ESP vs FLUX

Espey Mfg. & Electronics Corp. vs Flux Power Holdings, Inc. — Valuation Comparison 2026

ESP

Electrical Equipment & Parts
Espey Mfg. & Electronics Corp.
Quality
8.9
out of 10
Value Trap
26
LOW
Price
$59.64
Last close
Models
13/13
Active
VS

FLUX

Electrical Equipment & Parts
Flux Power Holdings, Inc.
Quality
5.8
out of 10
Value Trap
12
SAFE
Price
$1.12
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType ESP Fair ValueESP Upside FLUX Fair ValueFLUX Upside
Bayesian DCF Intrinsic $63.66 +6.7% $0.02 -98.6%
Earnings Power Value Intrinsic $31.40 -47.4% $2.12 +61.5%
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 ESP vs FLUX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ESP vs FLUX — Which Stock Is More Undervalued?

ESP scores higher with a 8.9/10 quality rating vs FLUX's 5.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Espey Mfg. & Electronics Corp. (ESP) and Flux Power Holdings, Inc. (FLUX) 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.

ESP currently trades at $59.64 with a QOC of 8.9/10, while FLUX trades at $1.12 with a QOC of 5.8/10.

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).