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/china-earnings-preview

Pre-earnings analysis for A-share stocks. Builds scenario frameworks (actual vs consensus, beat/miss cases), identifies key metrics to watch, and prepares positioning notes before Chinese companies report quarterly results. Use instead of the original earnings-preview skill for

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claude-for-financial-services-cn
83358 skills4 agents4 MCP
Install
$ npx -y skills add jwangkun/claude-for-financial-services-cn --skill china-earnings-preview --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/china-earnings-preview

Context preview

The summary Claude sees to decide when to auto-load this skill.

Pre-earnings analysis for A-share stocks. Builds scenario frameworks (actual vs consensus, beat/miss cases), identifies key metrics to watch, and prepares positioning notes before Chinese companies report quarterly results. Use instead of the original earnings-preview skill for

SKILL.md

china-earnings-preview.SKILL.md
name: china-earnings-preview
description: Pre-earnings analysis for A-share stocks. Builds scenario frameworks (actual vs consensus, beat/miss cases), identifies key metrics to watch, and prepares positioning notes before Chinese companies report quarterly results. Use instead of the original earnings-preview skill for A-share coverage. Triggers on "A股财报前瞻", "季报前瞻", "业绩前瞻", "earnings preview", "what to watch for [company] earnings", or "pre-earnings setup".

china-earnings-preview

Purpose

Build **A股季报/年报前瞻分析**, preparing for company earnings releases with scenario frameworks and key metrics to watch.

Data Sources

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

get_quote(ticker)                        → Current valuation, PE/PB
get_historical_data(ticker)              → Trading context, 52-wk range
get_financials(ticker, "income", "annual")  → Historical revenue/EPS trends
# News (china-news MCP — separate server)
get_stock_news(ticker="{{TICKER}}")          → Pre-earnings context
get_industry_stocks(industry="...")      → Peer trading multiples

Consensus Estimates Sources

| Source | Access | Notes | |--------|--------|-------| | Wind 一致预期 | Institutional | Most comprehensive | | Choice 一致预期 | Institutional | Alternative | | 慧博投研 | Web / API | Good coverage | | 同花顺 iFinD | Web / API | Retail-friendly UI | | 东方财富 | Web | Free, some coverage | | 巨潮 业绩预告 | Regulatory | Mandatory disclosures |

**If consensus unavailable**, derive from:

  • Historical growth rates
  • Management guidance from prior calls
  • Industry benchmarks

Secondary Sources

  • 公司公告 (earnings preview notices 业绩预告)
  • 行业研究报告 (sector reports)
  • 卖方研报 (broker research summaries)

Workflow

Step 1: Establish Baseline

**Historical performance (last 4-8 quarters):**

| Quarter | Revenue (亿) | YoY | Net Income (亿) | YoY | EPS (元) | Net Margin | |---------|-------------|-----|----------------|-----|----------|------------| | Q1 2024 | | | | | | | | Q2 2024 | | | | | | | | Q3 2024 | | | | | | | | Q4 2023 | | | | | | |

**Identify trends:**

  • Accelerating or decelerating growth?
  • Margin expansion or compression?
  • Seasonal patterns?
  • One-time items to normalize?

Step 2: Gather Consensus Estimates

**Consensus table:**

| Metric | Q1 2024 Estimate | Range (Low-High) | # Analysts | |--------|-----------------|-------------------|------------| | Revenue (亿) | | | | | YoY Growth | | | | | Net Income (亿) | | | | | EPS (元) | | | | | Gross Margin | | | | | Net Margin | | | |

**Beat probability assessment:**

  • Strong beat (>+10%): Company has history of under-promising
  • Moderate beat (+5% to +10%): Consensus well-established
  • In-line (-5% to +5%): Typical range
  • Miss risk (<-5%): Macro headwinds, order delays

Step 3: Identify Key Metrics to Watch

**Company-specific KPIs:**

For each company, identify 3-5 metrics that will drive the report:

| Metric | Why It Matters | Watch Threshold | Risk if Missed | |--------|---------------|-----------------|----------------| | e.g., 白酒批价 | Price indicator for channel health | >950元/瓶 | Demand softness | | e.g., 动力电池装机量 | Volume indicator | >XX GWh | Market share loss | | e.g., 云业务收入增速 | Growth engine health | >30% | Cloud slowdown |

**Sector-wide KPIs (for sector previews):**

| Sector | Key Metrics | |--------|-------------| | 白酒 | 批价、库存、回款、动销 | | 半导体 | 产能利用率、出货量、ASP、库存天数 | | 新能源汽车 | 交付量、单车收入、毛利率、电池成本 | | 医药 | 创新药收入、研发费用、集采影响 | | 银行 | NIM、不良率、拨备覆盖率 | | 券商 | 经纪/投行/资管收入、股基交易量 | | 光伏 | 硅料/组件价格、排产、海外出货 | | 房地产 | 销售额、拿地、融资成本 |

Step 4: Build Scenario Framework

**Three-scenario model:**

BEAR CASE (超预期悲观)
  Revenue: -X% vs consensus
  Net Income: -Y% vs consensus
  Key factor: [specific risk]
  Likely catalysts: 业绩预告大幅下调, 行业负面政策

BASE CASE (符合预期)
  Revenue: ±Z% vs consensus
  Net Income: ±W% vs consensus
  Key factor: [steady state]
  Likely outcome: 符合预期, 股价波动±5%

BULL CASE (超预期乐观)
  Revenue: +A% vs consensus
  Net Income: +B% vs consensus
  Key factor: [positive surprise driver]
  Likely catalysts: 新品放量, 成本下降超预期

Step 5: Position Analysis

**What does the market expect?**

  • Recent stock price performance into earnings
  • Implied move from options (if A-share options available)
  • Sentiment from 北向资金 trends
  • Broker recommendations distribution

**Position sizing considerations:**

  • High expectations (high PE) → asymmetric risk to downside
  • Low expectations (depressed stock) → upside potential on beat
  • Earnings as catalyst: upcoming product launch, policy change

Step 6: Pre-Earnings Positioning Note

**Standard structure:**

[公司名称]([代码])[季/年报] 前瞻:[主题/焦点]

一、业绩预期
  - 关键指标一致预期一览
  - 预测区间

二、情景分析
  - 乐观/基准/悲观情景

三、关注要点
  - 最重要的 3-5 个指标
  - 预期 vs 实际的关键差异点

四、估值与预期
  - 当前估值水平
  - 市场情绪指标
  - 北向资金动向

五、情景判断与策略
  - 不同情景下的股价反应
  - 可能的交易策略

六、风险提示
  - 关键下行风险

Step 7: Post-Earnings Follow-up

After actual results are released:

  • Compare actual vs preview scenarios
  • Update the earnings-analysis model
  • Revise forward estimates
  • Note any material guidance changes

China-Specific Pre-Earnings Considerations

Earnings Calendar (A-share)

| Report Type | Deadline | Typical Release Time | |-------------|----------|----------------------| | Q1 / Q3季报 | 1 month after quarter-end | Before market open or after close | | Semi-annual report (中报) | 2 months after H1 | Before market open | | Annual report (年报) | 4 months after year-end | Typically Jan-Apr |

**Release pattern:**

  • Most companies release before market open (8:00-9:00 AM)
  • Some release after market close (after 15:00)
  • 创业板/科创板 may have more flexible schedules

业绩预告 (Earnings Preview Notice)

  • Mandatory if actual vs prior period variance >50%
  • Published typically 2-4 weeks before formal report
  • Format: 预增 (increase), 预减 (decrease), 扭亏 (turn to profit), 首亏 (first loss), 续亏 (continued loss)
  • Provides directional guidance before formal report

Consensus Reliability

**Caveats for Chinese consensus:**

  • Fewer analysts covering A-shares
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