📂 WEDNESDAY – Sector Scanner: “Earnings Dispersion Heat Map”

As earnings season accelerates, dispersion increases—some sectors show consistent beats and follow-through, while others fracture under guidance pressure.

Today’s Intel Drop maps earnings dispersion by sector, helping you focus where stock picking actually pays.

Use this to avoid low-dispersion sectors where alpha is harder to generate.

💡PROMPT TEXT:

(copy & paste the below into your preferred AI model: ChatGPT, Claude, Gemini, Perplexity, Grok, Meta, etc.)

You are building an “Earnings Dispersion Heat Map” by sector as of January 21, 2026.

Goal:
Identify sectors where earnings results are producing wide dispersion (opportunity) vs tight clustering (low alpha).

Process:

1) Sector-Level Review
For each major sector:
- Range of earnings reactions (best vs worst performers)
- Frequency of guidance changes
- Valuation dispersion among constituents
- Sensitivity to macro inputs

2) Dispersion Scoring
Assign each sector:
- Earnings Dispersion Score (1–5)
- Alpha Opportunity Score (1–5)

3) Build a SECTOR HEAT MAP TABLE:
- Sector
- Dispersion Score
- Alpha Opportunity
- Typical Earnings Behavior
- Example Stocks with High Dispersion

4) Identify:
- 2–3 sectors where stock picking matters most
- 1–2 sectors where outcomes are largely macro-driven

Finish with:
- Why dispersion creates opportunity
- How to focus research efficiently during earnings season

Output in a clean table + 3–5 sentence explanation why this matters right now.

END PROMPT

→ Submit to AI model to receive actionable output.

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