LEA vs LIDR

Lear Corporation vs AEye, Inc. — Valuation Comparison 2026

LEA

Motor Vehicle Parts & Accessories
Lear Corporation
Quality
8.4
out of 10
Value Trap
Price
$143.12
Last close
Models
13/13
Active
VS

LIDR

Motor Vehicle Parts & Accessories
AEye, Inc.
Quality
5.3
out of 10
Value Trap
41
WARN
Price
$2.00
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType LEA Fair ValueLEA Upside LIDR Fair ValueLIDR Upside
Bayesian DCF Intrinsic $41.62 -70.9% $1.02 -49.0%
Earnings Power Value Intrinsic $135.73 -5.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $146.90 +2.6% $2.31 +15.5%
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 LEA vs LIDR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LEA vs LIDR — Which Stock Is More Undervalued?

LEA scores higher with a 8.4/10 quality rating vs LIDR's 5.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Lear Corporation (LEA) and AEye, Inc. (LIDR) 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.

LEA currently trades at $143.12 with a QOC of 8.4/10, while LIDR trades at $2.00 with a QOC of 5.3/10.

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).