Selected Public Companies
Show what peers trade at and what that range implies for the target.
Seat
IB · ER/HF · PE
Level
Analyst
Runtime
~30 min
Output
Trading comparables page: Excel + slide
What this helps you accomplish
Each peer’s market capitalisation, enterprise value and multiples, the maximum, quartiles, mean and median across the set, and the value per share those statistics imply for the target. The choice of peers is the analysis: a set that quietly excludes the two highest-multiple names lowers every statistic on the page.
What you get
Not a description of an output. The file itself, in the conventions a banker, a PE associate or a hedge fund analyst already reads without being told.
{TICKER}_trading-comps.xlsx
Excel workbook
- 1
Target inputs
Shares, net debt, the current price, the offer and the target’s own trailing and forward metrics
- 2
Selected public companies ($mm except per share)
Price, shares, net debt, revenue, EBITDA and forward EPS per peer, to market capitalisation, enterprise value and multiples, with maximum, 75th, mean, median, 25th and minimum rows
- 3
Implied valuation of the target
The 25th, median and 75th multiples applied to the target’s metrics, to enterprise value, equity value, value per share and the premium to the current price
- 4
PowerPoint
One slide: the comparables table, the implied-value table and a KPI rail of the range against the offer
Where the numbers come from
From the filings: each peer’s shares, net debt, trailing revenue, EBITDA and diluted earnings per share, from that peer’s own filings. Every filed figure resolves to its form, period, page, the printed value and a link into the filing on EDGAR. Not from filings: the peer list you pass, prices from the market data feed, and forward metrics set equal to trailing because filings carry no consensus. Assumptions are listed on the slide’s source line under "Not from filings", so an assumed input is never read as a filed one.
Run it yourself
python3 skills/deal-slides/build.py trading-comps --ticker BSX --peers MDT,SYK,EW --offer 60Delegate · Verify · Decide
The core L3VLUP principle: AI output is never automatically correct. Know what to hand off, what to check, and what only you can own.
Delegate
AI is good enough to do this.
- • Pulling each peer’s shares, net debt and trailing metrics with a citation on each
- • Building market capitalisation, enterprise value and every multiple as live formulas
- • The percentile statistics and the implied valuation block
- • Building the branded slide with both tables and the KPI rail
Verify
AI accelerates you here, but a professional checks the work.
- • That net cash is entered as negative net debt rather than as zero
- • That forward figures are calendarised to the same date for every peer, and the subtitle says so
- • That EBITDA is built the same way for every name, since it is not a tagged figure
- • That no peer in the set is under offer, in distress or otherwise not trading on its own fundamentals
Decide
Judgment the human owns. This is the skill.
- • The peer set, and the one-line reason each name is in or out
- • Whether to segment the set rather than force one universe
- • Which statistic the page leads with, and what a control premium on top of it looks like
- • Whether the implied range is defensible against the offer
Inputs required
- Company name or ticker
- The peer tickers
- The offer per share
- Peer prices, if the feed cannot reach one of them
- Your own numbers where you would rather not use the default
The workflow
- 1
Resolve the company and the peers
Each peer is resolved and read separately, so every line on the page traces to that company’s own filings.
- 2
Write the inclusion logic first
Business model, end markets, growth, margins and scale. The criteria come before the names, or the set becomes whatever the screen returned.
- 3
Read what is filed against what is assumed
Trailing metrics are filed. Forward metrics are set equal to trailing until you supply consensus, and the page says so.
- 4
Build and read the median
The median rather than the mean, because one outlier moves a mean of six. Then the target’s own row at the current price and at the offer.
- 5
Explain the spread
A very wide range usually means the set mixes two business models. Segment it or trim it rather than presenting a range nobody believes.
Run this skill
Any listed company, anywhere. Ticker or name.
Quality checklist
The output isn’t done until every box ticks.
- verify reports zero errors and every derived cell is a formula on blue inputs
- Units are on every table header and the subtitle names the pricing date
- The peer list is named on the slide, and a dropped peer is recorded as a decision rather than an omission
- Forward figures are calendarised to the same date for every peer
- Net cash is entered as negative net debt, not as zero
- The target’s row at the offer sits where the notes say it sits against the median and the 75th percentile