Find Untapped Thesis

Find Untapped Thesis — NOK-Style 未爆发 Hunting

Goal

Find stocks that meet ALL of these criteria:

1. 未涨过头: 1Y return < 50% (vs sector that's +100%+)
2. 估值便宜: Forward P/E < 25 OR PEG < 1.2
3. 真 catalyst: Concrete contracts, financials confirm thesis (not just narrative)
4. 机构未挤进: Institutional ownership <30% (room for re-rating)
5. Insider 不在大量卖: Use insider_ratio.py to filter out distribution

The classic pattern that captures multi-bagger reversal: low PE + low attention + new narrative tailwind + low institutional ownership + insider conviction.

Why this matters

Crowded names = limited upside (NVDA, AVGO, AMD already +100%+ YTD). Untapped names = asymmetric R/R because:

• Estimates can still be revised up
• Multiple expansion possible (PE 12 → 25 = +108%)
• Institutional buying creates flow tailwind
• Less covered by analysts = slower price discovery

The 6-Step Workflow

Step 1 — Define theme (user asks or you suggest)

Common themes for 2026:

• AI Data Center power (electricity, gas, nuclear) — EQT, AEP, ETR
• AI infrastructure 2nd derivative (cooling, busbar) — NVT, MOD, ENS
• Memory recovery (HBM, HDD) — MU, WDC
• Industrial gases for fabs — APD, LIN
• Copper for AI buildout — HBM, FCX, TECK
• Optical networking 2.0 — TEL, FN, CIEN
• EU defense / aerospace aluminum — CSTM
• Quantum computing (longer-term) — IONQ, RGTI, QBTS
• Robotics / industrial automation — ROK, EMR

Step 2 — Build candidate list

For each theme, generate 10-15 candidates via:

• Sector ETFs (XLE, XLU, XLB, SMH, XLK)
• WebSearch: "[theme] stocks 2026", "small cap [theme]"
• News: "[theme] suppliers", "[theme] picks-and-shovels"

Step 3 — Apply hard filters (parallel pull data)

For each candidate, pull via mcp__yfmcp__yfinance_get_ticker_info:

Hard cuts (must pass ALL):

Bonus filter — favor predictable growth models (see sector-rotation-analysis for sub-sector mechanics):

• 🟢 Prefer: Memory, Storage, Power utilities, Materials, SaaS (independent capacity / long-cycle)
• 🔴 Discount: Optical modules, OSAT, Neocloud (capacity-bottlenecked / single-customer risk)
• A 1Y-laggard in a high-predictability sub-sector beats a 1Y-laggard in a structurally constrained one

Soft preferences:

• Institutional ownership < 30%
• Analyst coverage < 15 (less crowded)
• Recent earnings beat
• Dividend yield > 1% (signal of cash gen)

Step 4 — Insider check (mandatory)

For each surviving candidate, run with --window 90 (recent activity only):

uv run --with yfinance python $(ls ~/.claude/{skills,plugins/claude-investment-skills}/review-investment-screenshot/scripts/insider_ratio.py 2>/dev/null | head -1) "TICKER1,TICKER2,..." --window 90

The script uses openinsider.com as primary source (Form 4 codes; only P = real purchase counts). For high-stakes calls, use --source both to cross-verify against yfinance.

Reject if (RECENT 0-90d):

• Buy/sell ratio < 0.1 (heavy distribution, NOT 10b5-1)
• 0 buys, 5+ ad-hoc sells from execs (verify codes are S, not F/A/M)
• CEO/CFO ad-hoc selling within 30 days

Boost if (RECENT 0-90d):

• 3+ unique insider open-market buys = "RECENT CLUSTER BUY ✅✅✅" (the MRVL/CEVA/NKE pattern)
• CEO/Founder/Chairman recent buy ($100K+, code "P")
• 0 ad-hoc sells AND any insider buy at all
• Buys clustered within 7 days

Don't punish:

• Old sells from "trusts" (THE EEC TRUST etc.) → 10b5-1 scheduled, not new info
• Tax withholding "F" code → mechanical
• RSU/DSU "A" code → compensation, not signal

Step 5 — Concrete catalyst verification

For each remaining candidate, WebSearch for:

• Last earnings: did revenue/EPS beat?
• Specific contracts: company name, $ amount, duration
• Partner announcements (NVIDIA, Microsoft, Meta, AWS, etc.)
• Capacity expansion / new fabs / facility openings
• Recent guidance raise

Reject if no concrete evidence, just narrative.

Step 6 — Rank by composite score

For each surviving candidate:

Predictability scoring:

• 10: Long-cycle infra (utilities, materials with multi-year backlog)
• 8: Independent capacity (memory, storage)
• 6: Demand-elastic with pricing power (GPU/ASIC)
• 4: Cyclical commodity
• 2: Capacity-bottlenecked downstream (optical modules, OSAT)
• 0: Single-customer concentration (some neoclouds)

Top 5 = recommend with entry plan + risk assessment.

Output format

# Untapped Thesis Screen: [Theme] — [Date]

## TL;DR
Found [N] candidates passing all filters. Top 3 recommendations:
1. [TICKER1] — [reason in 10 words]
2. [TICKER2] — [reason]
3. [TICKER3] — [reason]

## Theme Context
[Why this theme matters in 2026, what's the upside catalyst]

## Theme leaders (already up, for reference NOT to buy)
| Ticker | Price | YTD% | 1Y% | Forward P/E |

## Theme laggards (CANDIDATES)
| Ticker | Price | YTD% | 1Y% | Forward P/E | PEG | Inst% | Insider | Catalyst | Score |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | X/10 |

## Top 3 Deep Dive

### #1 [TICKER] — [tagline]
- **Why undervalued**: [concrete evidence — PE, peers, what's missed]
- **Catalyst (with date if known)**: [contracts, earnings, M&A]
- **Insider**: [ratio + key buyers]
- **Entry plan (3-tier)**:
  - Now: $X (try 30%)
  - 50DMA: $Y (add 30%)
  - 200DMA: $Z (add 40%)
- **12-month target**: $A (+B%)
- **Risk**: [main risks]
- **LEAPS suggestion**: [if applicable]

### #2 ...
### #3 ...

## Rejected (and why)
| Ticker | Why rejected |
| TICKER1 | Insider 1:50 distribution |
| TICKER2 | 1Y +180%, no longer laggard |
| TICKER3 | No concrete catalyst, just narrative |

Hard rules

1. Filter before deep-diving. Don't waste 15min analyzing a stock that's already up 200%.
2. Insider check is mandatory. No insider data = skip.
3. Concrete catalyst > narrative. "AI" is not a catalyst. "Microsoft signed $19.4B deal on 9/22/2025" is.
4. Show the rejected list. Transparency. Show 5-10 rejected so user knows you screened broadly.
5. Don't recommend market cap < $500M unless explicitly asked. Liquidity issues.
6. Cap "未爆发" claim: if 1Y > 100%, it's not 未爆发 anymore. EQT (1Y +10%) qualifies; CSTM (1Y +189%) does not.

Theme template (illustrative — verify with live data each time)

When screening a theme, organize results like this. Live data will populate specifics:

Theme Template

Always run live screening — themes evolve quarterly. Past laggards become leaders, leaders become laggards. Use mcp__yfmcp__yfinance_get_ticker_info for current data.

When to invoke

• User asks: "Find me the next NOK"
• User asks: "What undervalued [theme] stocks are there?"
• User asks: "What's 未爆发?"
• User asks: "Show me 落后 stocks in [theme]"
• Auto: weekly Monday scan (via schedule skill)

Companion skill

After this skill identifies top 3, run analyze-stock on each for the deep dive (10-step framework). This skill is the funnel, analyze-stock is the deep dive.