Event Studies in Finance
Theory, methodology, and empirical applications
This module provides a comprehensive treatment of event study methodology, the workhorse empirical tool in financial economics. The course progresses from foundational concepts through advanced extensions, combining rigorous statistical theory with hands-on Python implementations.
Sessions¶
| # | Topic | Key Concepts |
|---|---|---|
| 1 | Foundations of Event Studies | Event study design, timeline structure, literature overview |
| 2 | Market Model and Normal Returns | Market model estimation, OLS benchmarks, alternative models |
| 3 | Measuring Abnormal Returns | AR, CAR, BHAR computation, aggregation methods |
| 4 | Statistical Inference — Parametric | t-tests, cross-sectional tests, Patell test, BMP test |
| 5 | Statistical Inference — Nonparametric | Sign test, rank test, bootstrap methods, robustness |
| 6 | Cross-Sectional Analysis | Explaining CARs, multivariate regressions, selection bias |
| 7 | Long-Horizon Event Studies | BHAR vs. CAR, calendar-time portfolios, Fama-French approach |
| 8 | Extensions and Special Topics | Confounding events, event-induced variance, intraday studies |
| 9 | Design and Implementation | Practical workflow, sample selection, data issues, pitfalls |
| 10 | Capstone Case Studies | Complete applied event studies from start to finish |
How to use these notebooks¶
Each session builds on the previous one. The markdown cells develop the econometric theory with full derivations, while the code cells implement each method step by step. Exercises at the end of each session provide practice opportunities.