PEO vs PFG

Adams Natural Resources Fund, I vs Principal Financial Group Inc — Valuation Comparison 2026

PEO

Asset Management
Adams Natural Resources Fund, I
Quality
1.7
out of 10
Value Trap
Price
$25.62
Last close
Models
8/13
Active
VS

PFG

Asset Management
Principal Financial Group Inc
Quality
5.2
out of 10
Value Trap
12
SAFE
Price
$103.32
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType PEO Fair ValuePEO Upside PFG Fair ValuePFG Upside
Bayesian DCF Intrinsic $6.78 -73.5% $48.29 -53.3%
Earnings Power Value Intrinsic $81.09 -21.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $25.11 -2.9%
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 PEO vs PFG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PEO vs PFG — Which Stock Is More Undervalued?

PFG scores higher with a 5.2/10 quality rating vs PEO's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Adams Natural Resources Fund, I (PEO) and Principal Financial Group Inc (PFG) 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.

PEO currently trades at $25.62 with a QOC of 1.7/10, while PFG trades at $103.32 with a QOC of 5.2/10.

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).