GSUN vs IH

Golden Sun Technology Group Lim vs iHuman Inc. — Valuation Comparison 2026

GSUN

Education & Training Services
Golden Sun Technology Group Lim
Quality
2.1
out of 10
Value Trap
Price
$0.42
Last close
Models
9/13
Active
VS

IH

Education & Training Services
iHuman Inc.
Quality
9.4
out of 10
Value Trap
6
SAFE
Price
$1.60
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType GSUN Fair ValueGSUN Upside IH Fair ValueIH Upside
Bayesian DCF Intrinsic $0.08 -80.2% $6.35 +296.9%
Earnings Power Value Intrinsic $6.28 +292.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $0.72 +75.2% $2.51 +56.6%
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 GSUN vs IH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GSUN vs IH — Which Stock Is More Undervalued?

IH scores higher with a 9.4/10 quality rating vs GSUN's 2.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Golden Sun Technology Group Lim (GSUN) and iHuman Inc. (IH) 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.

GSUN currently trades at $0.42 with a QOC of 2.1/10, while IH trades at $1.60 with a QOC of 9.4/10.

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).