ARTC vs CHAR

Art Technology Acquisition Corp vs Charlton Aria Acquisition Corpo — Valuation Comparison 2026

ARTC

Blank Checks
Art Technology Acquisition Corp
Quality
4.0
out of 10
Value Trap
Price
$9.92
Last close
Models
7/13
Active
VS

CHAR

Blank Checks
Charlton Aria Acquisition Corpo
Quality
4.9
out of 10
Value Trap
10
SAFE
Price
$10.74
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType ARTC Fair ValueARTC Upside CHAR Fair ValueCHAR Upside
Bayesian DCF Intrinsic $2.65 -73.2% $1.08 -89.9%
Earnings Power Value Intrinsic $1.41 -86.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $3.92 -60.5% $3.88 -63.9%
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 ARTC vs CHAR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ARTC vs CHAR — Which Stock Is More Undervalued?

CHAR scores higher with a 4.9/10 quality rating vs ARTC's 4.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Art Technology Acquisition Corp (ARTC) and Charlton Aria Acquisition Corpo (CHAR) 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.

ARTC currently trades at $9.92 with a QOC of 4.0/10, while CHAR trades at $10.74 with a QOC of 4.9/10.

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).