§ 1.0 / Verdict
MergerIQ Verdict — Final
⟢
Adopted by Risk Committee
Deliverable·Adobe / Figma Analysis
Adobe Inc.
/
Figma Inc.
Elevated
risk.
risk.
Enter a deal and hit Run Analysis to generate your verdict.
Screening signal — directional, not a recommendation to transact or a basis for any deal decision.
Calibrating · live
0.0
%
Deal-readiness score
Base 51.4%
Adjusted +3.0%
Threshold ≥ 65%
0%25%50%→ 54.4%100%
Δ to threshold
−10.6 pp
This curve plots where a deal's readiness score falls across the full range of historical outcomes. Anything to the left of the dashed threshold sits in the zone where obstacles tend to outweigh momentum.
iWhat this means
Deal-readiness score. Above 70% signals strong fundamentals and a favorable regulatory environment. Between 50–70% signals a viable deal that faces real risks worth managing. Below 50% signals significant obstacles — regulatory, financial, or market-related — that warrant close scrutiny. Find where a deal lands on that scale to gauge its readiness at a glance.
§ 2.0
Metrics
Deal Metrics
Four ratios · benchmarked vs. SaaS comparables ±$15B
EBITDA Multiple iHigh
77.8×
Sector avg 23.3×+54.5 pp
Revenue Multiple iBreach
50.0×
Threshold 15×+35× over
EBITDA MarginHealthy
20.0%
Peer 18%+2 pp
Market ScoreOpen
100/100
VIX 16.7favorable
Each bar plots the deal's ratio (colored tick) against its sector benchmark (gray tick). When a tick sits far to the right of its benchmark, the buyer is paying well above the going rate — usually a sign the price is stretched.
§ 3.0
Reasoning
Under The Hood
Adobe / Figma · why this score · 12 steps · 184ms
00:000INPUT›deal = Adobe → Figma, price=$20.0B, rev=$400M, ebitda=$80M
00:008CALC ›revenue_multiple = price / rev = 20000/400 = 50.0×
00:012CALC ›ebitda_multiple = price / ebitda = 20000/80 = 77.8×
00:019CHECK›sector_peer[SaaS] = 23.3× ; delta = +54.5 pp
00:024KILL ›rev_mult > ceiling(15×) → FIRED rule R-01
00:031KILL ›ebitda_mult > sector × 3.0 → ELEVATED rule R-02
00:047CALC ›market_score = f(vix=16.7, rate=4.48, mult=23.3) = 0.997
00:062CALC ›base_prob = logistic( Σ feature × weight ) = 51.4%
00:089CALC ›market_adj = +3.0 pp → adjusted = 54.4%
00:118CHECK›regulator_risk[horizontal_SaaS] = elevated · DOJ 2nd request prob = 0.78
00:152CHECK›54.4% < threshold(65%) ∴ verdict = DO_NOT_PROCEED
00:184OUT ›emit verdict=terminate, counter_bid_breakeven=$14.2B
iWhat this means
This shows exactly why your deal received the score it did — which factors pushed the probability up and which pulled it down. Use it to understand where your deal is strong and where it is vulnerable before a deeper internal review. For example, an unusually high price relative to sales or profit, or elevated regulatory risk, pulls a score down — while healthy margins and calm markets push it up.
§ 4.0
Signals
Model Signals
Drivers · weights · triggered rules
§ 4.1 Prediction Drivers
The five things the model weighed most heavily when scoring this deal.
Deal Size vs. Revenue deal_to_revenue
How big the check is compared to the company's yearly sales. A huge price for modest sales is a warning sign.
12.6%
Revenue Multiple revenue_multiple
How many dollars the buyer pays for every $1 of yearly sales.
12.4%
Profit Multiple ebitda_multiple
How many dollars the buyer pays for every $1 of operating profit.
11.0%
Premium Paid premium
How far above the company's market price the buyer is willing to go.
8.0%
Industry industry_encoded
The sector the company is in — it sets the benchmark and how closely regulators will look.
7.5%
§ 4.2 Kill Criteria — 2 firing
Two thresholds breached; analyst override required.
!
R-01
Elevated Revenue Multiple · 00:024
50.0× exceeds the 15× threshold — significant overvaluation risk relative to SaaS comparables.
50.0×
▲
R-02
Aggressive EBITDA Multiple · 00:031
77.8× requires exceptional sustained growth assumptions to justify the price paid.
77.8×
iWhat this means
Kill criteria are the specific characteristics of your deal that most strongly predict failure based on historical transactions. They are not guaranteed deal-breakers, but they are exactly what a regulator, board, or financing party is likely to challenge. Each one is drawn from real deals that failed for the same reason.
§ 5.0
Market
Market Context for this Deal
Adobe / Figma · live at runtime · last tick 14:32:08
Risk-Free Rate · 10Y i
4.48%
30d low 4.21+27 bp
VIX · Volatility i
16.7
30d high 22.1−5.4
SaaS Multiple · EV/Rev
23.3×
30d low 18.2×+2.4×
Market Score · Composite
0.997
30d low 0.81+0.19
iWhat this means
These are the live market conditions at the moment you ran this analysis. The same deal can have very different outcomes depending on when it happens — a deal in a high-VIX, rising-rate environment faces a much harder path than the same deal in a stable market. MergerIQ adjusts the score accordingly. For example, calm volatility and a stable rate add to a deal's odds, while a spike in either pulls them down.
Sparklines show the 30-day trend for each signal. Calm volatility and a stable rate are tailwinds for any deal; rising ones are headwinds.
§ 6.0
Scenarios
Adobe / Figma — Scenario Analysis
5-year hold · Monte Carlo · 10,000 paths
§ 6.1 Probability Fan
Five-year value distribution. Median crosses break-even in year 3.
P90 upside
P50 median
P10 downside
↑ P90
Upside
The best case — if almost everything goes right: fast growth and strong profit margins.
Growth25%
Margin25.0%
Exit85.6×
RiskLow
28%
→ P50
Base
The most likely case — steady, expected performance with no big surprises.
Growth15%
Margin20.0%
Exit70.0×
RiskMedium
18%
↓ P10
Downside
The worst case — if things go wrong: slow growth and pressure on profit margins.
Growth5%
Margin15.0%
Exit54.4×
RiskHigh
8%
iWhat this means
Scenario analysis stress-tests the deal three ways. The Upside is the best case if things go well, the Base is the most likely outcome, and the Downside is the worst case if things go poorly. The big number on each card is the yearly return (IRR) — roughly how much you'd earn per year on the money invested. As a rule of thumb, a wide gap between the upside and downside means the outcome is highly uncertain, while a low return even in the upside case is a sign the price leaves little room for error.
The fan shows the five-year value range: P90 upside (green), P50 median (black), P10 downside (red). The later the median line crosses break-even, the longer the money is at risk before the deal starts to pay off.
§ 7.0
Comparables
Adobe / Figma — Comparable Transactions
SaaS · ±$15B · 2016–2022
§ 7.1 EV/Rev × Premium
n = 7
Completed
Terminated
§ 7.2 Transactions
6 deals · sorted by EV
| Transaction | Yr | EV i | EV/Rev i | EV/EBITDA i | Outcome |
|---|---|---|---|---|---|
| Salesforce / Slack | '20 | $27.7B | 30.7× | — | Completed |
| Microsoft / LinkedIn | '16 | $26.2B | 8.7× | 29.1× | Completed |
| Oracle / Cerner | '22 | $28.3B | 5.1× | 30.9× | Completed |
| Broadcom / VMware | '22 | $61.0B | 4.5× | 22.4× | Completed |
| Take-Two / Zynga | '22 | $12.7B | 5.4× | 28.0× | Completed |
| Visa / Plaid | '20 | $5.3B | 53.0× | — | Terminated |
iWhat this means
Comparable transactions are real historical deals most similar to yours — matched by industry, deal size, payment type, and market conditions. If comparable deals completed at similar multiples, that is a positive signal; if they were blocked, study why — those reasons are likely relevant to yours too. For example, if the closest comparable by price was blocked or terminated, treat it as a warning the deal could face the same scrutiny.
Each dot is a past deal plotted by premium (x) and price-to-revenue (y). Deals in the shaded danger zone above 15× revenue rarely clear cleanly — the further into it a deal sits, the harder its path.
§ 8.0
Outcome
Deal Autopsy · What actually happened
15 months · Sept 2022 → Dec 2023
Run an analysis above to see the deal outcome and timeline.
15 Sept 2022
Deal announced
$20B cash + stock · 50× rev multiple
Nov 2022
Stockholder approval
Both boards signed off · no internal friction
June 2023
EU Phase II opened
Horizontal-overlap concerns flagged · CMA followed
Nov 2023
Statement of objections
EU + UK both objected · remedies inadequate
18 Dec 2023
Terminated
Adobe paid $1.0B break fee · Figma resumes IPO path
§ 9.0
Memo
§ 10.0
Colophon
About MergerIQ
Dataset · performance · author
The Product
⟢
Currently in beta
Built at the intersection of machine learning, financial analysis, and real-time market data.
MergerIQ is an AI-powered pre-diligence screening tool, built at the intersection of machine learning, financial analysis, and real-time market data.
The model learns from a curated set of M&A transactions spanning 2012–2024 — across SaaS, fintech, hardware, healthcare, defense, telecom, and consumer sectors — each labeled with its real outcome and the market conditions at the time. Coverage is deepest in SaaS and hardware, and the dataset is built to expand as new transaction data comes online.
On a small held-out sample, indicative performance is ~85% accuracy and ~0.82 AUC — beta figures that will sharpen as the dataset grows. Each deal’s score is adjusted using live market signals: the VIX, the 10-year Treasury yield, and sector valuation multiples. Every screen also generates a first-draft memo — thesis, risks, and a recommendation for your review.
MergerIQ is in beta. Scores are directional, not predictions of outcome.
§ 10.1 By the Numbers
The model in numbers.
Training set
Hand-curated
Coverage
2012 → 2024
Sectors
11 verticals
Cross-val accuracy
85.5%
ROC-AUC
0.821
Live signals
VIX · 10Y · Multiples
Deal memos
AI-generated
Status
beta
Riya Pradhan
University of Toronto, Economics · Incoming Georgetown McDonough MSBA '27. Applied machine learning & M&A — let's connect.
© 2026 MergerIQ · A product of LaunchDream, LLC — an AI venture studio · Proprietary & confidential
Model output — not investment, legal, or financial advice.
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