AFYA vs CHGG

Afya Limited vs Chegg, Inc. — Valuation Comparison 2026

AFYA

Education & Training Services
Afya Limited
Quality
8.8
out of 10
Value Trap
Price
$14.05
Last close
Models
12/13
Active
VS

CHGG

Education & Training Services
Chegg, Inc.
Quality
5.9
out of 10
Value Trap
25
LOW
Price
$1.50
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType AFYA Fair ValueAFYA Upside CHGG Fair ValueCHGG Upside
Bayesian DCF Intrinsic $16.05 +14.3%
Earnings Power Value Intrinsic $25.48 +81.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $38.55 +174.4% $1.07 -28.9%
Dynamic NAV Asset-Based $1.97 +31.5%
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 AFYA vs CHGG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AFYA vs CHGG — Which Stock Is More Undervalued?

AFYA scores higher with a 8.8/10 quality rating vs CHGG's 5.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Afya Limited (AFYA) and Chegg, Inc. (CHGG) 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.

AFYA currently trades at $14.05 with a QOC of 8.8/10, while CHGG trades at $1.50 with a QOC of 5.9/10.

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).