GLXY vs HGBL

Galaxy Digital Inc. vs Heritage Global Inc. — Valuation Comparison 2026

GLXY

Capital Markets
Galaxy Digital Inc.
Quality
6.4
out of 10
Value Trap
Price
$30.13
Last close
Models
11/13
Active
VS

HGBL

Capital Markets
Heritage Global Inc.
Quality
7.2
out of 10
Value Trap
12
SAFE
Price
$1.26
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType GLXY Fair ValueGLXY Upside HGBL Fair ValueHGBL Upside
Bayesian DCF Intrinsic $31.72 +5.3% $2.76 +119.3%
Earnings Power Value Intrinsic $26.00 -13.7% $0.72 -42.9%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

GLXY vs HGBL — Which Stock Is More Undervalued?

HGBL scores higher with a 7.2/10 quality rating vs GLXY's 6.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Galaxy Digital Inc. (GLXY) and Heritage Global Inc. (HGBL) 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.

GLXY currently trades at $30.13 with a QOC of 6.4/10, while HGBL trades at $1.26 with a QOC of 7.2/10.

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).