ARX vs BWIN

Accelerant Holdings vs The Baldwin Insurance Group, In — Valuation Comparison 2026

ARX

Insurance Brokers
Accelerant Holdings
Quality
5.3
out of 10
Value Trap
Price
$16.27
Last close
Models
12/13
Active
VS

BWIN

Insurance Brokers
The Baldwin Insurance Group, In
Quality
6.7
out of 10
Value Trap
11
SAFE
Price
$19.86
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType ARX Fair ValueARX Upside BWIN Fair ValueBWIN Upside
Bayesian DCF Intrinsic $33.07 +103.3% $0.92 -95.4%
Earnings Power Value Intrinsic $14.25 +8.8% $16.40 -34.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 ARX vs BWIN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ARX vs BWIN — Which Stock Is More Undervalued?

BWIN scores higher with a 6.7/10 quality rating vs ARX's 5.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Accelerant Holdings (ARX) and The Baldwin Insurance Group, In (BWIN) 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.

ARX currently trades at $16.27 with a QOC of 5.3/10, while BWIN trades at $19.86 with a QOC of 6.7/10.

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).