ENGS vs PWR

Energys Group Limited vs Quanta Services, Inc. — Valuation Comparison 2026

ENGS

Electrical Work
Energys Group Limited
Quality
4.2
out of 10
Value Trap
Price
$1.56
Last close
Models
9/13
Active
VS

PWR

Electrical Work
Quanta Services, Inc.
Quality
7.9
out of 10
Value Trap
31
LOW
Price
$711.73
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType ENGS Fair ValueENGS Upside PWR Fair ValuePWR Upside
Bayesian DCF Intrinsic $0.17 -89.0% $210.70 -70.4%
Earnings Power Value Intrinsic $39.08 -94.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $0.92 -37.4% $215.43 -69.7%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for ENGS vs PWR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ENGS vs PWR — Which Stock Is More Undervalued?

PWR scores higher with a 7.9/10 quality rating vs ENGS's 4.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Energys Group Limited (ENGS) and Quanta Services, Inc. (PWR) 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.

ENGS currently trades at $1.56 with a QOC of 4.2/10, while PWR trades at $711.73 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).