ACIC vs ASIC

American Coastal Insurance Corp vs Ategrity Specialty Insurance Co — Valuation Comparison 2026

ACIC

Insurance - Property & Casualty
American Coastal Insurance Corp
Quality
8.9
out of 10
Value Trap
33
LOW
Price
$10.52
Last close
Models
10/13
Active
VS

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

Model-by-Model Comparison

ModelType ACIC Fair ValueACIC Upside ASIC Fair ValueASIC Upside
Bayesian DCF Intrinsic $55.92 +431.6% $36.07 +81.4%
Earnings Power Value Intrinsic $15.90 +51.1% $12.36 -37.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 ACIC vs ASIC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ACIC vs ASIC — Which Stock Is More Undervalued?

ACIC scores higher with a 8.9/10 quality rating vs ASIC's 8.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing American Coastal Insurance Corp (ACIC) and Ategrity Specialty Insurance Co (ASIC) 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.

ACIC currently trades at $10.52 with a QOC of 8.9/10, while ASIC trades at $19.88 with a QOC of 8.0/10.

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).