equity-research
anthropics/financial-services-plugins
Generate comprehensive equity research snapshots combining analyst consensus, fundamentals, and macro context.
What is equity-research?
This skill synthesizes IBES consensus estimates, company financials, historical prices, and macroeconomic data into structured research snapshots. Use it when building investment cases, comparing analyst estimates to actuals, assessing valuations, or analyzing company fundamentals for equity research.
- Pulls IBES analyst consensus estimates (EPS, Revenue, EBITDA, DPS) with analyst count and dispersion metrics
- Retrieves historical company fundamentals (income statement, balance sheet, cash flow) for ratio and trend analysis
- Fetches historical equity prices with OHLCV data, total returns, and beta calculations
- Gathers macroeconomic indicators (GDP, CPI, unemployment, PMI) to establish sector backdrop
- Synthesizes data into standardized tables: consensus estimates, financials summary, valuation metrics, and investment thesis
How to install equity-research
npx skills add https://github.com/anthropics/financial-services-plugins --skill equity-researchHow to use equity-research
- 1.Call qa_ibes_consensus to retrieve FY1 and FY2 estimates for EPS, Revenue, EBITDA, and DPS with analyst count and dispersion
- 2.Call qa_company_fundamentals to extract 3-5 years of historical financials and compute revenue growth, margins, leverage, and returns
- 3.Call qa_historical_equity_price to get 1-year price history and calculate YTD return, 1Y return, 52-week range, and beta
- 4.Call tscc_historical_pricing_summaries for 3-month daily data to assess volume trends and recent momentum
- 5.Call qa_macroeconomic to retrieve GDP, CPI, and policy rate data for the company's primary market
- 6.Synthesize outputs into structured tables (consensus, financials summary, valuation) and conclude with investment thesis including recommendation, fair value range, bull/bear cases, catalysts, and conviction level
Use cases
- Building a buy/sell/hold investment case with fair value range and conviction level
- Comparing analyst consensus estimates to actual reported results to identify surprises
- Analyzing multi-year revenue growth, margin trends, and return metrics (ROE, ROIC) for business quality assessment
- Assessing forward valuation (P/E, EV/EBITDA) relative to sector and historical averages
- Evaluating macroeconomic tailwinds or headwinds affecting a company's sector performance
- Equity research analysts
- Investment managers and portfolio managers
- Financial advisors evaluating stock opportunities
- Investors building investment theses and cases
equity-research FAQ
It uses MCP tools that provide IBES analyst consensus estimates, company fundamentals (financials), historical equity prices, pricing summaries, and macroeconomic indicators. All data comes from these integrated tools.
EPS, Revenue, EBITDA, and DPS estimates for FY1 and FY2, along with analyst count, median/mean estimates, high/low ranges, and dispersion percentages.
The thesis includes a recommendation (buy/hold/sell), fair value range, key bull case (1-2 sentences), key bear case (1-2 sentences), upcoming catalysts, and conviction level (high/medium/low).
Yes. By pulling consensus estimates and then comparing them to reported fundamentals from qa_company_fundamentals, you can identify where actual results beat or missed analyst expectations.
Forward P/E (derived from current price divided by consensus EPS), EV/EBITDA, and dividend yield, each with context relative to sector and historical averages.
Full instructions (SKILL.md)
Source of truth, from anthropics/financial-services-plugins.
name: equity-research description: Generate comprehensive equity research snapshots combining analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context. Use when researching stocks, comparing estimates to actuals, analyzing company financials, assessing equity valuations, or building investment cases.
Equity Research Analysis
You are an expert equity research analyst. Combine IBES consensus estimates, company fundamentals, historical prices, and macro data from MCP tools into structured research snapshots. Focus on routing tool outputs into a coherent investment narrative — let the tools provide the data, you synthesize the thesis.
Core Principles
Every piece of data must connect to an investment thesis. Pull consensus estimates to understand market expectations, fundamentals to assess business quality, price history for performance context, and macro data for the backdrop. The key question is always: where might consensus be wrong? Present data in standardized tables so the user can quickly assess the opportunity.
Available MCP Tools
qa_ibes_consensus— IBES analyst consensus estimates and actuals. Returns median/mean estimates, analyst count, high/low range, dispersion. Supports EPS, Revenue, EBITDA, DPS.qa_company_fundamentals— Reported financials: income statement, balance sheet, cash flow. Historical fiscal year data for ratio analysis.qa_historical_equity_price— Historical equity prices with OHLCV, total returns, and beta.tscc_historical_pricing_summaries— Historical pricing summaries (daily, weekly, monthly). Alternative/supplement for price history.qa_macroeconomic— Macro indicators (GDP, CPI, unemployment, PMI). Use to establish the economic backdrop for the company's sector.
Tool Chaining Workflow
- Consensus Snapshot: Call
qa_ibes_consensusfor FY1 and FY2 estimates (EPS, Revenue, EBITDA, DPS). Note analyst count and dispersion. - Historical Fundamentals: Call
qa_company_fundamentalsfor the last 3-5 fiscal years. Extract revenue growth, margins, leverage, returns (ROE, ROIC). - Price Performance: Call
qa_historical_equity_pricefor 1Y history. Compute YTD return, 1Y return, 52-week range position, beta. - Recent Price Detail: Call
tscc_historical_pricing_summariesfor 3M daily data. Assess volume trends and recent momentum. - Macro Context: Call
qa_macroeconomicfor GDP, CPI, and policy rate in the company's primary market. Summarize whether macro is tailwind or headwind. - Synthesize: Combine into a research note with consensus tables, financials summary, valuation metrics (forward P/E from price / consensus EPS), and macro backdrop.
Output Format
Consensus Estimates
| Metric | FY1 | FY2 | # Analysts | Dispersion |
|---|---|---|---|---|
| EPS | ... | ... | ... | ...% |
| Revenue (M) | ... | ... | ... | ...% |
| EBITDA (M) | ... | ... | ... | ...% |
Financials Summary
| Metric | FY-2 | FY-1 | FY0 (LTM) | Trend |
|---|---|---|---|---|
| Revenue (M) | ... | ... | ... | ... |
| Gross Margin | ... | ... | ... | ... |
| Operating Margin | ... | ... | ... | ... |
| ROE | ... | ... | ... | ... |
| Net Debt/EBITDA | ... | ... | ... | ... |
Valuation Summary
| Metric | Current | Context |
|---|---|---|
| Forward P/E | ... | vs sector/history |
| EV/EBITDA | ... | vs sector/history |
| Dividend Yield | ... | ... |
Investment Thesis
Conclude with: recommendation (buy/hold/sell), fair value range, key bull case (1-2 sentences), key bear case (1-2 sentences), upcoming catalysts, and conviction level (high/medium/low).
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