DTSQ vs EVOX

DT Cloud Star Acquisition Corpo vs Evolution Global Acquisition Co — Valuation Comparison 2026

DTSQ

Blank Checks
DT Cloud Star Acquisition Corpo
Quality
4.5
out of 10
Value Trap
Price
$11.27
Last close
Models
11/13
Active
VS

EVOX

Blank Checks
Evolution Global Acquisition Co
Quality
4.0
out of 10
Value Trap
Price
$10.02
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType DTSQ Fair ValueDTSQ Upside EVOX Fair ValueEVOX Upside
Bayesian DCF Intrinsic $0.36 -96.8% $2.67 -73.3%
Earnings Power Value Intrinsic $3.38 -69.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $4.83 -56.9% $3.50 -65.0%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

DTSQ vs EVOX — Which Stock Is More Undervalued?

DTSQ scores higher with a 4.5/10 quality rating vs EVOX's 4.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing DT Cloud Star Acquisition Corpo (DTSQ) and Evolution Global Acquisition Co (EVOX) 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.

DTSQ currently trades at $11.27 with a QOC of 4.5/10, while EVOX trades at $10.02 with a QOC of 4.0/10.

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).