NTRSO vs NXP

Northern Trust Corporation - De vs Nuveen Select Tax Free Income P — Valuation Comparison 2026

NTRSO

Asset Management
Northern Trust Corporation - De
Quality
7.3
out of 10
Value Trap
20
SAFE
Price
$18.94
Last close
Models
5/13
Active
VS

NXP

Asset Management
Nuveen Select Tax Free Income P
Quality
1.7
out of 10
Value Trap
Price
$14.23
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType NTRSO Fair ValueNTRSO Upside NXP Fair ValueNXP Upside
Bayesian DCF Intrinsic $3.77 -73.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $103.25 +437.5% $17.40 +22.9%
Markov DDM Intrinsic $18.18 -4.0% $7.43 -47.1%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

NTRSO vs NXP — Which Stock Is More Undervalued?

NTRSO scores higher with a 7.3/10 quality rating vs NXP's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Northern Trust Corporation - De (NTRSO) and Nuveen Select Tax Free Income P (NXP) 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.

NTRSO currently trades at $18.94 with a QOC of 7.3/10, while NXP trades at $14.23 with a QOC of 1.7/10.

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).