LOPE vs LRN

Grand Canyon Education, Inc. vs Stride, Inc. — Valuation Comparison 2026

LOPE

Education & Training Services
Grand Canyon Education, Inc.
Quality
9.1
out of 10
Value Trap
26
LOW
Price
$150.56
Last close
Models
12/13
Active
VS

LRN

Education & Training Services
Stride, Inc.
Quality
8.7
out of 10
Value Trap
12
SAFE
Price
$91.97
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType LOPE Fair ValueLOPE Upside LRN Fair ValueLRN Upside
Bayesian DCF Intrinsic $89.63 -40.5% $93.07 +1.2%
Earnings Power Value Intrinsic $50.52 -66.4% $122.94 +33.7%
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 LOPE vs LRN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LOPE vs LRN — Which Stock Is More Undervalued?

LOPE scores higher with a 9.1/10 quality rating vs LRN's 8.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Grand Canyon Education, Inc. (LOPE) and Stride, Inc. (LRN) 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.

LOPE currently trades at $150.56 with a QOC of 9.1/10, while LRN trades at $91.97 with a QOC of 8.7/10.

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).