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valuation-prosperity-tracking

Data & analytics

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Updated 10-15 19:55

About this skill

Industry Valuation & Prosperity Tracking

Overview

This skill is specifically designed forsecurities strategy researchers, aiming to provide systematicindustry valuation percentile monitoringandhigh-frequency prosperity tracking. Through real-time collection and analysis of multi-dimensional data, it identifies industry cost-effectiveness, warns of valuation bubbles and prosperity turning points, and assists industry allocation and rotation decisions.

All data queries must use the gildata-aidata service。

Core Analysis Framework

1. Valuation Monitoring

  • Absolute Valuation: PE (TTM)、PB (MRQ)。
  • Relative Valuation: Historical percentile (1-year, 3-year, 5-year, 10-year), risk premium (ERP).
  • Bubble Warning:
    • 🟢 Undervalued Zone: percentile < 20% (high margin of safety)
    • 🟡 Reasonable Zone: 20% <= percentile <= 80%
    • 🔴 Overvalued/Bubble Zone: percentile > 80% (beware of drawdown risk)
    • 📉 Extreme Bubble: percentile > 95% (strongly recommend reducing positions)

2. Prosperity Tracking

  • Volume-Price Indicators:
    • Price: Commodity prices (copper, aluminum, crude oil, coal), product prices (chemicals, building materials, panels, etc.), freight rate indices (container shipping, dry bulk).
    • Sales/Operating Rate: Automobile sales, excavator working hours, blast furnace operating rate, real estate sales area, etc.
  • Earnings Expectations: Changes in analysts' consensus net profit growth (proportion of upward/downward revisions).
  • Turning Point Warning:
    • 📈 Prosperity Upturn: Core indicators rose month-on-month for 2 consecutive periods, or turned positive year-on-year from negative.
    • 📉 Prosperity Downturn: Core indicators fell month-on-month for 2 consecutive periods, or broke below the long-term moving average.
    • ⚠️ Divergence Signal: High valuation + prosperity downturn (sell signal); low valuation + prosperity upturn (buy signal).

3. Industry Value-for-Money Scoring

  • Scoring Formula: Composite score = (Prosperity score * 0.6) + (Valuation value-for-money score * 0.4)
  • Matrix Classification:
    • 🌟 Double Excellence (High Prosperity + Low Valuation): Core overweight direction
    • 💎 Low Valuation + Improving Prosperity: Potential reversal opportunity
    • ⚠️ High Valuation + Deteriorating Prosperity: Resolutely avoid
    • 📦 High Prosperity + High Valuation: Beware of earnings falling short of expectations

Data Collection Guide (gildata-aidata tool mapping)

All data must be obtained through the gildata-aidata service, with specific tool calls as follows:

Industry Valuation Analysis

finx gildata-aidata IndustryValuation --body '{"industryCode": "行业代码", "tradeDate": "交易日期"}'

Query industry valuation indicator data, including total market capitalization, circulating market capitalization, PB, PE, etc. Used to obtain PE(TTM), PB(MRQ) and historical percentiles for each industry.

Industry Sector Performance and Capital Flows

finx gildata-aidata SectorRank --body '{"sectorType": "行业板块", "tradeDate": "交易日期"}'

Retrieve the sector list and sort it by indicators such as price change and turnover rate to help assess the relative strength of industries.

finx gildata-aidata SectorFundFlowRank --body '{"sectorType": "行业板块", "tradeDate": "交易日期"}'

Sort sectors by capital flow indicators, supporting analysis of main force and retail capital. Used to assess capital allocation preferences for industries.

Macro and Industry Economic Data (Prosperity Indicators)

finx gildata-aidata MacroIndustryEDB --body '{"indicatorCode": "指标代码", "startDate": "开始日期", "endDate": "结束日期"}'

Query economic indicator data such as China macro, industry economy, and international macro.

  • Used to obtain high-frequency prosperity data for various industries, such as: automobile sales, excavator production/operating rate, real estate sales/new construction area, steel/cement output, coal inventory, etc.

Industry Analysis Views

finx gildata-aidata IndustryAnalysisViewpoints --body '{"industry": "行业名称", "limit": 5}'

Obtain analytical views on industry trends, competitive landscape, policies, etc. from research reports. Used to help assess changes in earnings expectations and analyst attitudes.

Execution Process

  1. Data Collection:
    • Use IndustryValuation to obtain the latest valuation data for all industries (PE, PB, percentile).
    • Use SectorRank to obtain recent industry price change rankings.
    • Use SectorFundFlowRank to obtain industry capital flow rankings.
    • Use MacroIndustryEDB to obtain core high-frequency prosperity indicators for each industry (price, sales volume, operating rate, etc.).
    • Use IndustryAnalysisViewpoints to obtain the latest analyst views and direction of earnings forecast adjustments for each industry.
  2. Data Cleaning and Calculation:
    • Calculate historical valuation percentiles.
    • Calculate month-over-month and year-over-year growth rates of prosperity indicators.
    • Identify outliers and inflection point signals.
  3. Early Warning and Signal Generation:
    • Generate a "Valuation Bubble Warning List" (percentile > 90%).
    • Generate a "Prosperity Deterioration Warning List" (core indicators declining continuously).
    • Generate a "Prosperity Reversal Signal List" (stabilizing and rebounding at the bottom).
  4. Report Generation:
    • Generate a valuation and prosperity tracking report in Markdown format.

Report Template (Markdown)

# 📊 行业估值与景气跟踪报告 | [日期]

> **数据来源**: gildata-aidata 专业金融数据服务
> **跟踪周期**: [周度/月度]
> **核心结论**: [简述当前市场估值性价比最高的行业及景气度变化趋势]

---

## 一、 估值全景与泡沫预警 (Valuation Overview)

### 1.1 行业估值分位分布
| 行业 | PE(TTM) | PE分位(5年) | PB(MRQ) | PB分位(5年) | 估值状态 | 环比变化 |
|------|---------|-------------|---------|-------------|----------|----------|
| 银行 | 5.2 | 12% | 0.6 | 5% | 🟢 低估 | ➖ 持平 |
| 煤炭 | 8.5 | 45% | 1.2 | 30% | 🟡 合理 | ⬇️ 下降 |
| ... | ... | ... | ... | ... | ... | ... |

### 1.2 🚨 估值预警名单
*   **🔴 泡沫预警 (分位 > 90%)**: [列出行业，如：计算机、传媒]
    *   *风险提示*: 估值处于历史极值，需警惕业绩证伪带来的杀估值风险。
*   **🟢 黄金坑 (分位 < 20%)**: [列出行业，如：医药生物、房地产]
    *   *机会提示*: 估值处于历史底部，具备较高安全边际，关注催化剂。

---

## 二、 高频景气度跟踪 (Prosperity Tracking)

### 2.1 核心行业景气指标
| 行业 | 核心指标 | 最新值 | 环比 | 同比 | 景气趋势 | 信号 |
|------|----------|--------|------|------|----------|------|
| 钢铁 | 螺纹钢价格 | 3800元 | -1.5% | -5.2% | 📉 下行 | ⚠️ 承压 |
| 汽车 | 乘用车销量 | 200万辆 | +5.0% | +12% | 📈 上行 | 🟢 改善 |
| ... | ... | ... | ... | ... | ... | ... |

### 2.2 🔄 景气拐点信号
*   **📈 景气反转 (底部回升)**: [列出行业及指标，如：半导体 (存储芯片价格企稳回升)]
*   **📉 景气恶化 (高位回落)**: [列出行业及指标，如：光伏 (硅料价格持续下跌，开工率下降)]

---

## 三、 估值-景气匹配度矩阵 (Value-Prosperity Matrix)

*   **🌟 重点推荐 (高景气 + 低估值)**:
    *   [行业 1]: [逻辑，如：家电 (出海带动高增长，估值仅 12x)]
    *   [行业 2]: [逻辑]
*   **💎 关注修复 (低估值 + 景气改善)**:
    *   [行业 1]: [逻辑，如：医药 (反腐利空出尽，创新药出海增长，估值底部)]
*   **⚠️ 建议回避 (高估值 + 景气下行)**:
    *   [行业 1]: [逻辑，如：部分微盘股题材 (无业绩支撑，资金流出)]

---

## 四、 策略建议与配置方向 (Strategy & Allocation)

*   **超配建议**: [基于估值与景气双重验证的行业]
*   **低配/回避**: [基于估值泡沫或景气恶化的行业]
*   **下周期跟踪重点**: [提示下周需要重点关注的宏观数据或行业事件，如：美联储议息、某行业排产数据发布]

---

*免责声明：本报告基于 gildata-aidata 公开数据和量化模型生成，仅供参考，不构成投资建议。*

Notes

  • Data Source Uniqueness: All industry valuations, prosperity indicators, capital flows, and analyst views must be obtained through the gildata-aidata service to ensure data authority and consistency.
  • Representativeness of High-Frequency Data: Select core indicators that have the most direct impact on industry profits (such as wholesale prices for baijiu, price spreads for chemicals, and polysilicon prices for photovoltaics).
  • Dynamic Thresholds: The threshold for valuation percentiles can be dynamically adjusted according to the overall market level (for example, when the whole market is undervalued, the 80% percentile may be the bubble line).
  • Logical Consistency: Divergence between valuation and prosperity is a key object of analysis (such as Davis double play/double kill).
  • Tool Invocation Standards: When invoking the gildata-aidata tool, industry codes must use standard codes, and date formats must comply with API requirements.