ICE vs NDAQ

Intercontinental Exchange Inc. vs Nasdaq, Inc. — Valuation Comparison 2026

ICE

Financial Data & Stock Exchanges
Intercontinental Exchange Inc.
Quality
9.1
out of 10
Value Trap
17
SAFE
Price
$148.30
Last close
Models
12/13
Active
VS

NDAQ

Financial Data & Stock Exchanges
Nasdaq, Inc.
Quality
7.3
out of 10
Value Trap
41
WARN
Price
$91.00
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType ICE Fair ValueICE Upside NDAQ Fair ValueNDAQ Upside
Bayesian DCF Intrinsic $117.59 -20.7% $65.26 -28.3%
Earnings Power Value Intrinsic $43.21 -70.9% $10.14 -88.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 ICE vs NDAQ — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ICE vs NDAQ — Which Stock Is More Undervalued?

ICE scores higher with a 9.1/10 quality rating vs NDAQ's 7.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Intercontinental Exchange Inc. (ICE) and Nasdaq, Inc. (NDAQ) 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.

ICE currently trades at $148.30 with a QOC of 9.1/10, while NDAQ trades at $91.00 with a QOC of 7.3/10.

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).