CAE vs ELVA

CAE Inc. vs Electrovaya Inc. — Valuation Comparison 2026

CAE

Miscellaneous Electrical Machinery, Equipment & Supplies
CAE Inc.
Quality
8.0
out of 10
Value Trap
13
SAFE
Price
$25.81
Last close
Models
12/13
Active
VS

ELVA

Miscellaneous Electrical Machinery, Equipment & Supplies
Electrovaya Inc.
Quality
1.9
out of 10
Value Trap
Price
$11.69
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType CAE Fair ValueCAE Upside ELVA Fair ValueELVA Upside
Bayesian DCF Intrinsic $8.24 -68.1% $2.71 -76.8%
Earnings Power Value Intrinsic $18.80 -27.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $49.95 +93.5% $2.50 -75.6%
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 CAE vs ELVA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CAE vs ELVA — Which Stock Is More Undervalued?

CAE scores higher with a 8.0/10 quality rating vs ELVA's 1.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing CAE Inc. (CAE) and Electrovaya Inc. (ELVA) 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.

CAE currently trades at $25.81 with a QOC of 8.0/10, while ELVA trades at $11.69 with a QOC of 1.9/10.

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).