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China Lbo Model

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jwangkun
china-lbo-model

Leveraged buyout model for A-share and China-market LBO scenarios. Adapts the standard LBO-model methodology for Chinese debt markets, CAS accounting, and AkShare data. Use instead of the original lbo-model skill when analyzing leveraged buyouts involving Chinese targets or China-based sponsors.

Overview

Publisherjwangkun
Repositoryclaude-for-financial-services-cn
Skill namechina-lbo-model
Stars
834
Forks
132
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jwangkun on GitHub. Read the source before you install it.

Installation

Install the China Lbo Model AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jwangkun/claude-for-financial-services-cn/tree/main/agent-plugins/china-earnings-reviewer/skills/china-lbo-model
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jwangkun/claude-for-financial-services-cn.git /tmp/claude-for-financial-services-cn
mkdir -p .claude/skills
cp -r /tmp/claude-for-financial-services-cn/agent-plugins/china-earnings-reviewer/skills/china-lbo-model .claude/skills/china-lbo-model
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable China Lbo Model in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads China Lbo Model on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so China Lbo Model is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

china-lbo-model

Purpose

Build institutional-quality LBO models for transactions in the China market, accounting for:

  • Chinese debt market structures (bank loans, 公司债, 中期票据)
  • CAS accounting standards
  • A-share trading restrictions and considerations
  • China-specific tax treatment

Key Differences from US LBO Models

ParameterUS LBOChina LBO
Debt marketPublic bonds, institutional loansBank syndicated loans, 公司债, ABS
Typical leverage5-7x EBITDA3-5x EBITDA (bank-driven)
High-yield marketDevelopedLimited (mostly onshore/offshore 高收益债)
Covenant packageBond covenants, bank covenantsPrimarily bank-led covenants
Management rollStandardOften 管理层持股 + earn-out
Tax rate21% federal25% standard (高新技术企业 15%)
CurrencyUSDCNY (人民币)
AccountingUS GAAP / IFRSCAS (企业会计准则)

Data Sources

Primary: iFind MCP (Tier-1 付费) / AkShare MCP (Tier-2 免费备选)

python
get_financials(ticker, "income", "annual")   → 利润表 (EBIT, Net Income)
get_financials(ticker, "balance", "annual")  → 资产负债表 (Debt, Cash)
get_financials(ticker, "cashflow", "annual") → 现金流量表 (FCF, CapEx)
get_quote(ticker)                            → Current valuation
get_historical_data(ticker)                  → Trading history
get_stock_info(ticker)                       → Company profile

Secondary Sources

  • 巨潮资讯 — public filings for historicals
  • 公司公告 — debt agreements, covenant packages
  • 银行间市场交易商协会 — bond issuance data
  • Wind / 同花顺 — comparable transaction multiples

Workflow

Step 1: Company & Transaction Setup

Transaction Structure (China-specific):

  • Buyer type: 战略投资者 (strategic) vs 财务投资者 (financial sponsor)
  • ** Listed status**: A-share delisting process vs 借壳上市 (reverse merger)
  • Deal structure: 股权收购 vs 资产收购 vs 吸收合并
  • Lock-up: 限售期 considerations (typically 12-36 months)

Entry Valuation:

  • Current market cap (流通市值 or 总市值)
  • Control premium benchmark: 20-40% typical for China M&A (vs 20-30% US)
  • Take-private premium for A-share delistings: 30-50%

Step 2: Sources & Uses

Sources:

  • Senior debt (银行借款): 40-60% of total consideration
  • Subordinated debt / 夹层融资: 5-15%
  • Equity ( sponsor equity ): 30-50%
  • Revolver (循环额度): 10-20% of EBITDA
  • Seller note / 卖方分期付款: common in China

Uses:

  • Purchase equity
  • Refinance existing debt
  • Transaction fees (typically higher in China: 2-4%)
  • 印花税 (stamp tax): 0.05% on equity transfer
  • 增值税 on asset deals: 6% (一般纳税人) or 3% (小规模纳税人)

China-specific cost items:

  • 印花税 (Stamp tax): 0.05% of deal value for equity transfers
  • 契税 (Deed tax): 3-5% for asset acquisitions
  • 土地增值税 (Land appreciation tax): for real estate assets
  • 中介费用 (Advisory fees): typically 1.5-3% for IB, legal, accounting

Step 3: Operating Model

Build 5-year projections following standard LBO methodology:

Revenue assumptions:

  • Historical growth rate baseline
  • Synergy adjustments (cost savings, revenue uplift)
  • China-specific factors:
    • 产能利用率 (capacity utilization) trends
    • 新产能投放 (new capacity ramp)
    • 市场份额变化 (market share dynamics)

Margin assumptions:

  • Gross margin: track 毛利率
  • Operating margin: 营业利润率
  • Note: Chinese industrial companies often have thinner margins than Western peers

Working capital:

  • 应收账款 (AR) days — often elevated in China
  • 存货 (Inventory) days — FIFO only (no LIFO under CAS)
  • 应付账款 (AP) days
  • Net working capital as % of revenue change

CapEx:

  • 资本支出 intensity (Chinese industrials often capex-heavy)
  • Maintenance vs growth CapEx split

Step 4: Debt Schedule

China-specific debt structures:

  1. Senior secured bank loan (银行借款)

    • Typically 3-5 year tenor
    • SOFR/LPR + spread (1-3%)
    • Covenants: 资产负债率, 利息覆盖倍数, 流动比率
    • Amortization: typically 1-3% per year (bullet maturity)
  2. Subordinated debt / 夹层融资

    • Mezzanine structures gaining market share
    • 10-15% coupon typical
    • Often includes equity kicker (认股权证)
  3. High-yield bonds / 高收益债

    • Limited market, mostly offshore
    • 7-12% coupon range
    • 3-5 year maturity
  4. Revolver (循环贷款)

    • Based on 应收账款 or 存货 collateral
    • LTV: 50-70% of eligible collateral

Interest calculation:

  • Average debt balance method or daily balance
  • LIBOR/SOFR replaced with LPR for onshore RMB loans
  • Add credit spread based on company rating

Step 5: Returns Analysis

IRR calculation (same methodology as US LBO):

  • Sponsor equity contributions
  • Dividend recapitalizations
  • Exit proceeds

MOIC calculation:

  • Total cash returned / Total cash invested

Exit multiples:

  • Comparable to china-comps analysis
  • Typical EV/EBITDA exit: 6-12x for China industrials
  • IPO exit: increasingly common (A-share IPO for delisted targets)

Sensitivity tables:

  • Entry multiple vs exit multiple
  • EBITDA growth vs leverage level
  • IRR vs MOIC matrix

Step 6: Scenario Analysis

Bear case:

  • Revenue growth below plan
  • Margin compression
  • Higher borrowing costs
  • Covenant tightness

Base case:

  • Plan assumptions achieved
  • Standard amortization schedule
  • Target exit in year 4-5

Bull case:

  • Revenue synergies materialize
  • Margin expansion through 规模效应
  • Lower financing costs
  • IPO exit at premium

China-Specific Considerations

A-Share Delisting / 私有化

  • 全面要约收购 (General offer) required for >30% stake
  • Minimum offer price: typically 6 months highest trading price
  • Post-delisting: 3 years before re-listing (创业板/科创板: 2 years)
  • 回购价格 must meet regulatory minimums

Regulatory Approvals

  • 反垄断审查 (SAMR merger control)
  • 经营者集中申报 for deals above thresholds
  • 外资安全审查 (foreign investment review)
  • 行业准入 (sector-specific approvals)

Tax Considerations

  • 资本利得税: not separately levied on individuals
  • Corporate: 25% (or 15% for 高新技术企业)
  • Withholding tax on dividends to foreign investors: 10% ( treaty relief possible)
  • 转让所得税 on asset transfers: 25% corporate or 20% individual

Working Capital Norms

  • Chinese companies often have:
    • Higher AR days (less efficient collections)
    • Higher inventory days (especially manufacturing)
    • Higher AP days (supplier financing)
  • Net WC often runs 5-15% of revenue for industrials

Debt Market Access

  • Investment-grade: 3-5% borrowing cost
  • High-yield: 8-15% (offshore bonds)
  • Bank loans: LPR + 100-300 bps
  • Shadow banking restrictions affect 信托融资 availability

Excel Model Structure

Sheet Architecture

  1. Sources & Uses — deal structure
  2. Operating Model — 5-year projections
  3. Debt Schedule — all tranches with amortization
  4. Returns Summary — IRR, MOIC, equity bridge
  5. Sensitivity — entry/exit multiple sensitivity

Key Formulas

excel
// EBITDA
=Gross Profit + D&A - S&M - G&A

// Unlevered FCF
=NOPAT + D&A - CapEx - ΔNWC

// Levered FCF
=Unlevered FCF - Interest - Mandatory Amortization - Dividends

// Debt at end of year
=Beginning Debt + New Draws - Mandatory Amort - Optional Prepayment

// Interest expense
=Average Debt × Interest Rate

// IRR
=IRR(Equity contributions, dividends, exit proceeds)

// MOIC
=Exit Equity Value / Total Equity Invested

Output Checklist

Before delivering:

  • Sources & Uses balance (Total Sources = Total Uses)
  • Debt schedule amortizes correctly
  • Interest calculated on average or period-end debt (consistent)
  • All hardcoded inputs sourced and commented
  • IRR and MOIC calculations verified
  • Sensitivity tables fully populated
  • Scenario analysis included (Bear/Base/Bull)
  • China-specific taxes/fees included in Uses
  • CAS conventions documented
  • File named: [Ticker]_LBO_Model_[Date].xlsx

Reference

This skill extends the base lbo-model skill. For Excel formatting standards, formula conventions, and section checkpoint methodology, refer to:

  • lbo-model/SKILL.md — base LBO methodology
  • xlsx-author/SKILL.md — Excel file creation standards
  • audit-xls/SKILL.md — model validation and audit procedures

Data Source Mode Switch: Set env var IFIND_DATA_SOURCE_MODE to control data source preference.

  • ifind-only (strict): Use iFind only, error if unavailable
  • ifind-fallback (default): iFind preferred, fallback to AkShare
  • akshare-only: Skip iFind, use AkShare only

Frequently asked questions

What does the China Lbo Model AI skill do?

Leveraged buyout model for A-share and China-market LBO scenarios. Adapts the standard LBO-model methodology for Chinese debt markets, CAS accounting, and AkShare data. Use instead of the original lbo-model skill when analyzing leveraged buyouts involving Chinese targets or China-based sponsors.

Why use China Lbo Model on TypingMind?

Because you install it once and use it with any model. China Lbo Model is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install China Lbo Model in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jwangkun/claude-for-financial-services-cn/tree/main/agent-plugins/china-earnings-reviewer/skills/china-lbo-model. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use China Lbo Model?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with China Lbo Model?

As many as you like. As long as a model supports skills, you can use China Lbo Model with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the China Lbo Model AI skill free?

Yes. It is published on GitHub by jwangkun under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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