POWL vs SDST

Powell Industries, Inc. vs Stardust Power Inc. — Valuation Comparison 2026

POWL

Electrical Equipment & Parts
Powell Industries, Inc.
Quality
10.0
out of 10
Value Trap
28
LOW
Price
$288.90
Last close
Models
13/13
Active
VS

SDST

Electrical Equipment & Parts
Stardust Power Inc.
Quality
3.6
out of 10
Value Trap
22
SAFE
Price
$2.39
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType POWL Fair ValuePOWL Upside SDST Fair ValueSDST Upside
Bayesian DCF Intrinsic $62.83 -78.3% $0.66 -72.4%
Earnings Power Value Intrinsic $67.17 -76.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $288.01 -0.3% $3.74 +56.6%
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 POWL vs SDST — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

POWL vs SDST — Which Stock Is More Undervalued?

POWL scores higher with a 10.0/10 quality rating vs SDST's 3.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Powell Industries, Inc. (POWL) and Stardust Power Inc. (SDST) 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.

POWL currently trades at $288.90 with a QOC of 10.0/10, while SDST trades at $2.39 with a QOC of 3.6/10.

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).