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sector-allocation

Data & analytics

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Updated 10-15 19:55

About this skill

Sector Comparison & Allocation Recommendation (Sector Allocation & Comparison)

Overview

This skill assists securities strategy researchers in conductingsector rotation analysisandasset allocation decisions. Through the gildata-aidata professional financial data service, it automatically collects core sector data, applies a multi-dimensional comparison framework (prosperity, valuation, capital, policy), quantitatively evaluates the investment value of each sector, and outputs standardized sector allocation rankings and in-depth logical deduction.

Core Analysis Framework (4D Model)

1. Prosperity Dimension (Fundamentals / Momentum) - 40% weight

  • Earnings forecasts: proportion of analyst earnings forecast upgrades (number of EPS upgrades/total number).
  • Performance growth: year-on-year growth rate of revenue/net profit, quarter-on-quarter improvement.
  • High-frequency data: trends in sector high-frequency indicators (such as automobile sales, cement prices, semiconductor shipments, freight rate indices, etc.).
  • Prosperity score: those with an upward trend and above expectations receive a high score.

2. Valuation Dimension (Valuation) - 30% weight

  • Absolute valuation: absolute values of PE (TTM), PB (LF).
  • Relative valuation: percentile levels of PE/PB over the past 3 years/5 years/10 years (<20% undervalued, >80% overvalued).
  • Cost-effectiveness: PEG (PE / net profit growth rate), risk premium (ERP).
  • Valuation score: those that are undervalued and have repair elasticity receive a high score.

3. Capital Dimension (Liquidity / Flow) - 20% weight

  • Northbound capital: recent net inflow/outflow scale and trend.
  • Institutional holdings: changes in public fund holding proportions (overweight/underweight magnitude).
  • Trading heat: margin purchase proportion, turnover rate percentile, trading value proportion.
  • Capital score: those with continuous capital inflow and not overly crowded receive a high score.

4. Policy and Catalysts (Catalysts) - 10% weight

  • Policy orientation: national strategic planning, degree of industrial policy support (bonus item).
  • Event catalysts: short-term catalysts such as price increases, restructuring, technological breakthroughs, etc.

Data Collection Guidelines

All data queries must use the gildata-aidata service, and the specific tool mappings are as follows:

Valuation Data Acquisition

finx gildata-aidata IndustryValuation --body '{"industryCode": "行业代码", "date": "查询日期"}'

Obtain valuation indicators such as sector PE(TTM), PB(LF), total market value, circulating market value, dividend yield, etc.

Capital Flow Data Acquisition

finx gildata-aidata IndustryCapitalFlow --body '{"industryCode": "行业代码", "startDate": "开始日期", "endDate": "结束日期"}'

Obtain daily sector capital inflow and outflow conditions, including main force net amount, extra-large orders, large orders, etc.

Sector Market Ranking

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

Obtain the sector list and sort by indicators such as price change, turnover rate, etc.

Sector Capital Flow Ranking

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

Rank industry sectors by capital flow indicators, and analyze the capital movements of main forces and retail investors.

Industry financial analysis

finx gildata-aidata IndustryFinancialAnalysis --body '{"industryCode": "行业代码", "reportType": "最新报告期"}'

Obtain industry financial analysis indicators, covering profitability (ROE, gross margin), solvency, growth ability (revenue growth rate, net profit growth rate), etc.

Industry daily market data

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

Obtain the daily market data of the industry on a specified date, including closing price, trading volume, and price change.

Consensus forecast data

finx gildata-aidata ConsensusExpectation --body '{"stockCode": "成分股代码"}'

Obtain the profit forecast data of constituent stocks for the next three years, used to calculate changes in the industry's overall profit expectations.

Macro industry economic data

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

Query China's macroeconomic and industry economic data, such as PMI, industrial added value, retail sales, and other high-frequency indicators.

Industry research report views

finx gildata-aidata ResearchReport --body '{"industry": "行业名称", "reportType": "行业研究", "limit": 5}'

finx gildata-aidata IndustryAnalysisViewpoints --body '{"industry": "行业名称", "limit": 10}'

Obtain industry research reports and analytical views, used to judge policy direction and market consensus.

Execution process

  1. Data collection:
    • Use gildata-aidata of SectorRank to obtain the latest ranking of Shenwan Level 1 industry price changes
    • Use IndustryValuation to obtain PE, PB, and historical percentiles for each industry
    • Use SectorFundFlowRank to obtain the ranking of industry capital flows
    • Use IndustryFinancialAnalysis to obtain industry profitability and growth ability indicators
    • Use IndustryCapitalFlow to obtain recent capital flow trends
    • Use ResearchReport or IndustryAnalysisViewpoints to obtain industry research report views
  2. Multi-dimensional scoring and ranking:
    • Conduct semi-quantitative scoring for each industry according to the 4D framework (overweight, neutral, underweight)
    • Constructindustry allocation matrix: high prosperity + low valuation = golden allocation opportunity; high prosperity + high valuation = trend continuation but caution needed; low prosperity + low valuation = left-side positioning; low prosperity + high valuation = avoid
  3. Logical reasoning:
    • For industries recommended for overweight, write the core allocation logic (prosperity reversal? valuation repair? policy-driven?)
    • For industries recommended for underweight, highlight potential risks (performance below expectations? valuation bubble? capital outflow?)
  4. Report generation:
    • Generate an industry allocation report in Markdown format, and the data must indicate the source and cutoff date

Report template (Markdown)

# 📊 行业比较与配置建议 | [日期/周期]

> **数据来源**: gildata-aidata 专业金融数据服务
> **数据截止**: [具体日期]
> **核心观点**: [一句话总结当前行业配置主线，如"攻守兼备，聚焦盈利确定性高的红利资产与景气反转的半导体"]
> **市场风格预判**: [大盘/小盘，成长/价值，当前处于什么阶段]

---

## 一、 行业配置排序 (Allocation Ranking)

| 评级 | 行业名称 | 核心驱动因子 | 景气趋势 | 估值分位 | 资金动向 |
|------|----------|--------------|----------|----------|----------|
| 🟢 **超配** | [行业 A] | [如：业绩超预期 + 政策利好] | 📈 向上 | 35% (偏低) | 🟢 净流入 |
| 🟢 **超配** | [行业 B] | [如：涨价逻辑 + 底部反转] | 📈 向上 | 15% (极低) | 🟢 净流入 |
| 🟡 **标配** | [行业 C] | [如：高景气但估值已高] | 📈 向上 | 85% (偏高) | 🟡 震荡 |
| 🔴 **低配** | [行业 D] | [如：业绩下滑 + 资金流出] | 📉 向下 | 60% (中性) | 🔴 净流出 |

---

## 二、 重点行业配置逻辑 (Deep Dive)

### 1. 🟢 推荐超配：[行业 A]
* **景气逻辑**: [详细描述基本面改善，如"下游需求回暖，产品价格连续 3 周上涨，分析师一致预期上调 10%"]
* **估值逻辑**: [详细描述估值优势，如"当前 PE 仅 15 倍，处于近 5 年 20% 分位，安全边际高"]
* **资金逻辑**: [描述资金流向，如"近5日主力净流入XX亿元，北向资金持续增持"]
* **催化剂**: [近期可能发生的催化事件]

### 2. 🟢 推荐超配：[行业 B]
* ...

---

## 三、 风险提示与回避板块 (Risk & Avoid)

* 🔴 **建议回避**: [行业 D]
  * **逻辑**: [如"产能过剩导致价格战，业绩下行周期未结束，且外资持续减持"]
* ⚠️ **潜在风险**:
  * [宏观风险，如"美债收益率上行压制成长股估值"]
  * [流动性风险，如"成交额萎缩，微盘股流动性枯竭"]

---

*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-related data must be obtained through the gildata-aidata service to ensure data authority and consistency
  • Data timeliness: Ensure that the cited valuation and capital data are the latest available data (indicate the cutoff date)
  • Logical coherence: Prosperity, valuation, and capital must be logically coherent with one another. For example, high prosperity is usually accompanied by valuation improvement, and it is necessary to explain whether it is overextended
  • Style switching: Pay attention to the current dominant market style (growth vs value, large-cap vs small-cap), and reflect the style preference in the report
  • Tool invocation specifications: When calling the gildata-aidata tool, the parameters must use the correct industry codes (Shenwan industry codes) and date format