LODE vs LYB

Comstock Inc. vs LyondellBasell Industries NV — Valuation Comparison 2026

LODE

Industrial Organic Chemicals
Comstock Inc.
Quality
6.3
out of 10
Value Trap
40
WARN
Price
$4.15
Last close
Models
9/13
Active
VS

LYB

Industrial Organic Chemicals
LyondellBasell Industries NV
Quality
6.6
out of 10
Value Trap
14
SAFE
Price
$66.65
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType LODE Fair ValueLODE Upside LYB Fair ValueLYB Upside
Bayesian DCF Intrinsic $1.21 -70.8% $68.23 +2.4%
Earnings Power Value Intrinsic $13.46 -79.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $1.39 -66.5% $38.54 -42.2%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for LODE vs LYB — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LODE vs LYB — Which Stock Is More Undervalued?

LYB scores higher with a 6.6/10 quality rating vs LODE's 6.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Comstock Inc. (LODE) and LyondellBasell Industries NV (LYB) 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.

LODE currently trades at $4.15 with a QOC of 6.3/10, while LYB trades at $66.65 with a QOC of 6.6/10.

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).