ASPI vs ESI

ASP Isotopes Inc. vs Element Solutions Inc. — Valuation Comparison 2026

ASPI

Miscellaneous Chemical Products
ASP Isotopes Inc.
Quality
5.7
out of 10
Value Trap
24
SAFE
Price
$7.78
Last close
Models
11/13
Active
VS

ESI

Miscellaneous Chemical Products
Element Solutions Inc.
Quality
9.1
out of 10
Value Trap
12
SAFE
Price
$42.43
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType ASPI Fair ValueASPI Upside ESI Fair ValueESI Upside
Bayesian DCF Intrinsic $1.53 -80.3% $9.41 -77.8%
Earnings Power Value Intrinsic $0.06 -98.8% $8.54 -79.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 ASPI vs ESI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ASPI vs ESI — Which Stock Is More Undervalued?

ESI scores higher with a 9.1/10 quality rating vs ASPI's 5.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing ASP Isotopes Inc. (ASPI) and Element Solutions Inc. (ESI) 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.

ASPI currently trades at $7.78 with a QOC of 5.7/10, while ESI trades at $42.43 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).