LOOP vs OLN

Loop Industries, Inc. vs Olin Corporation — Valuation Comparison 2026

LOOP

Chemicals & Allied Products
Loop Industries, Inc.
Quality
5.5
out of 10
Value Trap
18
SAFE
Price
$1.39
Last close
Models
10/13
Active
VS

OLN

Chemicals & Allied Products
Olin Corporation
Quality
4.4
out of 10
Value Trap
24
SAFE
Price
$25.87
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType LOOP Fair ValueLOOP Upside OLN Fair ValueOLN Upside
Bayesian DCF Intrinsic $0.27 -80.4% $110.93 +328.8%
Earnings Power Value Intrinsic $44.03 +53.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.12 -91.1%
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 LOOP vs OLN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LOOP vs OLN — Which Stock Is More Undervalued?

LOOP scores higher with a 5.5/10 quality rating vs OLN's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Loop Industries, Inc. (LOOP) and Olin Corporation (OLN) 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.

LOOP currently trades at $1.39 with a QOC of 5.5/10, while OLN trades at $25.87 with a QOC of 4.4/10.

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).