analyzing-comparable-transactions
Structures precedent transaction analysis with deal multiples, premium calculation, and transaction characteristic comparison. Use when analyzing M&A precedents, calculating transaction multiples, or benchmarking deal terms.
Best use case
analyzing-comparable-transactions is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structures precedent transaction analysis with deal multiples, premium calculation, and transaction characteristic comparison. Use when analyzing M&A precedents, calculating transaction multiples, or benchmarking deal terms.
Teams using analyzing-comparable-transactions should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/analyzing-comparable-transactions/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How analyzing-comparable-transactions Compares
| Feature / Agent | analyzing-comparable-transactions | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structures precedent transaction analysis with deal multiples, premium calculation, and transaction characteristic comparison. Use when analyzing M&A precedents, calculating transaction multiples, or benchmarking deal terms.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Analyzing Comparable Transactions ## When To Use - Building a precedent transactions analysis for a sell-side or buy-side pitch book - Benchmarking a proposed offer price or premium against historical M&A deals - Supporting a fairness opinion with transaction-based valuation evidence - Evaluating deal structure trends (cash vs. stock, earnouts, termination fees) across a sector - Providing a board or special committee with market context on comparable deal terms ## Inputs To Gather - **Target description**: Industry, sub-sector, size (revenue, EBITDA, assets), geographic focus, and business model - **Transaction universe parameters**: Date range (typically 5–10 years), sector/SIC/NAICS codes, minimum deal size threshold, geographic scope - **Deal data per transaction**: Announcement and close dates, acquirer identity, transaction type (merger, asset purchase, tender offer, take-private), total enterprise value (TEV), equity value, consideration mix (cash/stock/mixed), premium to unaffected price (1-day, 30-day VWAP) - **Financial metrics for targets**: LTM and NTM revenue, EBITDA, EBIT, net income, total assets, book value — sourced at announcement date - **Deal-specific qualitative factors**: Strategic vs. financial buyer, competitive auction vs. negotiated sale, hostile vs. friendly, regulatory hurdles, break-up fee as % of TEV - **Source priority**: SEC filings (merger proxies, 14D-9, S-4), Capital IQ, Bloomberg, Refinitiv, PitchBook; note if any metric is derived rather than disclosed [VERIFY] ## Workflow 1. **Define the screening criteria** — Set sector, size, date range, deal type, and geography filters. Start broad, then narrow. Document every filter applied and the rationale for inclusion/exclusion thresholds. 2. **Build the transaction universe** — Pull all deals matching the screen. Record acquirer, target, announcement date, close date, TEV, equity value, and consideration type. Exclude withdrawn/terminated deals unless specifically relevant to the analysis narrative. 3. **Normalize financial metrics** — For each target, collect LTM and NTM revenue, EBITDA, and EBIT as of the announcement date (not close date). Adjust for non-recurring items, stock-based compensation, or restructuring charges only where disclosed and material. Flag any calendarization assumptions [VERIFY]. 4. **Calculate deal multiples** — Compute TEV/Revenue, TEV/EBITDA, TEV/EBIT, and P/E for each transaction. If EBITDA is negative or unavailable, exclude that deal from EBITDA-based metrics rather than imputing. Present mean, median, 25th and 75th percentiles for each multiple. 5. **Calculate premiums** — Compute premium to unaffected share price at 1-day, 1-week, and 30-day prior to announcement (or first leak date if pre-announcement run-up occurred). Use VWAP where possible. Note whether price was affected by rumors or sector moves [VERIFY]. 6. **Segment and annotate** — Group transactions by meaningful sub-categories: strategic vs. financial buyer, deal size tier, pre- vs. post-regulatory-change periods, auction vs. negotiated. Identify outliers and provide deal-specific context (e.g., distressed sale, competitive bidding, synergy-driven premium). 7. **Derive valuation range** — Apply selected multiples (typically median and interquartile range) to the subject company's financials to produce an implied valuation range. Cross-reference premium analysis against the subject's current trading price. 8. **Contextualize and caveat** — Note market conditions at the time of each precedent (credit environment, sector cycle, index levels). Acknowledge survivorship bias, data gaps, and any transactions where reported multiples may reflect non-public adjustments. ## Output - **Transaction summary table**: One row per deal — target, acquirer, date, TEV, equity value, consideration type, TEV/Revenue, TEV/EBITDA, TEV/EBIT, premium (1-day, 30-day) - **Statistical summary**: Mean, median, 25th/75th percentiles for each multiple and premium metric, with and without identified outliers - **Segmented analysis**: Sub-tables or charts breaking out multiples by buyer type, deal size, time period, or other relevant dimension - **Implied valuation range**: Table mapping selected multiples to subject company metrics, showing low/mid/high implied TEV and equity value - **Narrative commentary**: 1–2 paragraphs summarizing key takeaways — where the subject deal sits relative to precedents, what drives the range, and which comparables are most analogous ## Quality Checks - Every transaction multiple ties back to disclosed or clearly sourced financials — no orphaned numbers - TEV calculation is consistent across all deals (same treatment of minority interest, preferred, debt, cash) [VERIFY] - Premium calculations use the correct unaffected date; verify no pre-announcement price distortion - Mean vs. median divergence is explained if greater than 15–20% - At least 5–8 transactions in the universe; if fewer, disclose the thin dataset limitation prominently - NTM estimates use consensus as of the announcement date, not current consensus [VERIFY] - All excluded transactions are listed with exclusion rationale - Output distinguishes between LTM and NTM multiples — never mix without labeling - Implied valuation range is presented on both a TEV and per-share basis where applicable
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