NDAQ vs TRU

Nasdaq, Inc. vs TransUnion — Valuation Comparison 2026

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
VS

TRU

Financial Data & Stock Exchanges
TransUnion
Quality
7.3
out of 10
Value Trap
12
SAFE
Price
$71.66
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType NDAQ Fair ValueNDAQ Upside TRU Fair ValueTRU Upside
Bayesian DCF Intrinsic $65.26 -28.3% $17.12 -76.1%
Earnings Power Value Intrinsic $10.14 -88.9% $47.45 -33.8%
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 NDAQ vs TRU — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NDAQ vs TRU — Which Stock Is More Undervalued?

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

Comparing Nasdaq, Inc. (NDAQ) and TransUnion (TRU) 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.

NDAQ currently trades at $91.00 with a QOC of 7.3/10, while TRU trades at $71.66 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).