equity-research-skill

Equity Research Skill

Author: @rollingSirius

中文文档：README.zh-CN.md ｜ Sample reports: NVDA 中文 / English ｜ GOOGL 中文 / English

Possibly the deepest AI equity-research skill.

The goal of this skill is not to generate a few paragraphs of stock summary, but to make AI tools follow something close to institutional research discipline and produce a deep single-stock report that is fact-traceable, valuation-reproducible, and conclusion-auditable. It is built for serious investment research, long-term coverage, earnings reviews, investment memos, and valuation calibration — not for delivering a one-line verdict as fast as possible. Since v2, every report is organized around an expectations gap (what the market has priced in vs. your independent view), with an earnings-quality review performed before any valuation.

Positioning

Most AI stock analysis stops at "company profile + recent news + vague valuation." This skill deliberately goes deeper:

What it does

1. Full deep-dive research

For studying a company systematically for the first time, or rebuilding an investment framework from scratch. Default output is a nine-chapter report:

1. One-page summary (verdict box + tearsheet + expectations-gap table)
2. Company and business detail
3. Competitive landscape and moat
4. Management, governance, and capital-allocation scorecard
5. Financial analysis and earnings-quality review
6. Multi-method valuation (with a football-field chart)
7. Analyst views and divergence attribution
8. News, risks, and catalysts
9. Investment verdict, counter-case, and position-sizing reference

2. Deep earnings mode

For the moment right after a company reports a quarter, a full year, guidance, or a call transcript, when the question is "what did this print actually change?" Earnings mode splits into two cases:

Earnings mode outputs nine chapters by default:

1. Verdict and snapshot
2. Surprise and its quality
3. Revenue, segments, and KPIs
4. Margins, costs, and earnings quality
5. Cash flow, balance sheet, and capital allocation
6. Guidance, the call, and management signals
7. Competition, industry, and market reaction
8. Model, valuation, and the fair-value bridge
9. Thesis update and action list

Every earnings run also executes a minimum check set: accrual ratio, cash conversion, DSO/deferred-revenue divergence, and whether Non-GAAP adjustments are recurring.

3. Valuation and calibration

This skill does not allow a target price that merely "looks reasonable." It requires at least three valuation methods cross-checked against each other, with assumptions, calculations, and the label mapping all filed:

• Reverse DCF + PVGO decomposition: what revenue growth, margin, or return on capital the current price implies, and how much of the price is paying for future growth.
• Three-scenario DCF: bull, base, and bear scenarios with a probability-weighted fair value, plus a robustness test that pushes the probabilities toward the extremes.
• EPV / three-factor method: the asset-reproduction floor · EPV · growth entry ladder; the multi-year EPV-to-adjusted-book trend as financial verification of the moat.
• EVA / residual income: incumbent ROIC vs. incremental ROIIC, with the "growth = reinvestment rate × ROIIC" consistency check.
• Relative valuation: warranted-multiple discipline — derive the multiple the company deserves from growth, returns, and risk rather than copying the peer median.
• SOTP: for multi-business or multi-asset companies, or where segments differ sharply.
• Monte Carlo (optional): the P10–P90 fair-value distribution and P(intrinsic value < current price).

The verdict label (undervalued / fairly valued / overvalued + action) maps through pre-registered calibration rules (a ±15% buffer band), overlaid with an action matrix and veto conditions; each action ships with expected value, upside/downside asymmetry, and a Kelly-lite (¼ Kelly) sizing-magnitude reference.

4. Earnings-quality review

Before valuing anything, answer whether the profit is real:

• Accrual quality (Sloan): total accruals ratio and the cash-conversion trend.
• The eight-variable Beneish M-Score (computed automatically by the checker).
• Revenue-recognition red flags: DSO divergence, deferred-revenue divergence, channel-stuffing signals.
• Expense capitalization and earnings smoothing; governance and audit signals.
• Output: an A–D earnings-credibility grade. Grade C caps the action at "wait and see," grade D is always "avoid" — "it's cheap" may never be used to offset a credibility problem.

5. Industry-specific deep appendices

The main report does not apply one template to every company. The skill first identifies where the company sits in its value chain, then loads the matching appendix on demand:

See references/industry-routing.md for exact boundaries, mixed-business selection, official data entry points, and forecast-review fields. For mixed-business companies, only the primary appendix is loaded — plus any secondary appendix needed to change the model or valuation — so that irrelevant metrics are not piled on in the name of "completeness."

6. Data sourcing and reconciliation

No mandatory use of IBKR, Morningstar, or any single data vendor. The default priority order is:

1. Regulatory filings, exchange announcements, government/regulator databases, and the company's own primary documents.
2. Exchange or regulated market data, official company materials, and official industry statistics.
3. Professional sources such as Bloomberg, FactSet, LSEG, S&P Capital IQ, Visible Alpha, Morningstar, Koyfin, and Quartr.
4. Public quote and financial aggregators, used to fill gaps and cross-check.
5. Media, second-hand accounts, and search snippets are leads only; trace back to the original wherever possible.