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Cashflow valuation

Skill serejaris/kimi-skills/skills/cashflow-valuation

Полная коллекция скиллов Kimi (267 built-in + 7 plugin skills), выгруженная из сандбокса агента

Install
npx -y skills add serejaris/kimi-skills --skill cashflow-valuation

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DCF现金流折现估值模型,支持完整的自由现金流预测、终值计算和企业价值推导,并生成增长率 × 折现率的敏感性分析矩阵。当用户需要进行企业估值、DCF分析、现金流折现计算、WACC折现、终值计算、敏感性分析、股权价值或每股价值估算,或提及关键词如DCF、估值、折现、现金流、WACC、terminal value、sensitivity analysis、企业价值、equity value、Gordon Growth Model、自由现金流时触发。

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SKILL.md

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DCF Valuation / 现金流折现估值模型

基于 DCF(Discounted Cash Flow)方法的企业估值工具,支持完整的自由现金流预测、终值计算、企业价值推导,并自动生成 增长率 × 折现率 的敏感性分析矩阵。

Quick Start

基础 DCF 估值

python scripts/dcf_model.py --fcf 1000 --growth 0.15 --discount 0.10

带敏感性分析矩阵

python scripts/dcf_model.py --fcf 1000 --growth 0.15 --discount 0.10 --sensitivity

完整股权估值 + CSV 导出

python scripts/dcf_model.py \
    --fcf 5000 --growth 0.12 --discount 0.09 \
    --terminal-growth 0.03 --years 5 \
    --debt 8000 --cash 3000 --shares 10000000 \
    --currency "万元" \
    --sensitivity \
    --growth-range 0.05,0.25,0.05 \
    --discount-range 0.06,0.14,0.02 \
    --output valuation.csv

详细用法

核心估值流程

  1. 预测期自由现金流:基于基准 FCF 和增长率,逐年预测未来 N 年的 FCF
  2. 终值计算:使用 Gordon Growth Model(永续增长模型),TV = FCF_n × (1+g) / (r-g)
  3. 折现求和:将预测期 FCF 和终值折现到当前,得到企业价值(Enterprise Value)
  4. 股权价值Equity Value = EV - Net Debt + Cash
  5. 每股价值Per Share = Equity Value / Shares Outstanding

敏感性分析矩阵

通过 --sensitivity 参数自动生成二维矩阵,展示不同增长率和折现率组合下的企业价值,帮助理解关键假设变化对估值的影响。

矩阵中用 [方括号] 标记基准情景(base case)。

JSON 输出模式

添加 --json 参数可将完整结果以 JSON 格式输出到 stdout,便于程序化处理:

python scripts/dcf_model.py --fcf 1000 --growth 0.15 --discount 0.10 --json

参数说明

参数必填默认值说明
--fcf-基准年自由现金流(正数)
--growth-年增长率,如 0.15 表示 15%
--discount-折现率 / WACC,如 0.10 表示 10%
--terminal-growth0.03永续增长率(必须 < 折现率)
--years5预测年数(1-30)
--debt0净负债(用于计算股权价值)
--cash0现金及等价物
--shares0总股本(用于计算每股价值)
--sensitivityfalse生成敏感性分析矩阵
--growth-range自动增长率范围 min,max,step,如 0.05,0.25,0.05
--discount-range自动折现率范围 min,max,step,如 0.06,0.14,0.02
--output-输出文件路径(.csv 或 .json)
--currency""金额单位标签,如 万元M USD
--jsonfalse以 JSON 格式输出到 stdout

使用场景

  • 对目标公司进行 DCF 估值,评估其合理价格区间
  • 投资决策前的估值敏感性检验(关键假设变化多大会改变结论)
  • 财务建模时快速生成估值矩阵表格
  • 输出 CSV/JSON 结果,嵌入更大的分析流程或报告中

注意事项

  • 折现率必须大于永续增长率(Gordon Growth Model 的数学要求)
  • 本工具使用纯 Python 标准库,无需安装额外依赖
  • 敏感性矩阵中标记为 N/A 的单元格表示该参数组合不满足数学约束

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