HMN vs L

Horace Mann Educators Corporati vs Loews Corporation — Valuation Comparison 2026

HMN

Insurance - Property & Casualty
Horace Mann Educators Corporati
Quality
7.7
out of 10
Value Trap
18
SAFE
Price
$46.35
Last close
Models
11/13
Active
VS

L

Insurance - Property & Casualty
Loews Corporation
Quality
8.2
out of 10
Value Trap
3
SAFE
Price
$104.82
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType HMN Fair ValueHMN Upside L Fair ValueL Upside
Bayesian DCF Intrinsic $150.65 +225.0% $311.23 +196.9%
Earnings Power Value Intrinsic $19.91 -57.1% $30.21 -71.2%
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 HMN vs L — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

HMN vs L — Which Stock Is More Undervalued?

L scores higher with a 8.2/10 quality rating vs HMN's 7.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Horace Mann Educators Corporati (HMN) and Loews Corporation (L) 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.

HMN currently trades at $46.35 with a QOC of 7.7/10, while L trades at $104.82 with a QOC of 8.2/10.

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).