LFWD vs RGNT

Lifeward Ltd. vs Regentis Biomaterials Ltd. — Valuation Comparison 2026

LFWD

Orthopedic, Prosthetic & Surgical Appliances & Supplies
Lifeward Ltd.
Quality
5.2
out of 10
Value Trap
52
WARN
Price
$7.94
Last close
Models
11/13
Active
VS

RGNT

Orthopedic, Prosthetic & Surgical Appliances & Supplies
Regentis Biomaterials Ltd.
Quality
3.6
out of 10
Value Trap
Price
$1.85
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType LFWD Fair ValueLFWD Upside RGNT Fair ValueRGNT Upside
Bayesian DCF Intrinsic $1.25 -84.2% $2.06 -19.4%
Earnings Power Value Intrinsic $1.48 -79.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $11.67 +47.0% $0.32 -89.0%
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 LFWD vs RGNT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LFWD vs RGNT — Which Stock Is More Undervalued?

LFWD scores higher with a 5.2/10 quality rating vs RGNT's 3.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Lifeward Ltd. (LFWD) and Regentis Biomaterials Ltd. (RGNT) 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.

LFWD currently trades at $7.94 with a QOC of 5.2/10, while RGNT trades at $1.85 with a QOC of 3.6/10.

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).