SYNX vs TSAT

Silynxcom Ltd. vs Telesat Corporation — Valuation Comparison 2026

SYNX

Communication Equipment
Silynxcom Ltd.
Quality
2.0
out of 10
Value Trap
6
SAFE
Price
$1.19
Last close
Models
11/13
Active
VS

TSAT

Communication Equipment
Telesat Corporation
Quality
1.7
out of 10
Value Trap
Price
$58.51
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType SYNX Fair ValueSYNX Upside TSAT Fair ValueTSAT Upside
Bayesian DCF Intrinsic $0.16 -86.4% $17.27 -70.5%
Earnings Power Value Intrinsic $2.46 +93.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $1.06 -6.4% $44.34 -14.5%
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 SYNX vs TSAT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SYNX vs TSAT — Which Stock Is More Undervalued?

SYNX scores higher with a 2.0/10 quality rating vs TSAT's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Silynxcom Ltd. (SYNX) and Telesat Corporation (TSAT) 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.

SYNX currently trades at $1.19 with a QOC of 2.0/10, while TSAT trades at $58.51 with a QOC of 1.7/10.

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).