ACGL vs ACGLO

Arch Capital Group Ltd. vs Arch Capital Group Ltd. - Depos — Valuation Comparison 2026

ACGL

Insurance - Diversified
Arch Capital Group Ltd.
Quality
10.0
out of 10
Value Trap
12
SAFE
Price
$90.67
Last close
Models
11/13
Active
VS

ACGLO

Insurance - Diversified
Arch Capital Group Ltd. - Depos
Quality
10.0
out of 10
Value Trap
12
SAFE
Price
$19.87
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType ACGL Fair ValueACGL Upside ACGLO Fair ValueACGLO Upside
Bayesian DCF Intrinsic $171.45 +89.1% $76.68 +285.9%
Earnings Power Value Intrinsic $169.79 +87.3%
EROIC Spread Intrinsic $87.23 -3.8% $55.73 +180.5%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 ACGL vs ACGLO — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ACGL vs ACGLO — Which Stock Is More Undervalued?

Both ACGL and ACGLO score 10.0/10 on quality. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Arch Capital Group Ltd. (ACGL) and Arch Capital Group Ltd. - Depos (ACGLO) 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.

ACGL currently trades at $90.67 with a QOC of 10.0/10, while ACGLO trades at $19.87 with a QOC of 10.0/10.

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).