DEFT vs DGXX

Defi Technologies, Inc. vs Digi Power X Inc. — Valuation Comparison 2026

DEFT

Finance Services
Defi Technologies, Inc.
Quality
1.7
out of 10
Value Trap
Price
$0.65
Last close
Models
12/13
Active
VS

DGXX

Finance Services
Digi Power X Inc.
Quality
5.3
out of 10
Value Trap
Price
$7.81
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType DEFT Fair ValueDEFT Upside DGXX Fair ValueDGXX Upside
Bayesian DCF Intrinsic $0.19 -71.5% $2.47 -68.4%
Earnings Power Value Intrinsic $1.59 +102.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.19 -70.8% $1.36 -82.6%
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 DEFT vs DGXX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

DEFT vs DGXX — Which Stock Is More Undervalued?

DGXX scores higher with a 5.3/10 quality rating vs DEFT's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Defi Technologies, Inc. (DEFT) and Digi Power X Inc. (DGXX) 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.

DEFT currently trades at $0.65 with a QOC of 1.7/10, while DGXX trades at $7.81 with a QOC of 5.3/10.

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).