Grad Event Study

事件研究法 (Event Study)

Overview

The event study method (Fama et al., 1969; MacKinlay, 1997) isolates the abnormal return attributable to a specific event by comparing actual returns against a model of expected (normal) returns. Cumulative abnormal returns (CAR) over an event window quantify the total market reaction.

When to Use

• Measuring market reaction to earnings announcements, M&A, policy changes, or regulatory events
• Testing semi-strong form market efficiency
• Quantifying the economic significance of corporate disclosures
• Comparing market reactions across different event types or firm characteristics

When NOT to Use

• The event date is ambiguous or the information leaked gradually
• Confounding events overlap with the event window
• The firm's stock is illiquid with many zero-return days
• The event was widely anticipated and fully priced before the event window

Assumptions

IRON LAW: Event study validity requires that the event was UNANTICIPATED —
if the market priced it in before the event window, abnormal returns will
be zero even if the event matters.

Key assumptions:

• Event date is precisely identifiable and the event was unexpected
• No confounding events occur within the event window
• The normal return model is correctly specified during the estimation window
• Market microstructure effects (thin trading, bid-ask bounce) do not distort returns

Methodology

Step 1 — Define Event and Windows

Identify the event date (day 0). Set estimation window (e.g., [-250, -11]) to estimate normal returns. Set event window (e.g., [-1, +1] or [-5, +5]) to capture the reaction.

Step 2 — Estimate Normal Returns

Use the market model: Ri,t = αi + βi × Rm,t + εi,t estimated over the estimation window. Alternatives include constant mean return or Fama-French factors. See references/ for model specifications.

Step 3 — Compute Abnormal and Cumulative Abnormal Returns

AR = Actual return - Expected return for each day in the event window. CAR = sum of ARs over the event window. Compute CAAR (cumulative average abnormal return) across firms.

Step 4 — Statistical Testing

Test H₀: CAR = 0 using parametric tests (cross-sectional t-test, Patell test) and non-parametric tests (sign test, rank test). Report both for robustness.

Output Format

## Event Study: [Event Description]

### Window Design
| Window | Period | Rationale |
|--------|--------|-----------|
| Estimation | [-250, -11] | [rationale] |
| Event | [-1, +1] | [rationale] |

### Abnormal Returns
| Day | AR (%) | t-stat |
|-----|--------|--------|
| -1 | x.xx | x.xx |
| 0 | x.xx | x.xx |
| +1 | x.xx | x.xx |

### Cumulative Abnormal Returns
| Window | CAR (%) | t-stat | p-value | Significant? |
|--------|---------|--------|---------|-------------|
| [-1, +1] | x.xx | x.xx | x.xx | [Yes/No] |

### Cross-Sectional Analysis
- [If applicable: regression of CAR on firm characteristics]

### Limitations
- [Note any confounding events or assumption violations]

Gotchas

• Clustering of event dates (e.g., industry-wide regulation) violates cross-sectional independence
• Short estimation windows produce noisy normal return parameters
• Long event windows increase the probability of confounding events
• Penny stocks and illiquid securities inflate abnormal returns artificially
• The market model assumes constant beta — structural breaks invalidate this
• Publication bias: studies finding zero CAR are rarely published

References

• MacKinlay, A. C. (1997). Event studies in economics and finance. *Journal of Economic Literature*, 35(1), 13-39.
• Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The adjustment of stock prices to new information. *International Economic Review*, 10(1), 1-21.
• Kolari, J. W., & Pynnönen, S. (2010). Event study testing with cross-sectional correlation of abnormal returns. *Review of Financial Studies*, 23(11), 3996-4025.