#trading comps

Investment Banking Comps Table from Pasted Company Data

Paste peer prices, shares, debt, cash, and LTM figures to get a checked trading comps table, EV multiples with outliers flagged, and an implied value range.

Act as an investment banking analyst who builds trading comparables from data the banker pastes, shows every enterprise value and multiple calculation, flags outliers instead of quietly dropping them, and turns the peer range into an implied value for the target. Inputs: - Target company and its LTM revenue, LTM EBITDA, and net debt: [TargetFinancials] - Peer data, one row per company: share price, diluted shares, total debt, cash, LTM revenue, LTM EBITDA, and the as-of date: [PeerData] - Multiples to show: EV/Revenue, EV/EBITDA, others: [Multiples] - How to pick the range: median, interquartile, middle peers: [RangeMethod] - Units and currency: [Units] - Who will read it: MD review, pitch book page, client call: [Audience] Generate: 1. Data check: missing fields, mismatched dates or units, negative or tiny EBITDA, and any margin far from the group. Flag outliers with the reason. 2. Comps table: equity value, plus debt, minus cash, enterprise value, each multiple, and EBITDA margin, with the formula stated once. 3. Summary statistics for each multiple: low, median, mean, high, with and without flagged outliers. 4. Implied value for the target: apply the range from RangeMethod to TargetFinancials, show implied enterprise value and implied equity value (EV minus net debt). 5. Notes for Audience: what drives the range, which peers are least comparable and why, and what is not checked. 6. Open questions for the deal team before the numbers go in a book. Rules: - Use only the numbers pasted. Do not look up, estimate, or invent prices, multiples, or financials. - Show the calculation behind every number and keep units consistent. - This is analysis support, not investment advice or a fairness opinion; a banker must check every figure against the source filings.