China Earnings Preview

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 (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 vs US large caps
• Estimates may be stale (update frequency lower)
• Institutional vs retail analyst coverage varies significantly
• Broker research sometimes biased ( conflicted interests )
• Cross-reference multiple sources when possible

Policy Risk

• Regulatory changes can materially impact earnings overnight
• 行业政策 (industry policy) shifts common in:
• 医药 (pharmaceuticals — 集采)
• 教育 (education — 双减)
• 互联网 (internet — antitrust)
• 新能源 (renewables — subsidy changes)
• Factor policy risk into scenario analysis

Quality Checks

Before delivering preview:

• [ ] Historical data complete and accurate (AkSource verified)
• [ ] Consensus estimates sourced (or clearly noted as unavailable)
• [ ] Scenario framework covers bull/base/bear
• [ ] Key watch items identified with rationale
• [ ] China-specific risks flagged (政策, 集采, etc.)
• [ ] Valuation context included
• [ ] Pre-earnings positioning actionable

> 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: jwangkun
• Source: jwangkun/claude-for-financial-services-cn
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.