JILL vs ROST

J. Jill, Inc. vs Ross Stores, Inc. — Valuation Comparison 2026

JILL

Apparel Retail
J. Jill, Inc.
Quality
8.5
out of 10
Value Trap
20
SAFE
Price
$13.26
Last close
Models
10/13
Active
VS

ROST

Apparel Retail
Ross Stores, Inc.
Quality
8.9
out of 10
Value Trap
6
SAFE
Price
$227.20
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType JILL Fair ValueJILL Upside ROST Fair ValueROST Upside
Bayesian DCF Intrinsic $24.98 +88.4% $64.49 -71.6%
Earnings Power Value Intrinsic $21.19 +59.8% $42.67 -81.2%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for JILL vs ROST — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

JILL vs ROST — Which Stock Is More Undervalued?

ROST scores higher with a 8.9/10 quality rating vs JILL's 8.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing J. Jill, Inc. (JILL) and Ross Stores, Inc. (ROST) 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.

JILL currently trades at $13.26 with a QOC of 8.5/10, while ROST trades at $227.20 with a QOC of 8.9/10.

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).