LCUT vs RECT

Lifetime Brands, Inc. vs Rectitude Holdings Ltd — Valuation Comparison 2026

LCUT

Cutlery, Handtools & General Hardware
Lifetime Brands, Inc.
Quality
6.6
out of 10
Value Trap
28
LOW
Price
$8.55
Last close
Models
13/13
Active
VS

RECT

Cutlery, Handtools & General Hardware
Rectitude Holdings Ltd
Quality
8.7
out of 10
Value Trap
Price
$1.32
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType LCUT Fair ValueLCUT Upside RECT Fair ValueRECT Upside
Bayesian DCF Intrinsic $3.28 -61.7% $0.07 -95.0%
Earnings Power Value Intrinsic $9.78 +14.4% $0.61 -53.8%
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 LCUT vs RECT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LCUT vs RECT — Which Stock Is More Undervalued?

RECT scores higher with a 8.7/10 quality rating vs LCUT's 6.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Lifetime Brands, Inc. (LCUT) and Rectitude Holdings Ltd (RECT) 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.

LCUT currently trades at $8.55 with a QOC of 6.6/10, while RECT trades at $1.32 with a QOC of 8.7/10.

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).