PPLC vs SO

PPL Corporation Corporate vs Southern Company (The) — Valuation Comparison 2026

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
VS

SO

Electric Services
Southern Company (The)
Quality
6.6
out of 10
Value Trap
24
SAFE
Price
$92.05
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType PPLC Fair ValuePPLC Upside SO Fair ValueSO Upside
Bayesian DCF Intrinsic $0.97 -98.0% $66.86 -27.4%
Earnings Power Value Intrinsic $4.58 -95.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $19.95 -58.4% $36.84 -60.0%
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 PPLC vs SO — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PPLC vs SO — Which Stock Is More Undervalued?

SO scores higher with a 6.6/10 quality rating vs PPLC's 5.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing PPL Corporation Corporate (PPLC) and Southern Company (The) (SO) 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.

PPLC currently trades at $47.91 with a QOC of 5.1/10, while SO trades at $92.05 with a QOC of 6.6/10.

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).