Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format. **Perfect for:** - Public company valuation (M&A, investment analysis) - Benchmarking performance vs. industry peers - Pricing IPOs or funding rounds - Identifying valuation outliers (over/under-valued) - Supporting investment committee presentations - Creating sector overview reports **Not ideal for:** - Private companies without comparable public peers - Highly diversified conglomerates - Distressed/bankrupt companies - Pre-revenue startups - Companies with unique business models Use when users need to build comparable company analyses, peer sets, operating metric spreads, trading multiples, and valuation summaries in market research workflows.
Instalación
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Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format. **Perfect for:** - Public company valuation (M&A, investment analysis) - Benchmarking performance vs. industry peers - Pricing IPOs or funding rounds - Identifying valuation outliers (over/under-valued) - Supporting investment committee presentations - Creating sector overview reports **Not ideal for:** - Private companies without comparable public peers - Highly diversified conglomerates - Distressed/bankrupt companies - Pre-revenue startups - Companies with unique business models Use when users need to build comparable company analyses, peer sets, operating metric spreads, trading multiples, and valuation summaries in market research workflows.
Comparable Company Analysis
⚠️ CRITICAL: Data Source Priority (READ FIRST)
ALWAYS follow this data source hierarchy:
FIRST: Check for MCP data sources - If S&P Kensho MCP, FactSet MCP, or Daloopa MCP are available, use them exclusively for financial and trading information
DO NOT use web search if the above MCP data sources are available
ONLY if MCPs are unavailable: Then use Bloomberg Terminal, SEC EDGAR filings, or other institutional sources
NEVER use web search as a primary data source - it lacks the accuracy, audit trails, and reliability required for institutional-grade analysis
Why this matters: MCP sources provide verified, institutional-grade data with proper citations. Web search results can be outdated, inaccurate, or unreliable for financial analysis.
Overview
This skill teaches Codex to build institutional-grade comparable company analyses that combine operating metrics, valuation multiples, and statistical benchmarking. The output is a structured Excel/spreadsheet that enables informed investment decisions through peer comparison.
Reference Material & Contextualization:
An example comparable company analysis is provided in examples/comps_example.xlsx. When using this or other example files in this skill directory, use them intelligently:
Industry context: Big tech mega-caps need different metrics than emerging SaaS startups
Sector-specific needs: Add relevant metrics early (e.g., cloud ARR, enterprise customers, developer ecosystem for tech)
Company familiarity: Well-known companies may need less background, more focus on delta analysis
Decision type: M&A requires different emphasis than ongoing portfolio monitoring
Core principle: Use template principles (clear structure, statistical rigor, transparent formulas) but vary execution based on context. The goal is institutional-quality analysis, not institutional-looking templates.
User-provided examples and explicit preferences always take precedence over defaults.
Core Philosophy
"Build the right structure first, then let the data tell the story."
Start with headers that force strategic thinking about what matters, input clean data, build transparent formulas, and let statistics emerge automatically. A good comp should be immediately readable by someone who didn't build it.
⚠️ CRITICAL: Formulas Over Hardcodes + Step-by-Step Verification
Environment — Office JS vs Python:
If running inside Excel (Office Add-in / Office JS): Use Office JS directly (Excel.run(async (context) => {...})). Write formulas via range.formulas = [["=E7/C7"]], not range.values. No separate recalc step — Excel handles it natively. Use range.format.* for colors/fonts.
If generating a standalone .xlsx file: Use Python/openpyxl. Write cell.value = "=E7/C7" (formula string).
Same principles either way — just translate the API calls.
Office JS merged cell pitfall: Do NOT call .merge() then set .values on the merged range (throws InvalidArgument — range still reports its pre-merge dimensions). Instead write the value to the top-left cell alone, then merge + format the full range:
Every derived value (margin, multiple, statistic) MUST be an Excel formula referencing input cells — never a pre-computed number pasted in
When using Python/openpyxl to build the sheet: write cell.value = "=E7/C7" (formula string), NOT cell.value = 0.687 (computed result)
The only hardcoded values should be raw input data (revenue, EBITDA, share price, etc.) — and every one of those gets a cell comment with its source
Why: the model must update automatically when an input changes. A hardcoded margin is a silent bug waiting to happen.
Verify step-by-step with the user:
After setting up the structure → show the user the header layout before filling data
After entering raw inputs → show the user the input block and confirm sources/periods before building formulas
After building operating metrics formulas → show the calculated margins and sanity-check with the user before moving to valuation
After building valuation multiples → show the multiples and confirm they look reasonable before adding statistics
Do NOT build the entire sheet end-to-end and then present it — catch errors early by confirming each section
Section 1: Document Structure & Setup
Header Block (Rows 1-3)
Row 1: [ANALYSIS TITLE] - COMPARABLE COMPANY ANALYSIS
Row 2: [List of Companies with Tickers] • [Company 1 (TICK1)] • [Company 2 (TICK2)] • [Company 3 (TICK3)]
Row 3: As of [Period] | All figures in [USD Millions/Billions] except per-share amounts and ratios
Why this matters: Establishes context immediately. Anyone opening this file knows what they're looking at, when it was created, and how to interpret the numbers.
Visual Convention Standards (OPTIONAL - User preferences and uploaded templates always override)
IMPORTANT: These are suggested defaults only. Always prioritize:
User's explicit formatting preferences
Formatting from any uploaded template files
Company/team style guides
These defaults (only if no other guidance provided)
Suggested Font & Typography:
Font family: Times New Roman (professional, readable, industry standard)
Font size: 11pt for data cells, 12pt for headers
Bold text: Section headers, company names, statistic labels
Default Color & Shading — Professional Blue/Grey Palette (minimal is better):
Keep it restrained — only blues and greys. Do NOT introduce greens, oranges, reds, or multiple accent colors. A clean comps sheet uses 3-4 colors total.
Columns that DON'T need statistics (size metrics):
Revenue, EBITDA, Net Income (absolute size varies by company scale)
Market Cap, Enterprise Value (not comparable across different-sized companies)
Note: Add one blank row between company data and statistics rows for visual separation. Do NOT add a "SECTOR STATISTICS" or "VALUATION STATISTICS" header row.
Why quartiles matter: They show distribution, not just average. A 75th percentile multiple tells you what "premium" companies trade at.
Debt/Equity - Leverage (for capital structure analysis)
Key Principle: Include 3-5 core multiples that matter for your industry. Don't include every possible metric just because you can.
Formula Examples
// Core multiples - always include these
EV/Revenue: =[Enterprise Value]/[LTM Revenue]
EV/EBITDA: =[Enterprise Value]/[LTM EBITDA]
P/E Ratio: =[Market Cap]/[Net Income]
// Optional multiples - include if data available
FCF Yield: =[LTM FCF]/[Market Cap]
PEG Ratio: =[P/E]/[Growth Rate %]
Cross-Reference Rule
CRITICAL: Valuation multiples MUST reference the operating metrics section. Never input the same raw data twice. If revenue is in C7, then EV/Revenue formula should reference C7.
Statistics Block
Same structure as operating section: Max, 75th, Median, 25th, Min for every metric. Add one blank row for visual separation between company data and statistics. Do NOT add a "VALUATION STATISTICS" header row.
Section 4: Notes & Methodology Documentation
Required Components
Data Sources & Quality:
Where did the data come from? (S&P Kensho MCP, FactSet MCP, Daloopa MCP, Bloomberg, SEC filings)
What period does it cover? (Q4 2024, audited figures)
How was it verified? (Cross-checked against 10-K/10-Q)
Note: Prioritize MCP data sources (S&P Kensho, FactSet, Daloopa) if available for better accuracy and traceability
Key Definitions:
EBITDA calculation method (Gross Profit + D&A, or Operating Income + D&A)
Free Cash Flow formula (Operating CF - CapEx)
Special metrics explained (Rule of 40, FCF Conversion)
Time period definitions (LTM, CAGR calculation periods)
Valuation Methodology:
How was Enterprise Value calculated? (Market Cap + Net Debt)
What growth rates were used? (Historical CAGR, forward estimates)
Any adjustments made? (One-time items excluded, normalized margins)
Analysis Framework:
What's the investment thesis? (Cloud/SaaS efficiency)
What metrics matter most? (Cash generation, capital efficiency)
How should readers interpret the statistics? (Quartiles provide context)
Section 5: Choosing the Right Metrics (Decision Framework)
Start with "What question am I answering?"
"Which company is undervalued?"
→ Focus on: EV/Revenue, EV/EBITDA, P/E, Market Cap
→ Skip: Operational details, growth metrics
"Which company is most efficient?"
→ Focus on: Gross Margin, EBITDA Margin, FCF Margin, Asset Turnover
→ Skip: Size metrics, absolute dollar amounts
"Which company is growing fastest?"
→ Focus on: Revenue Growth %, EBITDA CAGR, User/Customer Growth
→ Skip: Margin metrics, leverage ratios
"Which is the best cash generator?"
→ Focus on: FCF, FCF Margin, FCF Conversion, CapEx intensity
→ Skip: EBITDA, P/E ratios
Industry-Specific Metric Selection
Software/SaaS:
Must have: Revenue Growth, Gross Margin, Rule of 40
Optional: ARR, Net Dollar Retention, CAC Payback
Skip: Asset Turnover, Inventory metrics
Manufacturing/Industrials:
Must have: EBITDA Margin, Asset Turnover, CapEx/Revenue
Optional: ROA, Inventory Turns, Backlog
Skip: Rule of 40, SaaS metrics
Financial Services:
Must have: ROE, ROA, Efficiency Ratio, P/E
Optional: Net Interest Margin, Loan Loss Reserves
Skip: Gross Margin, EBITDA (not meaningful for banks)
Retail/E-commerce:
Must have: Revenue Growth, Gross Margin, Inventory Turnover
Optional: Same-Store Sales, Customer Acquisition Cost
Skip: Heavy R&D or CapEx metrics
The "5-10 Rule"
5 operating metrics - Revenue, Growth, 2-3 margins/efficiency metrics
5 valuation metrics - Market Cap, EV, 3 multiples
= 10 total columns - Enough to tell the story, not so many you lose the thread
If you have more than 15 metrics, you're probably including noise. Edit ruthlessly.
Section 6: Best Practices & Quality Checks
Before You Start
Define the peer group - Companies must be truly comparable (similar business model, scale, geography)
Choose the right period - LTM smooths seasonality; quarterly shows trends
Standardize units upfront - Millions vs. billions decision affects everything
Map data sources - Know where each number comes from
As You Build
Input all raw data first - Complete the blue text before writing formulas
Add cell comments to ALL hard-coded inputs - Right-click cell → Insert Comment → Document source OR assumption
For sourced data, cite exactly where it came from:
Margin test: Gross margin > EBITDA margin > Net margin (always true by definition)
Multiple reasonableness:
EV/Revenue: typically 0.5-20x (varies widely by industry)
EV/EBITDA: typically 8-25x (fairly consistent across industries)
P/E: typically 10-50x (depends on growth rate)
Growth-multiple correlation: Higher growth usually means higher multiples
Size-efficiency trade-off: Larger companies often have better margins (scale benefits)
Common Mistakes to Avoid
❌ Mixing market cap and enterprise value in formulas
❌ Using different time periods for numerator and denominator (LTM vs quarterly)
❌ Hardcoding numbers into formulas instead of cell references
❌ Hard-coded inputs without cell comments citing the source OR explaining the assumption
❌ Missing hyperlinks to SEC filings or data sources when available
❌ Including too many metrics without clear purpose
❌ Including non-comparable companies (different business models)
❌ Using outdated data without disclosure
❌ Calculating averages of percentages incorrectly (should be median)
Section 6: Advanced Features
Dynamic Headers
For columns showing calculations, use clear unit labels:
Include industry-specific metrics (Rule of 40 for SaaS, etc.)
Add more statistics rows if you have >5 companies
Section 9: Industry-Specific Additions (Optional)
Only add these if they're critical to your analysis. Most comps work fine with just core metrics.
Software/SaaS:
Add if relevant: ARR, Net Dollar Retention, Rule of 40
Financial Services:
Add if relevant: ROE, Net Interest Margin, Efficiency Ratio
E-commerce:
Add if relevant: GMV, Take Rate, Active Buyers
Healthcare:
Add if relevant: R&D/Revenue, Pipeline Value, Patent Timeline
Manufacturing:
Add if relevant: Asset Turnover, Inventory Turns, Backlog
Section 10: Red Flags & Warning Signs
Data Quality Issues
🚩 Inconsistent time periods (mixing quarterly and annual)
🚩 Missing data without explanation
🚩 Significant differences between data sources (>10% variance)
Valuation Red Flags
🚩 Negative EBITDA companies being valued on EBITDA multiples (use revenue multiples instead)
🚩 P/E ratios >100x without hypergrowth story
🚩 Margins that don't make sense for the industry
Comparability Issues
🚩 Different fiscal year ends (causes timing problems)
🚩ixing pure-play and conglomerates
🚩 Materially different business models labeled as "comps"
When in doubt, exclude the company. Better to have 3 perfect comps than 6 questionable ones.
Section 11: Formulas Reference Guide
Essential Excel Formulas
// Statistical Functions
=AVERAGE(range) // Simple mean
=MEDIAN(range) // Middle value
=QUARTILE(range, 1) // 25th percentile
=QUARTILE(range, 3) // 75th percentile
=MAX(range) // Maximum value
=MIN(range) // Minimum value
=STDEV.P(range) // Standard deviation
// Financial Calculations
=B7/C7 // Simple ratio (Margin)
=SUM(B7:B9)/3 // Average of multiple companies
=IF(B7>0, C7/B7, "N/A") // Conditional calculation
=IFERROR(C7/D7, 0) // Handle divide by zero
// Cross-Sheet References
='Sheet1'!B7 // Reference another sheet
=VLOOKUP(A7, Table1, 2) // Lookup from data table
=INDEX(MATCH()) // Advanced lookup
// Formatting
=TEXT(B7, "0.0%") // Format as percentage
=TEXT(C7, "#,##0") // Thousands separator
Common Ratio Formulas
Gross Margin = Gross Profit / Revenue
EBITDA Margin = EBITDA / Revenue
FCF Margin = Free Cash Flow / Revenue
FCF Conversion = FCF / Operating Cash Flow
ROE = Net Income / Shareholders' Equity
ROA = Net Income / Total Assets
Asset Turnover = Revenue / Total Assets
Debt/Equity = Total Debt / Shareholders' Equity
Key Principles Summary
Structure drives insight - Right headers force right thinking
Less is more - 5-10 metrics that matter beat 20 that don't
Choose metrics for your question - Valuation analysis ≠ efficiency analysis
Statistics show patterns - Median/quartiles reveal more than average
Formula auditing shows no errors (#DIV/0!, #REF!, #N/A)
Continuous Improvement
After completing a comp analysis, ask:
Did the statistics reveal unexpected insights?
Were there any data gaps that limited analysis?
Did stakeholders ask for metrics you didn't include?
How long did it take vs. how long should it take?
What would make this more useful next time?
The best comp analyses evolve with each iteration. Save templates, learn from feedback, and refine the structure based on what decision-makers actually use.
