Equity Research Snapshot (LSEG)
Generate comprehensive equity research snapshots combining IBES analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context using LSEG data tools.
Building a proper equity research note means pulling consensus estimates, multi-year financials, price history, and macro data from separate sources — then somehow synthesizing it all into a coherent investment thesis before the opportunity moves.
Who it's for: equity research analysts, portfolio managers, buy-side analysts, investment bankers
Example
"Build a research snapshot for AAPL" → Consensus EPS/revenue estimates with dispersion, 3-year financials trend, forward P/E vs history, macro backdrop assessment, and a structured bull/bear thesis with conviction level
New here? 3-minute setup guide → | Already set up? Copy the template below.
# 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
1. **Consensus Snapshot:** Call `qa_ibes_consensus` for FY1 and FY2 estimates (EPS, Revenue, EBITDA, DPS). Note analyst count and dispersion.
2. **Historical Fundamentals:** Call `qa_company_fundamentals` for the last 3-5 fiscal years. Extract revenue growth, margins, leverage, returns (ROE, ROIC).
3. **Price Performance:** Call `qa_historical_equity_price` for 1Y history. Compute YTD return, 1Y return, 52-week range position, beta.
4. **Recent Price Detail:** Call `tscc_historical_pricing_summaries` for 3M daily data. Assess volume trends and recent momentum.
5. **Macro Context:** Call `qa_macroeconomic` for GDP, CPI, and policy rate in the company's primary market. Summarize whether macro is tailwind or headwind.
6. **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).
What This Does
Automates the equity research snapshot workflow by chaining LSEG MCP tools: pulling IBES consensus estimates (EPS, revenue, EBITDA), retrieving multi-year company fundamentals for ratio analysis, computing price performance and beta from historical data, and overlaying macroeconomic context. The output is a structured research note with standardized tables and an investment thesis.
The key question the skill helps answer: where might consensus be wrong? By presenting estimates alongside fundamentals trends, valuation metrics, and the macro backdrop, it surfaces the data you need to form a differentiated view.
Quick Start
Step 1: Create a Project Folder
mkdir -p ~/equity-research
cd ~/equity-research
Step 2: Download the Template
Click Download above, then move the file into your project folder as CLAUDE.md.
Step 3: Start Working
Launch Claude Code and try these prompts:
Build a research snapshot for MSFT including consensus estimates and 3-year fundamentals
Compare consensus estimates for AAPL vs GOOGL — where is analyst dispersion highest?
What is the macro backdrop for European bank stocks right now?
Output Sections
The research note follows a structured format:
- Consensus Estimates — FY1/FY2 EPS, revenue, EBITDA with analyst count and dispersion
- Financials Summary — 3-year revenue, margins, ROE, leverage trends
- Valuation Summary — Forward P/E, EV/EBITDA, dividend yield with sector context
- Investment Thesis — Buy/hold/sell recommendation, fair value range, bull/bear cases, catalysts, conviction level
Available MCP Tools
qa_ibes_consensus— IBES analyst consensus: median/mean estimates, analyst count, high/low range, dispersionqa_company_fundamentals— Income statement, balance sheet, cash flow for historical ratio analysisqa_historical_equity_price— OHLCV, total returns, and betaqa_macroeconomic— GDP, CPI, unemployment, PMI for economic backdrop
Tips for Best Results
- Start with the consensus snapshot to frame market expectations before diving into fundamentals
- High analyst dispersion signals uncertainty — these are often the most interesting research opportunities
- Use the macro context to assess whether sector tailwinds/headwinds are priced into consensus
- For cross-company comparisons, run the workflow for multiple tickers and compare valuation multiples side by side
- The forward P/E vs historical average is a quick sanity check on whether the market is pricing growth or contraction