CME vs COIN

CME Group Inc. vs Coinbase Global, Inc. — Valuation Comparison 2026

CME

Financial Data & Stock Exchanges
CME Group Inc.
Quality
9.8
out of 10
Value Trap
12
SAFE
Price
$277.42
Last close
Models
12/13
Active
VS

COIN

Financial Data & Stock Exchanges
Coinbase Global, Inc.
Quality
9.1
out of 10
Value Trap
35
LOW
Price
$182.25
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType CME Fair ValueCME Upside COIN Fair ValueCOIN Upside
Bayesian DCF Intrinsic $174.72 -37.0% $149.14 -18.2%
Earnings Power Value Intrinsic $77.99 -71.9% $40.38 -77.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for CME vs COIN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CME vs COIN — Which Stock Is More Undervalued?

CME scores higher with a 9.8/10 quality rating vs COIN's 9.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing CME Group Inc. (CME) and Coinbase Global, Inc. (COIN) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

CME currently trades at $277.42 with a QOC of 9.8/10, while COIN trades at $182.25 with a QOC of 9.1/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).