/comps-analysis

name: comps-analysis
description: |
  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

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 Web search results can be outdated, inaccurate, or unreliable for financial analysis.

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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.

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⚠️ 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:

  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 valua