How to Answer “Walk me through a precedent transactions analysis.” in Investment Banking Interviews

I start by defining precedent transactions analysis as valuing a company using valuation multiples and premiums paid in comparable historical M&A deals—typically reflecting control value at the time of the transaction.

Then I set scope so the output is defensible: subsector/business model, geography, size (revenue/EBITDA), growth/margins, and a timeframe that matches today’s market regime (often the last 2–5 years, adjusted for sector cycles). I also clarify whether we’re valuing the whole enterprise or focusing on equity value.

In interviews, I explicitly flag that deal environment matters—rates, financing availability, and buyer appetite can move multiples—so “recency + comparability” is a core part of the method, not an afterthought.

Next, I compile a relevant transaction set from credible deal sources and filings, and I record the key deal terms that impact valuation: announcement/close date, headline purchase price, % acquired (100% vs minority), consideration mix (cash/stock), earn-outs/contingent consideration, and any assumed debt or off-balance-sheet items that are treated like debt in the deal.

I also tag buyer type (strategic vs sponsor), deal rationale (platform vs tuck-in), and whether synergies were a major driver—because those factors often explain outliers.

If a deal isn’t truly comparable (distress, unusual carve-out complexity, very different unit economics), I exclude it from the core set or separate it into an “extended set” with clear notes.

Then I align each deal to a consistent financial basis—usually LTM at announcement (or NTM if the sector prices forward)—and normalise EBITDA/EBIT for one-offs so I’m comparing operating performance rather than noise.

Mechanically, I calculate the equity value paid (offer price × fully diluted shares, adjusted for % acquired when relevant), then derive enterprise value by adding net debt and other claims such as preferred and minority interest (and adjusting for non-operating assets if appropriate). From there, I compute deal multiples like EV/Revenue and EV/EBITDA (and EV/EBIT where useful).

In parallel, I compute takeover premiums to the unaffected share price (e.g., 1-day/1-week/30-day) as a cross-check on “control paid,” especially when capital structures differ across targets.

Once the raw multiples are calculated, I review the distribution, identify outliers, and tie them back to deal-specific reasons—competitive auction, unusually high synergy expectations, distress, or a unique buyer.

I typically present a tighter “core” range (often anchored by the most comparable and most recent deals) plus the broader observed range, with brief notes on what drives the low and high ends.

To value the target, I apply the selected multiple range to the target’s relevant metric (LTM or NTM EBITDA/revenue), producing an implied enterprise value range, then bridge to equity value by subtracting net debt and other claims. I sanity-check against trading comps and a DCF to ensure the precedent range makes economic sense in today’s market.

Finally, I run quick checks that prevent silent errors: consistent EV definitions across deals, correct treatment of leases/pensions where relevant, consistent currency and timing, and no mixing of LTM vs NTM without being explicit. For premiums, I confirm the correct “unaffected” reference price and adjust for unusual trading or deal rumours.

For the output, I keep it simple and decision-useful: a deal summary table, calculated multiples/premiums, and 3–5 takeaways on what drives the range (buyer type, cycle timing, scarcity, synergy potential).

In an interview, I compress this into a clean narrative: what precedents measure, how I build and clean the set, and how I translate the range into implied value and cross-check it—showing I can do the work and explain it.