Weekly lecture notes and Google Colab notebooks, posted as the course progresses. See the full syllabus for grading, readings, and course policies.
Course Structure
The course is organized around four questions rather than a chapter sequence:
- 1. Is it a good strategy? (sessions 1–6) — market efficiency, factor models, data mining and research protocol, luck vs. skill, the factor zoo (including ML/LLM signals, judged by the same tests).
- 2. Executing cheaply (sessions 7–8) — portfolio construction and risk models, trading costs, shorting, and capacity.
- 3. Executing with an ETF (session 9) — the active vs. passive debate.
- 4. Macro implications (sessions 10–12) — inelastic markets and price efficiency, then crowding and fragility: the August 2007 quant unwind, ETF liquidity mismatch (August 2015, bond ETFs in March 2020), and herding from common signals, including AI-driven trading.
Schedule
| Week | Dates | Theme |
|---|---|---|
| Week 1 | Oct 19 & 21 | Is it a good strategy? Market efficiency; basic models & factor choice |
| Week 2 | Oct 26 & 28 | Fundamental factor models (Case #1: GMO); data mining & research protocol |
| Week 3 | Nov 2 & 4 | Performance evaluation: luck vs. skill; the factor-zoo debate |
| Week 4 | Nov 9 & 11 | Executing cheaply: portfolio construction (Case #2: Martingale); trading costs & capacity |
| Week 5 | Nov 16 & 18 | Executing with an ETF: active vs. passive; inelastic markets & price efficiency |
| No class Nov 23 & 25 — Thanksgiving recess / university holiday | ||
| Week 6 | Nov 30 & Dec 2 | Macro implications: crowding & fragility — historical episodes, then AI-driven herding |
| Week 7 | Dec 7 & 9 | Team presentations of backtesting projects |
| Term B ends Dec 10 | ||