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a-share-earnings-surprise

Business
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A股业绩超预期/低预期量化分析。当用户说"业绩超预期"、"earnings surprise"、"超预期"、"低预期"、"业绩打败预期"、"不及预期"时触发。量化分析业绩公告后的市场反应。支持formal和brief风格。

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How to use this skill

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Source SKILL.md: https://github.com/aifinlab/FinClaw/blob/HEAD/skills/a-share-earnings-surprise/SKILL.md

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A股业绩超预期/低预期量化分析

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 "$SCRIPTS/cn_stock_data.py" quote --code [CODE]
python "$SCRIPTS/cn_stock_data.py" finance --code [CODE]

Workflow

Step 1: 获取财务数据和K线

Step 2: 计算业绩超预期度

  • SUE = (实际EPS - 预期EPS) / |预期EPS|
  • 或用 实际净利润 vs 上期同比趋势线

Step 3: 事件效应分析

  • 业绩公告后T+1/T+3/T+5/T+20的CAR
  • 区分超预期和低预期的不对称效应

Step 4: 业绩漂移（PEAD）

分析业绩公告后的收益率漂移持续性

Step 5: 输出

维度formalbrief
超预期度SUE计算+排名超/达/低预期
市场反应CAR序列分析公告后涨跌
漂移分析PEAD统计漂移方向
默认风格：brief。

关键规则

  1. A股业绩漂移效应（PEAD）显著存在——超预期后继续涨
  2. 负面业绩反应通常比正面更剧烈（不对称效应）
  3. 业绩预告vs正式报告可能有差异——两次都需关注
  4. 分析师一致预期是衡量超预期的最佳基准（如有）
  5. 业绩公告通常盘后发布——T+1为首个反应日

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