LRN vs MH

Stride, Inc. vs McGraw Hill, Inc. — Valuation Comparison 2026

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
VS

MH

Education & Training Services
McGraw Hill, Inc.
Quality
1.7
out of 10
Value Trap
Price
$11.74
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType LRN Fair ValueLRN Upside MH Fair ValueMH Upside
Bayesian DCF Intrinsic $93.07 +1.2% $3.46 -70.5%
Earnings Power Value Intrinsic $122.94 +33.7% $5.65 -56.5%
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 LRN vs MH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LRN vs MH — Which Stock Is More Undervalued?

LRN scores higher with a 8.7/10 quality rating vs MH's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Stride, Inc. (LRN) and McGraw Hill, Inc. (MH) 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.

LRN currently trades at $91.97 with a QOC of 8.7/10, while MH trades at $11.74 with a QOC of 1.7/10.

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).