OSG vs RYAN

Octave Specialty Group, Inc. vs Ryan Specialty Holdings, Inc. — Valuation Comparison 2026

OSG

Insurance - Specialty
Octave Specialty Group, Inc.
Quality
6.0
out of 10
Value Trap
19
SAFE
Price
$5.52
Last close
Models
10/13
Active
VS

RYAN

Insurance - Specialty
Ryan Specialty Holdings, Inc.
Quality
7.5
out of 10
Value Trap
17
SAFE
Price
$31.79
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType OSG Fair ValueOSG Upside RYAN Fair ValueRYAN Upside
Bayesian DCF Intrinsic $26.17 -17.7%
Earnings Power Value Intrinsic $9.05 +63.9%
EROIC Spread Intrinsic $11.23 +103.5% $12.12 -62.9%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $0.54 -90.4% $5.61 -83.7%
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 OSG vs RYAN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

OSG vs RYAN — Which Stock Is More Undervalued?

RYAN scores higher with a 7.5/10 quality rating vs OSG's 6.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Octave Specialty Group, Inc. (OSG) and Ryan Specialty Holdings, Inc. (RYAN) 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.

OSG currently trades at $5.52 with a QOC of 6.0/10, while RYAN trades at $31.79 with a QOC of 7.5/10.

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).