APOG vs ASPN

Apogee Enterprises, Inc. vs Aspen Aerogels, Inc. — Valuation Comparison 2026

APOG

Building Products & Equipment
Apogee Enterprises, Inc.
Quality
8.0
out of 10
Value Trap
18
SAFE
Price
$37.74
Last close
Models
13/13
Active
VS

ASPN

Building Products & Equipment
Aspen Aerogels, Inc.
Quality
5.8
out of 10
Value Trap
39
LOW
Price
$6.37
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType APOG Fair ValueAPOG Upside ASPN Fair ValueASPN Upside
Bayesian DCF Intrinsic $62.42 +65.4% $8.17 +28.3%
Earnings Power Value Intrinsic $28.79 -23.7% $33.54 +426.6%
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 APOG vs ASPN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

APOG vs ASPN — Which Stock Is More Undervalued?

APOG scores higher with a 8.0/10 quality rating vs ASPN's 5.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Apogee Enterprises, Inc. (APOG) and Aspen Aerogels, Inc. (ASPN) 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.

APOG currently trades at $37.74 with a QOC of 8.0/10, while ASPN trades at $6.37 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).