Comps Analysis

Comparable Company Analysis

⚠️ CRITICAL: Data Source Priority (READ FIRST)

ALWAYS follow this data source hierarchy:

1. 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
2. DO NOT use web search if the above MCP data sources are available
3. ONLY if MCPs are unavailable: Then use Bloomberg Terminal, SEC EDGAR filings, or other institutional sources
4. 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 Web search results can be outdated, inaccurate, or unreliable for financial analysis.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 Claude 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:

DO use examples for:

• Understanding structural hierarchy (how sections flow)
• Grasping the level of rigor expected (statistical depth, documentation standards)
• Learning principles (clear headers, transparent formulas, audit trails)

DO NOT use examples for:

• Exact reproduction of format or metrics
• Copying layout without considering context
• Applying the same visual style regardless of audience

ALWAYS ask yourself first:

1. "Do you have a preferred format or should I adapt the template style?"
2. "Who is the audience?" (Investment committee, board presentation, quick reference, detailed memo)
3. "What's the key question?" (Valuation, growth analysis, competitive positioning, efficiency)
4. "What's the context?" (M&A evaluation, investment decision, sector benchmarking, performance review)

Adapt based on specifics:

• 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: ``js ws.getRange("A1").values = [["TECHNOLOGY — COMPARABLE COMPANY ANALYSIS"]]; const hdr = ws.getRange("A1:H1"); hdr.merge(); hdr.format.fill.color = "#1F4E79"; hdr.format.font.color = "#FFFFFF"; hdr.format.font.bold = true; ``

Formulas, not hardcodes:

• 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:

1. User's explicit formatting preferences
2. Formatting from any uploaded template files
3. Company/team style guides
4. 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.
• Section headers (e.g., "OPERATING STATISTICS & FINANCIAL METRICS"):
• Dark blue background (#1F4E79 or #17365D navy)
• White bold text
• Full row shading across all columns
• Column headers (e.g., "Company", "Revenue", "Margin"):
• Light blue background (#D9E1F2 or similar pale blue)
• Black bold text
• Centered alignment
• Data rows:
• White background for company data
• Black text for formulas; blue text for hardcoded inputs
• Statistics rows (Maximum, 75th Percentile, etc.):
• Light grey background (#F2F2F2)
• Black text, left-aligned labels
• That's the whole palette: dark blue + light blue + light grey + white. Nothing else unless the user's template says otherwise.

Suggested Formatting Conventions:

• Decimal precision:
• Percentages: 1 decimal (12.3%)
• Multiples: 1 decimal (13.5x)
• Dollar amounts: No decimals, thousands separator (69,632)
• Margins shown as percentages: 1 decimal (68.7%)
• Borders: No borders (clean, minimal appearance)
• Alignment: All metrics center-aligned for clean, uniform appearance