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a-share-credit-risk-quant

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A股信用风险量化/违约概率分析。当用户说"信用风险"、"违约概率"、"credit risk"、"PD"、"信用评分"、"违约预警"、"信用量化"时触发。基于 cn-stock-data 获取数据，量化评估上市公司信用风险。支持 formal/brief 两种输出风格。

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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-credit-risk-quant/SKILL.md

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信用风险量化/违约概率分析助手

数据获取

通过 cn-stock-data skill 获取数据：

  • 财务数据: 资产负债表/利润表/现金流
  • 市场数据: 股价/波动率/市值
  • 评级数据: 外部信用评级

分析工作流

Step 1: Merton模型

  • 公司股权 = 对公司资产的看涨期权
  • 资产价值 V 和资产波动率 σ_V 的联立求解
  • 违约距离 DD = (ln(V/D) + (μ-σ²/2)T) / (σ√T)
  • 违约概率 PD = N(-DD)

Step 2: 财务指标评分

  • Altman Z-Score：Z = 1.2X1+1.4X2+3.3X3+0.6X4+X5
  • Z>2.99安全，1.81<Z<2.99灰色，Z<1.81危险
  • 现金流覆盖率：经营现金流/短期债务
  • 资产负债率/流动比率/利息保障倍数

Step 3: 机器学习违约预测

  • 特征：财务指标+市场指标+行业指标
  • 标签：ST/退市/债券违约事件
  • 模型：LightGBM/逻辑回归
  • 评估：AUC/KS/Gini系数

Step 4: 信用风险监控

  • PD时序监控：违约概率趋势变化
  • 预警阈值：PD>5%进入观察名单
  • 行业对比：同行业PD分位数
  • 事件触发：财务异常/评级下调/诉讼

Step 5: 输出报告

输出格式

formal 风格（研报级）

# [标的] 信用风险量化报告

## 一、违约概率
| 模型 | PD | 评级 |
|------|-----|------|

## 二、财务健康
[Z-Score、关键财务指标]

## 三、趋势分析
[PD时序变化]

## 四、风险提示

brief 风格（快速分析）

## [标的] 信用风险速览
- Merton PD = 0.8%，信用良好
- Z-Score = 2.5，灰色区域
- 资产负债率 55%，中等
- 建议：关注现金流变化趋势

参考 references/credit-risk-quant-guide.md 获取详细方法论与 A股实证研究。

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