ASIC vs BOW

Ategrity Specialty Insurance Co vs Bowhead Specialty Holdings Inc. — Valuation Comparison 2026

ASIC

Insurance - Property & Casualty
Ategrity Specialty Insurance Co
Quality
8.0
out of 10
Value Trap
Price
$19.88
Last close
Models
11/13
Active
VS

BOW

Insurance - Property & Casualty
Bowhead Specialty Holdings Inc.
Quality
9.5
out of 10
Value Trap
Price
$26.82
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType ASIC Fair ValueASIC Upside BOW Fair ValueBOW Upside
Bayesian DCF Intrinsic $36.07 +81.4%
Earnings Power Value Intrinsic $12.36 -37.9% $10.81 -59.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $24.98 +25.6% $64.66 +141.1%
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 ASIC vs BOW — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ASIC vs BOW — Which Stock Is More Undervalued?

BOW scores higher with a 9.5/10 quality rating vs ASIC's 8.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Ategrity Specialty Insurance Co (ASIC) and Bowhead Specialty Holdings Inc. (BOW) 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.

ASIC currently trades at $19.88 with a QOC of 8.0/10, while BOW trades at $26.82 with a QOC of 9.5/10.

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).