KINS vs MCY

Kingstone Companies, Inc vs Mercury General Corporation — Valuation Comparison 2026

KINS

Insurance - Property & Casualty
Kingstone Companies, Inc
Quality
10.0
out of 10
Value Trap
12
SAFE
Price
$15.35
Last close
Models
11/13
Active
VS

MCY

Insurance - Property & Casualty
Mercury General Corporation
Quality
9.6
out of 10
Value Trap
12
SAFE
Price
$96.53
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType KINS Fair ValueKINS Upside MCY Fair ValueMCY Upside
Bayesian DCF Intrinsic $46.73 +204.4% $173.51 +79.8%
Earnings Power Value Intrinsic $30.92 +101.4% $165.12 +71.1%
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 KINS vs MCY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

KINS vs MCY — Which Stock Is More Undervalued?

KINS scores higher with a 10.0/10 quality rating vs MCY's 9.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Kingstone Companies, Inc (KINS) and Mercury General Corporation (MCY) 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.

KINS currently trades at $15.35 with a QOC of 10.0/10, while MCY trades at $96.53 with a QOC of 9.6/10.

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).