POR vs PPLC

Portland General Electric Co vs PPL Corporation Corporate — Valuation Comparison 2026

POR

Electric Services
Portland General Electric Co
Quality
7.1
out of 10
Value Trap
18
SAFE
Price
$50.12
Last close
Models
10/13
Active
VS

PPLC

Electric Services
PPL Corporation Corporate
Quality
5.1
out of 10
Value Trap
6
SAFE
Price
$47.91
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType POR Fair ValuePOR Upside PPLC Fair ValuePPLC Upside
Bayesian DCF Intrinsic $111.99 +123.5% $0.97 -98.0%
EROIC Spread Intrinsic $24.31 -51.5% $15.47 -67.7%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $25.57 -49.0% $19.95 -58.4%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
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 POR vs PPLC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

POR vs PPLC — Which Stock Is More Undervalued?

POR scores higher with a 7.1/10 quality rating vs PPLC's 5.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Portland General Electric Co (POR) and PPL Corporation Corporate (PPLC) 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.

POR currently trades at $50.12 with a QOC of 7.1/10, while PPLC trades at $47.91 with a QOC of 5.1/10.

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).