1. The Problem
Financial analysts spend 4-6 hours writing each market report. Same data. Different audiences. Endless rewrites.
You pull the quarterly data. Tech sector up 12%, healthcare down 3%, energy volatile. You write the institutional investor report—dense, technical, risk-focused. Then you rewrite it for retail clients—simpler language, more context, different emphasis. Then the internal team needs their version. Then the compliance-approved public version. Four audiences. Same numbers. Four complete rewrites. By the time you finish report #10, you've spent 50 hours saying the same thing ten different ways.
4-6 hours per report
Per analyst daily
Each market analysis report requires data extraction, trend identification, insight generation, and audience-specific formatting. That's 20-30 hours per week just repackaging the same information for different readers.
10-15 reports weekly
Per analyst
Institutional clients want daily updates. Retail clients need weekly summaries. Internal teams demand real-time briefs. Compliance needs sanitized versions. Each report is written from scratch because copy-paste doesn't work across audiences.
$75K annually
In opportunity cost
An analyst making $150K spending 60% of their time rewriting reports instead of finding alpha. That's $90K in salary paying for manual reformatting. Plus the revenue lost from insights they never had time to discover.
"I spend Tuesday writing the institutional report. Wednesday I rewrite it for retail. Thursday it's the compliance version. Friday I realize the data changed and I start over. I didn't become an analyst to be a human copy machine."
— Equity research analyst, 8 years at bulge bracket firm
2. The 3 Building-Block Prompts
Example: 01: Extract Key Metrics
Extract key financial metrics from this market data and format as JSON. Include: sector, current_price, price_change_pct, volume, 52_week_high, 52_week_low, pe_ratio, market_cap, key_trends (array), risk_factors (array), analyst_consensus. If any field is missing, use null. Be precise with numbers—no rounding unless specified. Market data: Technology Sector Update - Q2 2025 NASDAQ Composite: 18,450 (+12.3% QoQ) Volume: 4.2B shares (avg: 3.8B) 52-week range: 15,200 - 18,600 Sector P/E: 28.5x (5-year avg: 24.2x) Market cap: $14.2T Key trends: - AI infrastructure spending up 45% YoY - Cloud revenue growth slowing to 18% (was 32% last year) - Semiconductor shortage easing, prices normalizing - Cybersecurity M&A activity surging Risks: - Interest rate sensitivity (duration risk) - Regulatory scrutiny on big tech intensifying - Valuation concerns at historical highs Analyst consensus: 65% Buy, 30% Hold, 5% Sell Output valid JSON only, no commentary.
✅ Generated Output:
✅ Intro Paragraph:
{ "sector": "Technology", "current_price": 18450, "price_change_pct": 12.3, "volume": 4200000000, "volume_avg": 3800000000, "52_week_high": 18600, "52_week_low": 15200, "pe_ratio": 28.5, "pe_5yr_avg": 24.2, "market_cap": "14.2T", "key_trends": [ "AI infrastructure spending +45% YoY", "Cloud revenue growth slowing to 18%", "Semiconductor shortage easing", "Cybersecurity M&A activity surging" ], "risk_factors": [ "Interest rate sensitivity", "Regulatory scrutiny intensifying", "Valuation at historical highs" ], "analyst_consensus": { "buy": 65, "hold": 30, "sell": 5 } }
✅ Meta Description:
Why this works: Reports need structured data, not paragraphs. This prompt turns 'tech is up and AI is hot' into precise metrics you can query, compare, and reformat. The JSON structure means you can feed this into the next prompt without manual cleanup. // Try it now: Copy this prompt into ChatGPT or Claude. Paste any market summary—Bloomberg snippet, internal memo, earnings call transcript. Watch messy narrative become clean data. This is your foundation—everything builds on clean extraction.
3. Your 10-Minute Quick Win
Test the 3-Prompt System Right Now
Turn one market dataset into three audience-specific reports in 10 minutes
You don't need to build anything. You don't need to integrate APIs. Just open ChatGPT or Claude, copy the three prompts above, and watch 4 hours of work collapse into 10 minutes. Here's your test workflow:
Extract Your Data
Copy Prompt 1. Paste any market summary you have—Bloomberg terminal output, internal research note, earnings call transcript, sector update email. Run it. You'll get clean JSON with every metric structured and queryable.
Generate Insights
Copy Prompt 2. Paste the JSON from step 1. Run it. You'll get 5 specific, actionable insights with confidence levels and supporting data. Each insight is a mini-thesis backed by your numbers.
Format for Audiences
Copy Prompt 3. Paste the insights from step 2. Run it. You'll get three complete reports: institutional (technical), retail (accessible), and internal (concise). Each one is ready to send—just add your header and logo.
Old Way
1 dataset → 50 hours
- Write institutional report: 6 hours
- Rewrite for retail: 5 hours
- Rewrite for internal: 3 hours
- Repeat for 3 more datasets: 42 hours
This Method
1 dataset → 10 minutes → 3 reports
- Extract data: 2 minutes
- Generate insights: 3 minutes
- Format 3 reports: 5 minutes
- Total: 10 min per dataset (vs 12-14 hours)
By The Numbers
10x
Output Multiplier
50 hrs
Saved Weekly
60 min
Total Time for 10 Reports
Using This Manually?
Imagine this running 24/7, integrated with your data feeds, auto-generating reports the moment new market data arrives.