Equity Research Earnings Preview
Build pre-earnings analysis with estimate models, bull/bear scenarios, key metrics to watch, and trading setup notes.
Going into earnings without a structured preview means you're reacting instead of positioning — a disciplined pre-earnings framework with scenario analysis separates prepared analysts from those caught off guard.
Who it's for: equity research analysts, portfolio managers, buy-side analysts, traders, hedge fund analysts
Example
"Build an earnings preview for Nvidia Q4" → One-page preview with consensus estimates, bull/base/bear scenarios with stock price implications, top 5 catalyst checklist, and options-implied move analysis
New here? 3-minute setup guide → | Already set up? Copy the template below.
# Earnings Preview
Build pre-earnings analysis with estimate models, scenario frameworks, and key metrics to watch. Use before a company reports quarterly earnings to prepare positioning notes, set up bull/bear scenarios, and identify what will move the stock.
## Workflow
### Step 1: Gather Context
- Identify the company and reporting quarter
- Pull consensus estimates via web search (revenue, EPS, key segment metrics)
- Find the earnings date and time (pre-market vs. after-hours)
- Review the company's prior quarter earnings call for any guidance or commentary
### Step 2: Key Metrics Framework
Build a "what to watch" framework specific to the company:
**Financial Metrics:**
- Revenue vs. consensus (total and by segment)
- EPS vs. consensus
- Margins (gross, operating, net) — expanding or contracting?
- Free cash flow
- Forward guidance vs. consensus
**Operational Metrics** (sector-specific):
- Tech/SaaS: ARR, net retention, RPO, customer count
- Retail: Same-store sales, traffic, basket size
- Industrials: Backlog, book-to-bill, price vs. volume
- Financials: NIM, credit quality, loan growth, fee income
- Healthcare: Scripts, patient volumes, pipeline updates
### Step 3: Scenario Analysis
Build 3 scenarios with stock price implications:
| Scenario | Revenue | EPS | Key Driver | Stock Reaction |
|----------|---------|-----|------------|----------------|
| Bull | | | | |
| Base | | | | |
| Bear | | | | |
For each scenario:
- What would need to happen operationally
- What management commentary would signal this
- Historical context — how has the stock moved on similar prints?
### Step 4: Catalyst Checklist
Identify the 3-5 things that will determine the stock's reaction:
1. [Metric] vs. [consensus/whisper number] — why it matters
2. [Guidance item] — what the buy-side expects to hear
3. [Narrative shift] — any strategic changes, M&A, restructuring
### Step 5: Output
One-page earnings preview with:
- Company, quarter, earnings date
- Consensus estimates table
- Key metrics to watch (ranked by importance)
- Bull/base/bear scenario table
- Catalyst checklist
- Trading setup: recent stock performance, implied move from options
## Important Notes
- Consensus estimates change — always note the source and date of estimates
- "Whisper numbers" from buy-side surveys are often more relevant than published consensus
- Historical earnings reactions help calibrate expectations (search for "[company] earnings reaction history")
- Options-implied move tells you what the market expects — compare to your scenarios
What This Does
Creates structured pre-earnings analysis to prepare for quarterly reports. Builds consensus estimates tables, bull/base/bear scenario frameworks with stock price implications, and catalyst checklists ranked by importance. Helps position ahead of earnings events.
Quick Start
Step 1: Create a Project Folder
Create a folder for your earnings previews and place the downloaded template inside as CLAUDE.md.
Step 2: Download the Template
Click Download above, then move the file into your project folder as CLAUDE.md.
Step 3: Start Working
"Build an earnings preview for Amazon Q1 2025"
"What should I watch for in Tesla's earnings?"
"Create a bull/bear/base scenario for Meta's Q4"
"Set up earnings positioning notes for my tech coverage"
Sector-Specific Metrics
The template includes operational metrics frameworks for:
- Tech/SaaS: ARR, net retention, RPO, customer count
- Retail: Same-store sales, traffic, basket size
- Industrials: Backlog, book-to-bill, price vs. volume
- Financials: NIM, credit quality, loan growth
- Healthcare: Scripts, patient volumes, pipeline updates
Best Practices
- Always note the source and date of consensus estimates — they change frequently
- "Whisper numbers" from buy-side surveys are often more relevant than published consensus
- Historical earnings reaction data helps calibrate expectations for stock moves
- Options-implied move tells you what the market expects — compare to your own scenarios