ROOT vs SIGIP

Root, Inc. vs Selective Insurance Group, Inc. — Valuation Comparison 2026

ROOT

Insurance - Property & Casualty
Root, Inc.
Quality
7.4
out of 10
Value Trap
18
SAFE
Price
$52.50
Last close
Models
10/13
Active
VS

SIGIP

Insurance - Property & Casualty
Selective Insurance Group, Inc.
Quality
8.4
out of 10
Value Trap
Price
$16.53
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType ROOT Fair ValueROOT Upside SIGIP Fair ValueSIGIP Upside
Bayesian DCF Intrinsic $207.95 +296.1%
Earnings Power Value Intrinsic $87.12 +65.9% $58.74 +255.4%
EROIC Spread Intrinsic $39.83 -24.1% $29.29 +77.2%
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 ROOT vs SIGIP — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ROOT vs SIGIP — Which Stock Is More Undervalued?

SIGIP scores higher with a 8.4/10 quality rating vs ROOT's 7.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Root, Inc. (ROOT) and Selective Insurance Group, Inc. (SIGIP) 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.

ROOT currently trades at $52.50 with a QOC of 7.4/10, while SIGIP trades at $16.53 with a QOC of 8.4/10.

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).