WHG vs WT

Westwood Holdings Group Inc vs WisdomTree, Inc. — Valuation Comparison 2026

WHG

Asset Management
Westwood Holdings Group Inc
Quality
8.2
out of 10
Value Trap
23
SAFE
Price
$16.44
Last close
Models
13/13
Active
VS

WT

Asset Management
WisdomTree, Inc.
Quality
8.8
out of 10
Value Trap
23
SAFE
Price
$18.39
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType WHG Fair ValueWHG Upside WT Fair ValueWT Upside
Bayesian DCF Intrinsic $31.34 +90.6% $13.76 -25.2%
Earnings Power Value Intrinsic $5.64 -65.7% $10.97 -40.3%
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 WHG vs WT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

WHG vs WT — Which Stock Is More Undervalued?

WT scores higher with a 8.8/10 quality rating vs WHG's 8.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Westwood Holdings Group Inc (WHG) and WisdomTree, Inc. (WT) 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.

WHG currently trades at $16.44 with a QOC of 8.2/10, while WT trades at $18.39 with a QOC of 8.8/10.

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).