--- name: investment-industry version: 2.0.0 category: finance domain: finance author: mahipal license: Apache-2.0 subdomain: finance description: "Use when industry research and sector rotation for portfolio alpha — TAM/SAM/SOM analysis, competitive dynamics, regulatory tailwinds, and sector timing to beat the market by 5-15% annually." keywords: [industry, research, market analysis, sector rotation, TAM, competitive dynamics, top-down investing, macro, thematic] source: ai-berkshire money: true tags: [investment, industry, finance] --- # Investment Industry Research ## When Not to Use - **Simple or one-off tasks** — if the task is straightforward, direct execution is faster than structured methodology. - **Already established workflows** — follow existing team conventions rather than introducing new frameworks. - **When automation overhead exceeds benefit** — for very small scopes, the setup cost may not be justified. ## Dependencies - Python 3.8+ or Node.js 18+ - Access to relevant APIs/services for your specific use case - Basic understanding of the domain concepts ## Commands ```bash # Refer to the skill's usage section for specific commands # Adapt these to your workflow ``` ## Money-Making Overview **Sector rotation** is the single most leveragable edge for portfolio returns. Getting the sector right matters more than picking the right stock within it — sector allocation explains 70-90% of a diversified portfolio's returns. This skill transforms industry research into actionable sector rotation trades with specific entry/exit signals, position sizing, and monitoring cadence. **ROI Track Record:** Systematic sector rotation strategies outperform buy-and-hold by 5-15% annually. Top-decile industry analysts earn 25%+ annualized on concentrated sector bets. **Capital Required:** $1,000 minimum (sector ETFs or individual stocks) **Time to First Trade:** 3-4 hours for full industry analysis, 1 hour/month to reassess **Archetype:** Top-Down Macro Investor (Medium Capital, Low Time) --- ## Revenue Streams from Industry Research | Method | Effort | Return Potential | |--------|--------|-----------------| | Sector rotation (Sector ETFs) | 2 hr initial + 1 hr/month | 5-10% alpha over market | | Thematic industry positions | 3 hr research/position | 3-10x on thematic winners | | Industry research reports | 8-10 hr/week | $5K-15K/month consulting | | Sector newsletter/substack | 5 hr/week | $2K-8K/month subscriptions | --- ## Workflow: Industry Analysis → Trade ### Phase 1: The Funnel (Top-Down Sector Selection) ``` MACRO CHECK → SECTOR CHECK → INDUSTRY CHECK → POSITION CHECK ↓ ↓ ↓ ↓ Rate cycle Relative TAM/SAM/SOM Company screen Inflation sector perf Competitive Financial health GDP trend Earnings Regulatory Management quality Policy shift momentum moat/dynamics Valuation ``` ### Phase 2: Macro Regime → Sector Map | Regime | Outperform | Underperform | |--------|-----------|-------------| | Rising rates + strong economy | Financials, Energy, Industrials | Tech (growth), Real Estate | | Falling rates + strong economy | Tech, Consumer Discretionary | Utilities, Staples | | Rising rates + weak economy | Healthcare, Staples | Real Estate, Consumer Disc. | | Falling rates + weak economy (recession) | Treasuries, Gold, Staples | Cyclicals, Small-cap | | Inflation > 4% | Energy, Materials, Commodities | Long-duration bonds, Tech | | Inflation < 2% | Tech, Consumer Disc., REITs | Energy, Materials | ### Phase 3: TAM/SAM/SOM Analysis (Market Sizing) ``` TAM (Total Addressable Market): ├── Global spending in the space ├── Gartner/IDC/Statista reports ├── Revenue of all public players combined × market share assumption ├── Growth rate: 3-year CAGR SAM (Serviceable Addressable Market): ├── Portion of TAM your target can realistically reach ├── Geographic constraints (US-only, EU-only) ├── Regulatory constraints ├── Technology constraints SOM (Serviceable Obtainable Market): ├── What the target company can capture in 3-5 years ├── Current market share × realistic growth ├── Sales capacity check ├── Competitive response analysis ``` ### Phase 4: Industry Scoring Matrix | Factor | Weight | Score (1-10) | Weighted | |--------|--------|-------------|----------| | TAM growth rate (>10% CAGR = 10, <0% = 0) | 15% | — | — | | Competitive structure (monopoly/duopoly = 10) | 20% | — | — | | Regulatory tailwind (pro-industry = 10) | 10% | — | — | | Macro regime alignment | 15% | — | — | | Revenue visibility (>2yr backlog = 10) | 10% | — | — | | Pricing power trend (rising = 10) | 15% | — | — | | Consolidation potential (fragmented = low) | 15% | — | — | | **Total** | 100% | — | — | **Buy:** >70 — overweight sector 1.5-2x benchmark **Hold:** 40-70 — market weight or slight overweight **Sell/Underweight:** <40 — reduce to 0-50% of benchmark ### Phase 5: Position Sizing & Execution ```python #!/usr/bin/env python3 """Sector rotation model — allocates based on industry scores.""" import pandas as pd # industry_scores: dict of {industry_name: score_0_100} industry_scores = { "semiconductors": 88, "healthcare_equipment": 72, "renewable_energy": 55, "retail_banking": 42, "commercial_real_estate": 18, } total_score = sum(industry_scores.values()) print("=== SECTOR ROTATION ALLOCATION ===") for industry, score in sorted(industry_scores.items(), key=lambda x: x[1], reverse=True): allocation = score / total_score # Relative weight action = "OVERWEIGHT" if score >= 70 else ("MARKET WEIGHT" if score >= 40 else "UNDERWEIGHT") print(f" {industry}: Score {score}/100 → Weight {allocation:.1%} → {action}") ``` --- ## First Action in 60 Minutes ``` 1. Identify current macro regime (rising/falling rates, inflation) — 5 min 2. Check 10-year Treasury yield, Fed funds rate, CPI — 5 min 3. Map regime to sector outperformers from table above — 5 min 4. For top 2 sectors, read 1 industry report each (Morningstar/IBD) — 20 min 5. Score each industry using scoring matrix — 15 min 6. Execute: Buy sector ETF (XLF, XLI, XLK, etc.) or top 3 stocks — 10 min → Total: ~60 min for a complete sector rotation ``` --- ## Anti-Rationalization Table | Excuse | Why It's Wrong | |--------|---------------| | "I'm a stock picker, not a macro investor" | 70% of your return comes from sector. Picking stocks in the wrong sector is fighting the tape. | | "Sector timing is impossible" | Systematic rules (rate regime → sector map) beat discretionary macro timing. Follow the rules. | | "I need to read 100 industry reports" | One Gartner PDF + scoring matrix beats reading 100 reports. Depth over breadth. | | "The macro is confusing right now" | When uncertain, default to healthcare + staples (defensive). Stay until the regime is clear. | | "This doesn't work in crypto" | Sector rotation works on every asset class. Rotate between DeFi, L1, infrastructure, memecoins. | --- ## Output Format ``` INDUSTRY ANALYSIS RECORD: Date: ____ Industry: ____ Macro Regime: ____ TAM: $____ | Growth: ____% CAGR Score: ____/100 Allocation Decision: Overweight / Market Weight / Underweight Position(s): ____ Entry Date: ____ Exit Date: ____ Return vs Benchmark: ____% ``` ## Verification Checklist ``` ☐ Macro regime identified (rate direction + inflation trend) ☐ Sector-to-regime map consulted ☐ TAM/SAM/SOM calculated for selected industry ☐ Industry scoring matrix completed (7 factors) ☐ At least 1 position in overweight sector ☐ Underweight positions reduced to <50% of benchmark ☐ Monthly reassessment calendar reminder set ``` ## When to Use Use this skill when working with investment industry.