KORE vs SATS

KORE Group Holdings, Inc. vs EchoStar Corporation — Valuation Comparison 2026

KORE

Communications Services, NEC
KORE Group Holdings, Inc.
Quality
5.2
out of 10
Value Trap
30
LOW
Price
$9.18
Last close
Models
9/13
Active
VS

SATS

Communications Services, NEC
EchoStar Corporation
Quality
5.7
out of 10
Value Trap
18
SAFE
Price
$129.19
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType KORE Fair ValueKORE Upside SATS Fair ValueSATS Upside
Bayesian DCF Intrinsic $1.90 -79.2% $21.36 -82.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $25.86 +181.7% $22.53 -83.7%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $9.83 +7.0% $329.99 +180.8%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for KORE vs SATS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

KORE vs SATS — Which Stock Is More Undervalued?

SATS scores higher with a 5.7/10 quality rating vs KORE's 5.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing KORE Group Holdings, Inc. (KORE) and EchoStar Corporation (SATS) 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.

KORE currently trades at $9.18 with a QOC of 5.2/10, while SATS trades at $129.19 with a QOC of 5.7/10.

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).