PSIG vs SLGB

PS International Group Ltd. vs Smart Logistics Global Limited — Valuation Comparison 2026

PSIG

Arrangement of Transportation of Freight & Cargo
PS International Group Ltd.
Quality
2.1
out of 10
Value Trap
Price
$8.30
Last close
Models
13/13
Active
VS

SLGB

Arrangement of Transportation of Freight & Cargo
Smart Logistics Global Limited
Quality
5.6
out of 10
Value Trap
Price
$0.48
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType PSIG Fair ValuePSIG Upside SLGB Fair ValueSLGB Upside
Bayesian DCF Intrinsic $1.55 -81.3% $0.06 -88.2%
Earnings Power Value Intrinsic $0.34 -94.6% $0.29 -50.3%
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 $•••.•• ••.•% $•••.•• ••.•%
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PSIG vs SLGB — Which Stock Is More Undervalued?

SLGB scores higher with a 5.6/10 quality rating vs PSIG's 2.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing PS International Group Ltd. (PSIG) and Smart Logistics Global Limited (SLGB) 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.

PSIG currently trades at $8.30 with a QOC of 2.1/10, while SLGB trades at $0.48 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).