JRSH vs LANV

Jerash Holdings (US), Inc. vs Lanvin Group Holdings Limited — Valuation Comparison 2026

JRSH

Apparel & Other Finishd Prods of Fabrics & Similar Matl
Jerash Holdings (US), Inc.
Quality
7.2
out of 10
Value Trap
18
SAFE
Price
$3.40
Last close
Models
13/13
Active
VS

LANV

Apparel & Other Finishd Prods of Fabrics & Similar Matl
Lanvin Group Holdings Limited
Quality
4.7
out of 10
Value Trap
18
SAFE
Price
$1.50
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType JRSH Fair ValueJRSH Upside LANV Fair ValueLANV Upside
Bayesian DCF Intrinsic $4.25 +25.1% $0.60 -61.7%
Earnings Power Value Intrinsic $0.87 -74.3% $5.63 +254.0%
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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JRSH vs LANV — Which Stock Is More Undervalued?

JRSH scores higher with a 7.2/10 quality rating vs LANV's 4.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Jerash Holdings (US), Inc. (JRSH) and Lanvin Group Holdings Limited (LANV) 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.

JRSH currently trades at $3.40 with a QOC of 7.2/10, while LANV trades at $1.50 with a QOC of 4.7/10.

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).