AAPL vs FOXX

Apple Inc. vs Foxx Development Holdings Inc. — Valuation Comparison 2026

AAPL

Consumer Electronics
Apple Inc.
Quality
10.0
out of 10
Value Trap
Price
$312.51
Last close
Models
12/13
Active
VS

FOXX

Consumer Electronics
Foxx Development Holdings Inc.
Quality
5.5
out of 10
Value Trap
12
SAFE
Price
$2.76
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType AAPL Fair ValueAAPL Upside FOXX Fair ValueFOXX Upside
Bayesian DCF Intrinsic $133.26 -57.4% $0.62 -77.6%
Earnings Power Value Intrinsic $78.29 -74.9% $2.04 -58.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 AAPL vs FOXX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AAPL vs FOXX — Which Stock Is More Undervalued?

AAPL scores higher with a 10.0/10 quality rating vs FOXX's 5.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Apple Inc. (AAPL) and Foxx Development Holdings Inc. (FOXX) 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.

AAPL currently trades at $312.51 with a QOC of 10.0/10, while FOXX trades at $2.76 with a QOC of 5.5/10.

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).