Teaching
Quantitative Finance
A course in portfolio construction
A quantitative portfolio-construction course in 14 sessions: return and risk measurement, portfolio construction, and full strategy backtesting, with worked Python examples throughout. Students build portfolios, estimate factor models, hunt for alpha, and run a semester-long Fund Project under real backtesting discipline.
Topics
- Mean-variance optimization, rebalancing, and estimation error
- Covariance and factor models
- Momentum, reversals, and the investment horizon
- CAPM, multifactor models, APT, and pricing tests
- Characteristics versus covariances; finding alpha
- The Black-Litterman model
- Event studies and strategy examples (betting against beta, quality minus junk, macro momentum)
- Backtesting and evaluation
Audience
Advanced undergraduate students.
Materials
A complete undergraduate textbook (~270 pp., 14 chapters with exercises and a full answer key), 14 lecture decks, four team projects plus the Fund Project.
Chapters map one-to-one onto sessions, with worked Python examples throughout and end-of-chapter exercises with a full answer key. Graded work is built around four team projects (portfolios, the frontier, factor covariance, momentum and Black-Litterman) plus a semester-long Fund Project with its own rules and deadline calendar.