Key takeaways
- Values a target using EV/EBITDA, EV/Revenue, or EV/EBIT multiples paid in actual M&A deals, not what listed peers trade at.
- The defining feature: precedents include a control premium (the extra paid to acquire 100% of a company vs. trading a non-controlling share). Typically 20–35% above the unaffected price.
- Use in M&A and PE deal contexts — when the question is "what would someone pay for this company?". Less useful for an equity research target price.
- Triangulate against trading comps (no control premium, ongoing market multiple) and DCF (intrinsic). Three lenses; serious memos show all three.
- Big caveats: market-timing noise (deals at peak market vs trough) and deal-specific synergies (strategic buyers paying for synergies you can't replicate). Adjust for both before relying on the median.
What the analysis is
Precedent transactions analysis answers a specific question: "At what multiple have similar companies actually been acquired?". You build a list of completed M&A deals where the target operated in a similar industry, had similar size and growth, and was acquired in a similar market environment. For each, you compute the implied multiple paid:
Aggregate the multiples across the deal set — typically median and quartile spreads. Apply that range to your target's LTM (or NTM) EBITDA to derive an implied EV range. Compare to your DCF and trading comp ranges. The triangulation is the value.
Building the deal set
The credibility of the analysis lives or dies in the deal selection. Standard filters:
- Industry match — same industry by SIC / GICS, or a tighter sub-segment (e.g., "consumer SaaS" rather than "software").
- Size proximity — typically within 0.5–2.0× the target's EV. A $5B target shouldn't reference a $50M deal.
- Recency — ideally within 3 years; older deals get diluted by market regime change.
- Deal type — strategic vs financial (PE) buyer matters. Strategic premia are typically higher (synergy capture).
- Geography — domestic vs cross-border can carry different multiples (regulatory, FX, integration).
- Completed deals only — terminated deals didn't have a clearing price.
Sources: Mergermarket, S&P Capital IQ, FactSet, Bloomberg M&A. Free fallbacks: SEC filings (DEF 14A merger proxy filings disclose the financial advisor's fairness opinion, which usually includes the precedent set), and major newspapers for deal-announcement coverage.
Worked example
Target: a $3B EV consumer SaaS business with $200M of LTM EBITDA. Deal set of 8 recent SaaS acquisitions:
| Date | Target → Acquirer | EV ($B) | EV/EBITDA |
|---|---|---|---|
| 2025 | SaaS-A → Strategic | 2.5 | 16.0× |
| 2025 | SaaS-B → PE | 4.0 | 14.0× |
| 2024 | SaaS-C → Strategic | 3.2 | 18.0× |
| 2024 | SaaS-D → PE | 2.8 | 13.5× |
| 2024 | SaaS-E → Strategic | 5.5 | 17.5× |
| 2023 | SaaS-F → PE | 3.5 | 12.5× |
| 2023 | SaaS-G → Strategic | 2.2 | 15.0× |
| 2023 | SaaS-H → PE | 4.5 | 13.0× |
Median 14.5×, mean 14.9×. Strategic buyers cluster 15–18×; PE 12.5–14×. Apply to the target's $200M EBITDA: implied EV range $2.6B–$3.6B at the 25th–75th percentile. Compared to a DCF EV of $3.0B and trading-comp EV of $2.4B, the precedent set sits at the higher end — consistent with a control-premium-included multiple.
Precedents vs trading comps vs DCF
| Precedents | Trading comps | DCF | |
|---|---|---|---|
| Control premium included | Yes (~20–35%) | No | No |
| Reflects current market environment | Partially (deals lag) | Yes (real-time) | Yes (via WACC and terminal) |
| Captures synergies | Yes (in strategic deals) | No | Only if modelled explicitly |
| Best for | M&A pitches, PE bid context | Equity research, public-equity valuation | Intrinsic valuation, IC memos |
| Sensitivity to market timing | High (peak vs trough deals) | Medium (current snapshot) | Low (driver-based) |
Common errors
- Cherry-picked deal set. Building a list that supports a target valuation rather than reflecting genuine peer transactions. Senior reviewers will catch this. Be honest about the inclusion criteria.
- Mixing strategic and financial buyers without splitting them. Strategic premia are systematically higher because of synergies. If your buyer universe is one type, the multiples from the other are misleading.
- Stale deals. A 2018 SaaS deal at 25× has very little to say about 2026 valuations. Cap the deal age at 2–3 years unless industry conditions are unchanged.
- Ignoring deal structure. All-stock deals price differently than all-cash. Earn-outs and contingent consideration distort headline EV. Use the disclosed unaffected enterprise value where possible.
- Comparing LTM-at-announcement to forward EBITDA. Match the basis. Most precedent multiples are quoted on LTM at announcement; quote your target on the same basis.
How Smalt AI builds it
For an M&A precedent transactions analysis, Smalt AI:
- Pulls the deal set from public sources (SEC filings, newspaper announcements, company press releases) with explicit inclusion criteria documented.
- Computes EV/EBITDA, EV/Revenue, and EV/EBIT for each precedent, with the source filing and announcement date in cell comments.
- Splits the set into strategic vs financial buyers; reports medians, quartiles, and the spread separately.
- Applies the multiple range to the target to derive an implied EV range, with mid-point and 25th–75th percentile bounds.
- Triangulates against trading comps and DCF in a summary table.
- Flags any precedent older than 3 years for review (regime change risk).
Read more: Comps · Use case: financial modeling · Use case: due diligence.
Further reading
- Rosenbaum & Pearl — Investment Banking, chapter on precedent transactions analysis.
- Damodaran, Aswath — Investment Valuation, on relative valuation and the control premium.