/sector-analysis

description: Analyze US market sectors and identify rotation opportunities

Sector Analysis

⚠️ Data Verification — Do This Before Any Analysis

Before running any analysis, always retrieve the latest market data for the sectors you compare:

1. **Fetch current levels** — use web search or ask the user for the current price and 3-, 6- and 12-month performance of each sector ETF (XLK, XLF, …) and the S&P 500. Never assume a level from training data. 2. **Confirm key figures** — each sector's forward P/E and earnings-revision trend, as applicable to this skill. 3. **State your data source** — fill in the `Data & Sources` header (next section) so the origin, as-of date, retrieval path, and confidence of every figure are explicit at the top of the output. 4. **Flag stale data explicitly** — if live data is unavailable, display this warning before proceeding:

> ⚠️ **Live data unavailable.** The following analysis uses training-data estimates which may be significantly out of date. Verify all prices and metrics before making any decisions.

Never silently substitute training-data estimates for current prices. When in doubt, ask the user to paste the latest quote.

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📋 Data & Sources Header — Open Every Output With It

The first thing in the output is this provenance block, filled in — never left as placeholders. It is the standard documented on the [Data & Accuracy](https://yennanliu.github.io/InvestSkill/data-and-accuracy.html) page and the first thing `result-validator` looks for:

Data & Sources
  As of:      <date the figures represent, e.g. 2026-06-30>
  Source:     <primary docs — SEC EDGAR 10-K/10-Q, company IR, FRED, exchange data …>
  Retrieval:  <pasted by user | web/tool retrieval | model memory>
  Confidence: <HIGH | MEDIUM | LOW>

• `Retrieval: model memory` must be paired with `Confidence: LOW` — memory is a placeholder until confirmed against a primary source.
• Mixed sources: list each with its own as-of date rather than blending them.
• Sources disagree on the same figure (a closing price, a deal term): prefer the primary source (SEC filing, company release, exchange data) over an aggregator, then the most recent dated one. Show both values with their sources and dates, say which you used and why, and never average them.
• Data the user pasted is reported as `pasted by user`; do not upgrade its confidence beyond what the user's own source supports.

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Analyze US market sectors and identify sector rotation opportunities based on economic cycles.

Sector Overview

Analyze the 11 S&P 500 sectors: 1. Information Technology 2. Healthcare 3. Financials 4. Consumer Discretionary 5. Communication Services 6. Industrials 7. Consumer Staples 8. Energy 9. Utilities 10. Real Estate 11. Materials

Analysis Framework

1. **Sector Performance**

• Relative performance vs. S&P 500
• Historical performance trends
• Momentum and trend strength
• Volatility analysis

2. **Economic Cycle Positioning**

• Early cycle: Financials, Technology, Industrials
• Mid cycle: Industrials, Materials, Energy
• Late cycle: Energy, Consumer Staples, Healthcare
• Recession: Utilities, Consumer Staples, Healthcare

3. **Fundamental Metrics**

• Sector valuation (P/E, P/B vs. historical)
• Earnings growth forecasts
• Profit margin trends
• Revenue growth outlook

4. **Macro Drivers**

• Interest rate sensitivity
• Commodity price exposure
• Economic growth correlation
• Currency impact
• Regulatory environment

5. **Technical Picture**

• Sector ETF chart patterns
• Relative strength analysis
• Support/resistance levels
• Volume trends

Sector Rotation Strategy

• Identify current economic cycle phase
• Determine leading and lagging sectors
• Assess rotation timing signals
• Evaluate defensive vs. cyclical positioning
• Consider factor tilts (value, growth, quality)

Top Holdings Analysis

• Identify sector leaders and laggards
• Analyze top 5-10 stocks per sector
• Evaluate sector concentration risks
• Find emerging opportunities

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Sector Valuation Comparison Table

Use these ranges as historical context benchmarks. Always verify current figures against live data sources. Ranges reflect long-run averages across full market cycles; individual readings may deviate significantly in extremes.

| Sector | Typical P/E Range | Typical P/S Range | EV/EBITDA Range | Dividend Yield Range | Historical EPS Growth (10Y CAGR) | |---|---|---|---|---|---| | Information Technology | 22–40x | 4–10x | 15–30x | 0.5–1.5% | 12–18% | | Healthcare | 16–28x | 1.5–4x | 12–20x | 1.5–2.5% | 8–12% | | Financials | 10–16x | 2–4x (Price/Book 1–2x) | 8–14x | 2.0–3.5% | 7–11% | | Consumer Discretionary | 18–35x | 0.8–2.5x | 10–20x | 0.5–1.5% | 10–15% | | Communication Services | 15–28x | 2–5x | 10–18x | 0.5–2.0% | 6–12% | | Industrials | 16–25x | 1–2.5x | 10–16x | 1.5–2.5% | 7–11% | | Consumer Staples | 18–26x | 0.8–2x | 12–17x | 2.5–4.0% | 5–8% | | Energy | 8–18x (volatile) | 0.5–1.5x | 5–12x | 3.0–5.5% | 3–8% (commodity-driven) | | Utilities | 14–22x | 1.5–3x | 10–15x | 3.0–5.0% | 3–6% | | Real Estate (REITs) | 30–60x (use P/FFO: 14–22x) | 5–12x | 15–25x | 3.5–6.0% | 4–8% | | Materials | 12–22x | 0.8–2x | 8–14x | 2.0–3.5% | 5–10% |

**Notes:**

• P/E for Energy and Financials is highly cyclical — use normalized or through-cycle P/E.
• REITs are best valued on Price/FFO (Funds From Operations) or EV/EBITDA, not standard P/E.
• Dividend yield ranges shift with interest rate regimes; compare to prevailing 10Y Treasury for context.
• P/S is most useful for early-stage growth sectors (Tech, Comm Services) where margins are expanding.

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Sector Seasonality Calendar

Historical seasonal patterns based on decades of S&P 500 sector returns. These are tendencies, not guarantees — confirm with current macro backdrop and momentum before acting.

| Month | Historically Strong Sectors | Historically Weak Sectors | Key Seasonal Driver | |---|---|