CMII vs COLA

Columbus Circle Capital Corp II vs Columbus Acquisition Corp — Valuation Comparison 2026

CMII

Blank Checks
Columbus Circle Capital Corp II
Quality
3.4
out of 10
Value Trap
Price
$9.92
Last close
Models
6/13
Active
VS

COLA

Blank Checks
Columbus Acquisition Corp
Quality
5.2
out of 10
Value Trap
Price
$10.82
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CMII Fair ValueCMII Upside COLA Fair ValueCOLA Upside
Bayesian DCF Intrinsic $2.62 -73.6% $1.37 -87.1%
Earnings Power Value Intrinsic $1.79 -83.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $9.30 -6.2% $9.95 -5.9%
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 CMII vs COLA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CMII vs COLA — Which Stock Is More Undervalued?

COLA scores higher with a 5.2/10 quality rating vs CMII's 3.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Columbus Circle Capital Corp II (CMII) and Columbus Acquisition Corp (COLA) 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.

CMII currently trades at $9.92 with a QOC of 3.4/10, while COLA trades at $10.82 with a QOC of 5.2/10.

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).