DAKT vs FLEX

Daktronics, Inc. vs Flex Ltd. — Valuation Comparison 2026

DAKT

Electronic Components
Daktronics, Inc.
Quality
9.0
out of 10
Value Trap
10
SAFE
Price
$20.62
Last close
Models
13/13
Active
VS

FLEX

Electronic Components
Flex Ltd.
Quality
9.1
out of 10
Value Trap
24
SAFE
Price
$144.85
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType DAKT Fair ValueDAKT Upside FLEX Fair ValueFLEX Upside
Bayesian DCF Intrinsic $15.12 -26.7% $42.34 -70.8%
Earnings Power Value Intrinsic $9.79 -52.5% $29.01 -80.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 DAKT vs FLEX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

DAKT vs FLEX — Which Stock Is More Undervalued?

FLEX scores higher with a 9.1/10 quality rating vs DAKT's 9.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Daktronics, Inc. (DAKT) and Flex Ltd. (FLEX) 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.

DAKT currently trades at $20.62 with a QOC of 9.0/10, while FLEX trades at $144.85 with a QOC of 9.1/10.

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).