NMR vs PJT

Nomura Holdings Inc vs PJT Partners Inc. — Valuation Comparison 2026

NMR

Capital Markets
Nomura Holdings Inc
Quality
7.7
out of 10
Value Trap
18
SAFE
Price
$7.99
Last close
Models
7/13
Active
VS

PJT

Capital Markets
PJT Partners Inc.
Quality
9.3
out of 10
Value Trap
18
SAFE
Price
$155.67
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType NMR Fair ValueNMR Upside PJT Fair ValuePJT Upside
Bayesian DCF Intrinsic $148.06 -4.9%
Earnings Power Value Intrinsic $37.92 -75.6%
EROIC Spread Intrinsic $19.45 +143.4% $20.09 -87.1%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $41.06 +413.9% $63.26 -59.4%
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 NMR vs PJT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NMR vs PJT — Which Stock Is More Undervalued?

PJT scores higher with a 9.3/10 quality rating vs NMR's 7.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Nomura Holdings Inc (NMR) and PJT Partners Inc. (PJT) 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.

NMR currently trades at $7.99 with a QOC of 7.7/10, while PJT trades at $155.67 with a QOC of 9.3/10.

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).