--- name: a-share-dcf-56b8afe3192e description: "A\u80A1DCF\u4F30\u503C\u6A21\u578B/\u73B0\u91D1\u6D41\u6298\u73B0\u3002\u5F53\u7528\u6237\u8BF4\u201CDCF\u201D\u3001\u201C\u73B0\u91D1\u6D41\u6298\u73B0\u201D\u3001\u201CDCF\u4F30\u503C\u201D\u3001\u201C\u5185\u5728\u4EF7\u503C\u201D\u3001\u201Cintrinsic value\u201D\u3001\u201C\u5E2E\u6211\u7B97\u4E00\u4E0BXX\u503C\u591A\u5C11\u94B1\u201D\u3001\u201C\u5408\u7406\u4F30\u503C\u201D\u3001\u201C\u76EE\u6807\u4EF7\u63A8\u5BFC\u201D\u65F6\u89E6\u53D1\u3002\u57FA\u4E8E cn-stock-data \u83B7\u53D6\u5386\u53F2\u8D22\u52A1\u6570\u636E\uFF0C\u6784\u5EFA\u81EA\u7531\u73B0\u91D1\u6D41\u6298\u73B0\u6A21\u578B\uFF08FCFF\uFF09\uFF0C\u8BA1\u7B97 WACC\uFF0C\u8F93\u51FA\u542B\u654F\u611F\u6027\u5206\u6790\u7684\u4F30\u503C Excel\u3002\u652F\u6301\u673A\u6784 DCF \u62A5\u544A\u98CE\u683C\uFF08formal\uFF09\u548C\u4E2A\u4EBA\u4F30\u503C\u7B14\u8BB0\u98CE\u683C\uFF08brief\uFF09\u3002\u4E0D\u9002\u7528\u4E8E\u53EF\u6BD4\u516C\u53F8\u4F30\u503C\u6CD5\uFF08\u7528 a-share-comps\uFF09\u6216\u8D22\u62A5\u5206\u6790\uFF08\u7528 a-share-earnings-analysis\uFF09\u3002" --- ## 数据源 ```bash SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts" # 历史财务指标（收入/利润/折旧/资本开支/营运资金等） python "$SCRIPTS/cn_stock_data.py" finance --code [CODE] # 最新行情（市值、股价，用于 WACC 和对比） python "$SCRIPTS/cn_stock_data.py" quote --code [CODE] # 行业可比公司行情（用于 Beta/WACC 参考） python "$SCRIPTS/cn_stock_data.py" quote --code [COMP1],[COMP2],[COMP3] ``` 补充：通过 web 搜索获取： - 无风险利率（10 年期国债收益率） - 行业 Beta 参考值 - 公司债务成本（如有公开债券） - 分析师收入/利润预测（用于交叉验证） ## Workflow ### Step 1: 数据收集 1. 通过 cn-stock-data finance 获取过去 3-5 年财务数据 2. 通过 cn-stock-data quote 获取当前市值 3. Web 搜索：10 年期国债收益率、公司有息负债利率 ### Step 2: 历史分析 分析过去 3-5 年的关键财务趋势： - 收入增速趋势 - EBIT/EBITDA 利润率趋势 - 资本开支/收入 比率 - 营运资金/收入 比率 - 折旧摊销/收入 比率 - 有效税率 ### Step 3: 收入预测 - 基于历史趋势 + 行业增速 + web 搜索的分析师预期 - 预测期：通常 5 年（高增长公司可延长到 7-10 年） - 构建 Base 情景，可选 Bull/Bear ### Step 4: 利润率与费用建模 - 毛利率预测（基于历史趋势 + 竞争格局判断） - 运营费用率预测 - EBIT/EBITDA 推导 - 折旧摊销预测（基于历史比率或资本开支的固定比例） - 资本开支预测 - 营运资金变动预测 ### Step 5: 自由现金流(FCFF)计算 ``` FCFF = EBIT × (1 - 税率) + 折旧摊销 - 资本开支 - 营运资金增加 ``` ### Step 6: WACC 计算 ``` A 股 WACC 参数: - 无风险利率 Rf: 10 年期国债收益率（web 搜索获取，通常 2.5%-3.5%） - 市场风险溢价 ERP: A 股通常取 5%-7%（高于美股的 4%-6%，反映新兴市场溢价） - Beta: 优先用 web 搜索获取；若不可用，取行业平均或根据公司特征估计 - 股权成本 Ke = Rf + Beta × ERP - 债务成本 Kd: 公司实际借款利率或同类公司债利率 - 税率: A 股一般企业 25%，高新技术企业 15% - WACC = Ke × (E/(E+D)) + Kd × (1-T) × (D/(E+D)) ``` ### Step 7: 终值(Terminal Value)计算 两种方法取平均或选其一： - **永续增长法**: TV = FCFF_最后年 × (1+g) / (WACC - g) - A 股永续增长率 g 通常取 2%-3%（GDP 长期增速预期） - **退出倍数法**: TV = EBITDA_最后年 × Exit Multiple - Exit Multiple 参考行业可比公司 EV/EBITDA 中位数 ### Step 8: 估值汇总与敏感性分析 **估值桥**: ``` FCFF 现值之和 + 终值现值 = 企业价值(EV) - 净债务（有息负债 - 现金） - 少数股东权益 = 股权价值 / 总股本 = 每股价值 vs 当前股价 → 上行/下行空间(%) ``` **敏感性分析表**（必须）： 1. WACC vs 永续增长率 → 每股价值 2. 收入增速 vs EBIT 利润率 → 每股价值 3. 可选：Beta vs 无风险利率 → WACC ## 输出 ### Excel 输出（formal 模式） - Sheet 1 "DCF": 历史数据 → 预测数据 → FCFF → 折现 → 估值汇总 → 敏感性分析表 - Sheet 2 "WACC": 资本成本计算详细过程 - 格式规则： - 蓝色字体 = 输入假设 - 黑色字体 = 公式计算 - 所有派生值必须是 Excel 公式 - 敏感性分析表每个单元格是完整公式（非线性插值） - 基准情景（中心格）用蓝色底色 + 粗体标注 ### Markdown 输出（brief 模式） - 直接输出 Markdown 表格 - 包含：关键假设 → FCFF 预测表 → 估值结果 → 简化敏感性分析 - 在结果下方给出个人判断 ## 风格说明 | 维度 | formal（机构 DCF） | brief（个人估值笔记） | |------|-------------------|---------------------| | 输出格式 | Excel (.xlsx) | Markdown | | 预测年数 | 5-7 年 | 3-5 年 | | WACC | 完整推导过程 | 直接给出假设值 | | 敏感性 | 3 张 5×5 表 | 1 张简化 3×3 表 | | 情景 | Base + 可选 Bull/Bear | 仅 Base | | 数据源标注 | 每个输入附来源 | 关键假设说明即可 | | 免责声明 | 需要 | 不需要 | ## 关键规则 1. **公式优先于硬编码**：Excel 中所有计算值必须是公式，不硬编码数字 2. **假设透明**：每个关键假设（增长率、利润率、WACC 参数）都必须明确标注 3. **合理性检验**： - 每股价值 vs 当前股价差异 > 50% 时必须解释原因 - 永续增长率不应超过长期 GDP 增速 - 终值占比不应超过总价值的 75%（否则预测期不够长） 4. **A 股特殊考量**： - ERP 取 5%-7%（新兴市场溢价） - 注意限售股对总股本的影响 - 部分行业有政府补贴，需判断是否持续 - 关联交易可能扭曲利润，需调整 5. **不做精确预测**：DCF 的价值在于理解价值驱动因素和估值区间，而非得出一个"精确"的目标价 6. **与其他 skill 联动**：建议用 a-share-comps 的估值中位数交叉验证 DCF 结果 ## 使用示例 ### 示例 1: 基本使用 ```python # 调用 skill result = run_skill({ "param1": "value1", "param2": "value2" }) ``` ### 示例 2: 命令行使用 ```bash python scripts/run_skill.py --input data.json ``` --- Source attribution: aifinlab Original project: https://github.com/aifinlab/FinClaw/blob/9e62862d1412157fa03ffd1f0b74969dcf0ed66c/skills/a-share-dcf/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Dave AI. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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