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Skillsearnings-forecast

earnings-forecast

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Updated 10-15 22:13

About this skill

Earnings Forecasts and Consensus Expectations

Overview

Build trading signals around discrepancies between corporate earnings forecasts and market consensus expectations. Core logic: short-term stock prices are driven by earnings expectation gaps, and capturing "expectation gaps" is more valuable than forecasting absolute earnings. Two main lines: ① Compare independent forecasts with consensus expectations to find discrepancies; ② Track analyst forecast revision momentum.

Core Concepts

1. Top-Down Forecasting Method (Top-Down)

Forecasting chain:

GDP增速预测 → 行业增加值增速 → 行业收入增速 → 龙头公司收入增速 → 利润率假设 → EPS预测

A-share practical example (using the baijiu industry as an example):

Level Indicator Forecast logic
Macro GDP +5.0% Consumption accounts for 65% of GDP, consumption growth is about +6%
Industry Baijiu revenue +8% High-end baijiu sees both volume and price rise, structural upgrading
Company Kweichow Moutai (600519.SH) Ex-factory price +10%, sales volume +2%, revenue about +12%
Earnings Net profit margin 55% Price increase transmission, expense ratio stable
EPS About 62 yuan Net profit/total share capital

Applicable scenarios: Industry beta judgment, market-wide earnings cycle positioning, macro strategy alignment

2. Bottom-Up Forecasting Method (Bottom-Up)

Three approaches to revenue breakdown:

# 方法1：量价拆解
revenue = volume * price
# 例：中国神华(601088.SH) = 煤炭销量(亿吨) × 煤价(元/吨) + 电力收入

# 方法2：客户/产品拆解
revenue = sum(segment_revenue for segment in business_lines)
# 例：美的集团(000333.SZ) = 暖通空调 + 消费电器 + 机器人及自动化

# 方法3：门店/用户拆解
revenue = stores * revenue_per_store  # 或 users * ARPU
# 例：海底捞(6862.HK) = 门店数 × 翻台率 × 客单价 × 营业天数

Key points for profit margin assumptions:

  • Gross margin: changes in raw material cost share, product structure upgrading
  • Expense ratio: economies of scale (revenue increases, expense ratio decreases), changes in R&D investment
  • Tax rate: high-tech enterprise 15% vs ordinary 25%, whether tax incentives are expiring

3. Standardized Unexpected Earnings (SUE)

Formula:

SUE = (actual_EPS - consensus_EPS) / std(actual_EPS - consensus_EPS)
# consensus_EPS = 分析师一致预期EPS（取中位数）
# std = 过去8个季度预测偏差的标准差

Signal thresholds (A-share empirical reference):

SUE range Meaning Trading action
SUE > +2.0 Large positive surprise Strong buy signal
SUE +1.0~+2.0 Moderate positive surprise Buy signal
SUE -1.0~+1.0 In line with expectations No signal
SUE -2.0~-1.0 Moderate negative surprise Sell signal
SUE < -2.0 Large negative surprise Strong sell signal

4. Post-Earnings Announcement Drift (PEAD)

Phenomenon: After earnings announcements, stock price drift in the direction of the surprise can last 30-60 trading days.

A-share PEAD strategy implementation:

# 策略逻辑
# 1. 业绩公告日（年报4/30前，中报8/31前，季报各截止日）
# 2. 计算SUE
# 3. SUE > +1.5 的股票买入持有 40 个交易日
# 4. SUE < -1.5 的股票卖出/做空（如果可以）

# 关键参数
holding_period = 40      # 持有交易日数
sue_threshold = 1.5      # SUE阈值
max_positions = 10       # 最大持仓数
rebalance_on = "earnings_date"  # 在业绩公告日调仓

A-share PEAD considerations:

  • A-share short selling is restricted (securities lending), PEAD strategies usually only go long
  • Earnings forecasts (end of January/mid-July) come earlier than formal reports, allowing front-running
  • Annual reports are due by 4/30 and concentrated in April; during information-crowded periods, diversification is needed

5. Analyst Forecast Revision Momentum

Three key indicators:

# 1. 预期修正比率（ERM）
ERM = (上调家数 - 下调家数) / 总覆盖家数
# ERM > 0.3 = 正面动量, ERM < -0.3 = 负面动量

# 2. 预期变化幅度
eps_change_pct = (new_consensus - old_consensus_30d_ago) / abs(old_consensus_30d_ago)
# 变化 > +5% = 显著上调

# 3. 预期离散度
dispersion = std(all_analyst_EPS) / mean(all_analyst_EPS)
# 离散度 > 0.3 = 分歧大, 不确定性高
# 离散度 < 0.1 = 共识强, 确定性高

Forecast revision momentum strategy:

  • Buy: ERM > +0.3 and eps_change_pct > +5% and dispersion < 0.25
  • Sell: ERM < -0.3 and eps_change_pct < -5%
  • Signal validity period: about 60-90 trading days (forecast revision momentum decays)

Analysis Framework

Four-Step Earnings Analysis Method

  1. Build Forecast: Choose Top-Down or Bottom-Up approach, output EPS forecast value
  2. Obtain Consensus Estimates: Obtain analyst consensus EPS from Wind/East Money/THS
  3. Calculate Deviation: SUE or simple percentage deviation, determine direction of beat/miss
  4. Signal Generation: Generate trading signals based on SUE threshold, manage positions in combination with PEAD holding period

Earnings Calendar (Key Time Nodes for A-shares)

Time Event Strategy Action
Mid-January Peak disclosure of annual report earnings forecasts Capture expectation gaps early
March-April Official annual report release Confirm SUE, build PEAD positions
April 30 Annual report deadline Non-disclosure = bearish signal
Mid-July Interim report earnings forecasts Semi-annual expectation revision
August 31 Interim report deadline Same as above
October 31 Q3 report deadline Q3 data validates full-year expectations

Expectation Gap Trading Portfolio Construction

# 组合构建参数
config = {
    "universe": "沪深300成分股",          # 流动性保障
    "signal": "SUE > +1.5 或 ERM > +0.3", # 超预期信号
    "max_positions": 20,                  # 最大持仓
    "position_weight": "equal",           # 等权
    "holding_period": 40,                 # 交易日
    "rebalance": "earnings_calendar",     # 按财报日历调仓
    "stop_loss": -0.08,                   # 8%止损
}

Output Format

## 盈利预测分析 — [标的代码] [公司名称]

### 盈利预测
- 预测方法：[Top-Down / Bottom-Up]
- 预测EPS：[X元]
- 预测依据：[收入增速X%，利润率X%，关键假设]

### 一致预期对比
- 一致预期EPS：[X元]（来源：[Wind/东财]，覆盖[N]家）
- 预期偏差：[+X% / -X%]
- SUE：[+X.X]
- 预期离散度：[X.X]（[高分歧/低分歧]）

### 分析师动量
- ERM（预期修正比率）：[+X.X]（过去30日[N]家上调/[M]家下调）
- 预期变化幅度：[+X%]

### 信号判断
- SUE信号：[强买入/买入/无/卖出/强卖出]
- 动量信号：[正面/中性/负面]
- PEAD建仓窗口：[是/否]（距财报发布[X]日）

### 风险提示
- [具体风险：如一次性收益、会计政策变更、商誉减值等]

Notes

  • Consensus estimate data requires paid terminals such as Wind/Choice; free data sources (East Money web version) may not be timely enough
  • SUE calculation requires at least 8 quarters of historical forecast deviation data to estimate the standard deviation
  • Earnings forecasts and earnings flash reports are earlier signal sources than official financial reports, but with lower accuracy
  • Information is crowded during A-share earnings season (April/August), and PEAD signals may interfere with each other
  • One-off gains and losses (asset disposal/government subsidies/investment income) can distort EPS; non-recurring gains and losses should be excluded by using adjusted EPS
  • Expectation revision momentum has a self-fulfilling tendency (analyst herd effect); inflection point identification is more valuable than trend following
  • Small-cap stocks have little analyst coverage (< 3 firms), and the statistical significance of consensus estimates is weak; prioritize constituents of CSI 300/CSI 500
  • This framework is for research backtesting only and does not constitute investment advice