PRU vs SNFCA

Prudential Financial, Inc. vs Security National Financial Cor — Valuation Comparison 2026

PRU

Insurance - Life
Prudential Financial, Inc.
Quality
6.4
out of 10
Value Trap
15
SAFE
Price
$100.61
Last close
Models
10/13
Active
VS

SNFCA

Insurance - Life
Security National Financial Cor
Quality
8.0
out of 10
Value Trap
16
SAFE
Price
$9.45
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType PRU Fair ValuePRU Upside SNFCA Fair ValueSNFCA Upside
Bayesian DCF Intrinsic $87.82 -12.7% $19.98 +111.4%
Earnings Power Value Intrinsic $79.69 -20.8% $12.81 +35.6%
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 PRU vs SNFCA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PRU vs SNFCA — Which Stock Is More Undervalued?

SNFCA scores higher with a 8.0/10 quality rating vs PRU's 6.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Prudential Financial, Inc. (PRU) and Security National Financial Cor (SNFCA) 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.

PRU currently trades at $100.61 with a QOC of 6.4/10, while SNFCA trades at $9.45 with a QOC of 8.0/10.

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).