grad-event-study

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

事件研究法 (Event Study)

Overview

概述

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.

事件研究法（Fama等人，1969；MacKinlay，1997）通过将实际收益与预期（正常）收益模型进行对比，分离出特定事件带来的异常收益。事件窗口内的累计异常收益（CAR）用于量化市场的整体反应。

When to Use

适用场景

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

衡量市场对盈利公告、并购、政策变动或监管事件的反应
检验半强式市场有效性
量化公司披露信息的经济意义
比较不同事件类型或企业特征下的市场反应

When NOT to Use

不适用场景

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

事件日期模糊或信息逐步泄露
混淆事件与事件窗口重叠
公司股票流动性差，存在大量零收益交易日
事件已被广泛预期，并在事件窗口前已完全反映在股价中

Assumptions

假设条件

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

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.

核心假设：

事件日期可精准确定，且事件具有意外性
事件窗口内无混淆事件发生
估计窗口内的正常收益模型设定正确
市场微观结构效应（如交易清淡、买卖价差波动）不会扭曲收益

Methodology

研究方法

Step 1 — Define Event and Windows

步骤1 — 定义事件与窗口

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.

确定事件日期（第0天）。设定估计窗口（例如[-250, -11]）以估计正常收益。设定事件窗口（例如[-1, +1]或[-5, +5]）以捕捉市场反应。

Step 2 — Estimate Normal Returns

步骤2 — 估计正常收益

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.

使用市场模型：Ri,t = αi + βi × Rm,t + εi,t，在估计窗口内进行估计。替代模型包括恒定均值收益模型或Fama-French因子模型。模型规格详见

references/

。

Step 3 — Compute Abnormal and Cumulative Abnormal Returns

步骤3 — 计算异常收益与累计异常收益

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.

AR = 事件窗口内每日实际收益 - 预期收益。CAR = 事件窗口内AR的总和。计算跨公司的累计平均异常收益（CAAR）。

Step 4 — Statistical Testing

步骤4 — 统计检验

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.

使用参数检验（横截面t检验、Patell检验）和非参数检验（符号检验、秩检验）检验原假设H₀: CAR = 0。为保证稳健性，需同时报告两种检验结果。

Output Format

输出格式

markdown

undefined

markdown

undefined

Event Study: [Event Description]

Window Design

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

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

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]

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]

[If applicable: regression of CAR on firm characteristics]

Limitations

[Note any confounding events or assumption violations]

undefined

[Note any confounding events or assumption violations]

undefined

Gotchas

注意事项

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

事件日期聚类（如全行业监管）违反横截面独立性假设
短估计窗口会导致正常收益参数存在噪声
长事件窗口会增加混淆事件发生的概率
低价股和流动性差的证券会人为抬高异常收益
市场模型假设beta恒定，结构突变会使该假设失效
发表偏倚：未发现显著CAR的研究很少被发表

References

参考文献

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.

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.