CPOP vs FTRK

Pop Culture Group Co., Ltd vs FAST TRACK GROUP — Valuation Comparison 2026

CPOP

Entertainment
Pop Culture Group Co., Ltd
Quality
1.7
out of 10
Value Trap
Price
$0.29
Last close
Models
12/13
Active
VS

FTRK

Entertainment
FAST TRACK GROUP
Quality
5.6
out of 10
Value Trap
Price
$0.51
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CPOP Fair ValueCPOP Upside FTRK Fair ValueFTRK Upside
Bayesian DCF Intrinsic $0.06 -80.2% $0.11 -77.7%
Earnings Power Value Intrinsic $0.02 -93.5% $0.24 -34.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 CPOP vs FTRK — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CPOP vs FTRK — Which Stock Is More Undervalued?

FTRK scores higher with a 5.6/10 quality rating vs CPOP's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Pop Culture Group Co., Ltd (CPOP) and FAST TRACK GROUP (FTRK) 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.

CPOP currently trades at $0.29 with a QOC of 1.7/10, while FTRK trades at $0.51 with a QOC of 5.6/10.

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).