a-share-event-quant

Business

A股事件驱动量化/事件研究法。当用户说"事件研究"、"event study"、"事件驱动量化"、"公告效应"、"CAR"、"异常收益"、"XX公告后会怎样"、"事件窗口"时触发。基于 cn-stock-data 获取K线数据，运用事件研究法量化分析特定事件对股价的影响。支持研报风格（formal）和快速分析风格（brief）。

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A股事件驱动量化

数据源

SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts"
python "$SCRIPTS/cn_stock_data.py" kline --code [CODE] --freq daily --start [日期]
python "$SCRIPTS/cn_stock_data.py" kline --code SH000300 --freq daily --start [日期]
python "$SCRIPTS/cn_stock_data.py" quote --code [CODE]

补充：通过 web 搜索获取事件日期、公告内容。

Workflow

Step 1: 定义事件与事件日

明确事件类型（业绩预告/定增/回购/高管增持等）
确定精确的事件日（公告日 T=0）

Step 2: 设定窗口

估计窗口：[-250, -11]（用于估计正常收益模型）
事件窗口：[-10, +10] 或 [-5, +20]（观察异常收益）

Step 3: 计算正常收益（市场模型）

R_normal = α + β × R_market
α, β 在估计窗口内通过 OLS 回归得到

Step 4: 计算异常收益

AR_t = R_actual - R_normal（每日异常收益）
CAR = Σ AR_t（累计异常收益）
CAAR = mean(CAR) across events（平均累计异常收益）

Step 5: 统计检验 + 输出

t 检验：CAR / (σ_AR × √T)
|t| > 1.96 → 5% 水平显著

维度	formal	brief
模型	市场模型+Fama-French	市场模型
检验	多种统计量	仅 t 统计量
图表	CAR 时序图+置信区间	CAR 数值

默认风格：brief。

关键规则

事件日必须精确——公告日 vs 实施日区别很大
A 股盘后公告次日生效，需注意 T+1 定义
避免事件窗口重叠（同一股票短期内多个事件）
样本量 > 30 才有统计意义

使用示例

示例 1: 基本使用

# 调用 skill
result = run_skill({
    "param1": "value1",
    "param2": "value2"
})

示例 2: 命令行使用

python scripts/run_skill.py --input data.json