MTEK vs SNT

Maris-Tech Ltd. vs Senstar Technologies Corporatio — Valuation Comparison 2026

MTEK

Communications Equipment, NEC
Maris-Tech Ltd.
Quality
2.3
out of 10
Value Trap
6
SAFE
Price
$1.24
Last close
Models
11/13
Active
VS

SNT

Communications Equipment, NEC
Senstar Technologies Corporatio
Quality
2.6
out of 10
Value Trap
Price
$2.77
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType MTEK Fair ValueMTEK Upside SNT Fair ValueSNT Upside
Bayesian DCF Intrinsic $0.34 -73.0% $0.53 -80.9%
Earnings Power Value Intrinsic $1.25 -2.2% $2.15 -21.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 MTEK vs SNT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MTEK vs SNT — Which Stock Is More Undervalued?

SNT scores higher with a 2.6/10 quality rating vs MTEK's 2.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Maris-Tech Ltd. (MTEK) and Senstar Technologies Corporatio (SNT) 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.

MTEK currently trades at $1.24 with a QOC of 2.3/10, while SNT trades at $2.77 with a QOC of 2.6/10.

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).