EMA vs ENIC

Emera Incorporated vs Enel Chile S.A. — Valuation Comparison 2026

EMA

Electric Services
Emera Incorporated
Quality
7.0
out of 10
Value Trap
18
SAFE
Price
$52.16
Last close
Models
11/13
Active
VS

ENIC

Electric Services
Enel Chile S.A.
Quality
1.7
out of 10
Value Trap
Price
$4.33
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType EMA Fair ValueEMA Upside ENIC Fair ValueENIC Upside
Bayesian DCF Intrinsic $25.32 -51.5% $1.28 -70.5%
Earnings Power Value Intrinsic $2.35 -48.4%
EROIC Spread Intrinsic $23.89 -54.2% $1.66 -63.6%
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 EMA vs ENIC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EMA vs ENIC — Which Stock Is More Undervalued?

EMA scores higher with a 7.0/10 quality rating vs ENIC's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Emera Incorporated (EMA) and Enel Chile S.A. (ENIC) 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.

EMA currently trades at $52.16 with a QOC of 7.0/10, while ENIC trades at $4.33 with a QOC of 1.7/10.

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).