ES vs GPJA

Eversource Energy (D/B/A) vs Georgia Power Company Series 20 — Valuation Comparison 2026

ES

Electric Services
Eversource Energy (D/B/A)
Quality
8.3
out of 10
Value Trap
18
SAFE
Price
$68.27
Last close
Models
12/13
Active
VS

GPJA

Electric Services
Georgia Power Company Series 20
Quality
1.6
out of 10
Value Trap
Price
$22.16
Last close
Models
0/13
Active

Model-by-Model Comparison

ModelType ES Fair ValueES Upside GPJA Fair ValueGPJA Upside
Earnings Power Value Intrinsic $4.39 -93.6%
EROIC Spread Intrinsic $34.52 -49.4%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 ES vs GPJA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ES vs GPJA — Which Stock Is More Undervalued?

ES scores higher with a 8.3/10 quality rating vs GPJA's 1.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Eversource Energy (D/B/A) (ES) and Georgia Power Company Series 20 (GPJA) 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.

ES currently trades at $68.27 with a QOC of 8.3/10, while GPJA trades at $22.16 with a QOC of 1.6/10.

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).