Students will work with industry-standard financial databases such as CRSP, Compustat, and TAQ using Python and SQL, and will design and backtest an original quantitative strategy as a final group project. Guest speakers from top quantitative investment firms complement the lecture material throughout the term.

The course is ideal for aspiring buy-side equity professionals, macro and credit investors who apply a quantitative overlay, and data scientists supporting systematic investment processes. It is not designed for arbitrageurs, market makers, or derivatives traders.

Prerequisites: This course carries a higher technical bar than most MBA electives. Students must have completed the finance core and Capital Markets and Investments courses (or equivalents), the CBS Python and database/SQL courses, and must be comfortable with linear algebra and introductory convex optimization at the level of Strang and Boyd and Vandenberghe respectively. Students without prior exposure to matrix operations or constrained optimization are strongly advised against enrolling. Familiarity with AI-assisted coding tools such as Claude Code, Cursor, or Copilot is encouraged, as these will be actively used in assignments and the final project.