EDN vs ELPC

Empresa Distribuidora Y Comerci vs Companhia Paranaense de Energia — Valuation Comparison 2026

EDN

Electric Services
Empresa Distribuidora Y Comerci
Quality
1.9
out of 10
Value Trap
Price
$27.64
Last close
Models
7/13
Active
VS

ELPC

Electric Services
Companhia Paranaense de Energia
Quality
7.9
out of 10
Value Trap
6
SAFE
Price
$11.40
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType EDN Fair ValueEDN Upside ELPC Fair ValueELPC Upside
Bayesian DCF Intrinsic $6.61 -76.1% $5.76 -49.5%
Earnings Power Value Intrinsic $6.66 -47.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $5.20 -79.2% $2.47 -78.3%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-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 EDN vs ELPC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EDN vs ELPC — Which Stock Is More Undervalued?

ELPC scores higher with a 7.9/10 quality rating vs EDN's 1.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Empresa Distribuidora Y Comerci (EDN) and Companhia Paranaense de Energia (ELPC) 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.

EDN currently trades at $27.64 with a QOC of 1.9/10, while ELPC trades at $11.40 with a QOC of 7.9/10.

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).