KIDZ vs LINC

Classover Holdings, Inc. vs Lincoln Educational Services Co — Valuation Comparison 2026

KIDZ

Education & Training Services
Classover Holdings, Inc.
Quality
4.4
out of 10
Value Trap
8
SAFE
Price
$0.42
Last close
Models
8/13
Active
VS

LINC

Education & Training Services
Lincoln Educational Services Co
Quality
7.6
out of 10
Value Trap
16
SAFE
Price
$47.58
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType KIDZ Fair ValueKIDZ Upside LINC Fair ValueLINC Upside
Bayesian DCF Intrinsic $0.14 -65.7% $0.68 -98.6%
Earnings Power Value Intrinsic $6.52 -84.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $0.37 -65.9% $7.61 -84.0%
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 KIDZ vs LINC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

KIDZ vs LINC — Which Stock Is More Undervalued?

LINC scores higher with a 7.6/10 quality rating vs KIDZ's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Classover Holdings, Inc. (KIDZ) and Lincoln Educational Services Co (LINC) 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.

KIDZ currently trades at $0.42 with a QOC of 4.4/10, while LINC trades at $47.58 with a QOC of 7.6/10.

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).