CMTL vs GILT

Comtech Telecommunications Corp vs Gilat Satellite Networks Ltd. — Valuation Comparison 2026

CMTL

Radio & Tv Broadcasting & Communications Equipment
Comtech Telecommunications Corp
Quality
4.7
out of 10
Value Trap
12
SAFE
Price
$5.62
Last close
Models
11/13
Active
VS

GILT

Radio & Tv Broadcasting & Communications Equipment
Gilat Satellite Networks Ltd.
Quality
8.2
out of 10
Value Trap
11
SAFE
Price
$17.17
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CMTL Fair ValueCMTL Upside GILT Fair ValueGILT Upside
Bayesian DCF Intrinsic $7.20 +85.0% $1.03 -94.0%
Earnings Power Value Intrinsic $17.78 +216.4% $5.23 -69.6%
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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CMTL vs GILT — Which Stock Is More Undervalued?

GILT scores higher with a 8.2/10 quality rating vs CMTL's 4.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Comtech Telecommunications Corp (CMTL) and Gilat Satellite Networks Ltd. (GILT) 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.

CMTL currently trades at $5.62 with a QOC of 4.7/10, while GILT trades at $17.17 with a QOC of 8.2/10.

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).