Comparable Company Analysis
Les banquiers d'investissement, analystes de private equity et professionnels de la recherche actions utilisent ce prompt pour structurer rapidement un groupe de comparables et un cadre de valorisation lors d'une initiation de couverture, d'une opération M&A ou d'une levée de fonds.
Prompts
You are a senior equity research analyst and valuation specialist. I need to build a comparable company analysis (comps) framework for [COMPANY NAME], which operates in the [INDUSTRY] sector. The company is based in [COUNTRY/REGION] and reported [CURRENCY] [LATEST REVENUE] in revenue and [CURRENCY] [LATEST EBITDA] in EBITDA for its most recent fiscal year. Please structure the comparable company analysis as follows: 1. **Peer Group Selection Criteria** Define the criteria for selecting relevant comparable companies, covering: industry classification (primary and sub-sector), geographic focus, revenue size range (typically 0.5x–2x the subject company), business model similarity, and growth profile. List 8–12 hypothetical or named peer companies that would fit these criteria in [INDUSTRY]. 2. **Key Trading Multiples to Collect** For each comparable, specify which multiples to gather and why each is relevant for [INDUSTRY]: - EV/EBITDA (LTM and NTM) - EV/Revenue (LTM and NTM) - P/E (LTM and NTM) - EV/EBIT where depreciation-heavy models make EBITDA less comparable - Any sector-specific multiples relevant to [INDUSTRY] (e.g., EV/Subscribers, Price/Book) 3. **Normalization Adjustments** Explain how to normalize for the following differences across the peer set: - Differences in capital structure (adjust to enterprise value basis) - Non-recurring items in EBITDA - Different fiscal year-end dates - Geographic revenue mix creating growth rate differences - Accounting policy differences (e.g., lease treatment under IFRS 16 vs. US GAAP) 4. **Deriving the Implied Valuation Range** Walk through the methodology for translating peer multiples into an implied valuation range for [COMPANY NAME]: - Apply 25th percentile, median, and 75th percentile multiples to [COMPANY NAME]'s metrics - Derive implied enterprise value range - Subtract net debt to arrive at implied equity value range - Present a football field chart description showing the range across all multiples used 5. **Premium / Discount Assessment** Identify factors that would justify [COMPANY NAME] trading at a premium or discount to the peer median — including growth rate differential, margin profile, market share trajectory, management quality, and liquidity. Present the output as a structured framework with tables and clear section headers suitable for an investment committee presentation.
Variables du Prompt
Remplacez chaque placeholder par vos informations spécifiques :
[COMPANY NAME][INDUSTRY][COUNTRY/REGION][CURRENCY][LATEST REVENUE][LATEST EBITDA]Ce que vous obtiendrez
Un cadre de sélection des comparables avec critères et 8 à 12 sociétés illustratives, une liste de multiples pertinents avec ajouts sectoriels, une méthodologie de normalisation, une dérivation étape par étape de la valorisation implicite produisant une plage de résultats, et une évaluation qualitative prime/décote.
💡 Conseil d'Expert
Précisez si [COMPANY NAME] est cotée en bourse ou privée — l'IA ajustera la sélection des multiples et la discussion sur la décote d'illiquidité en conséquence.
Outils IA Compatibles
Claude
Best for building the full framework and normalization methodology narrative. Provide company financials and sector context. Claude will structure the analysis in investment-committee-ready format.
ChatGPT
Effective for generating peer lists and multiple tables. Use GPT-4 with browsing enabled or Code Interpreter with uploaded peer data to compute implied valuation ranges automatically.
Gemini
Useful for quickly pulling recent peer multiple data via search integration. Combine with a structured prompt to move from data retrieval to formatted comps output in one session.
Perplexity
Excellent for researching current peer trading multiples with live web sources cited. Use Perplexity to gather the data, then move to Claude or ChatGPT to structure the full analysis.