HLIT vs INSG

Harmonic Inc. vs Inseego Corp. — Valuation Comparison 2026

HLIT

Communication Equipment
Harmonic Inc.
Quality
8.1
out of 10
Value Trap
22
SAFE
Price
$17.00
Last close
Models
13/13
Active
VS

INSG

Communication Equipment
Inseego Corp.
Quality
5.6
out of 10
Value Trap
28
LOW
Price
$13.15
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType HLIT Fair ValueHLIT Upside INSG Fair ValueINSG Upside
Bayesian DCF Intrinsic $5.90 -65.3% $3.61 -72.6%
Earnings Power Value Intrinsic $4.09 -75.9% $6.86 -47.9%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

HLIT vs INSG — Which Stock Is More Undervalued?

HLIT scores higher with a 8.1/10 quality rating vs INSG's 5.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Harmonic Inc. (HLIT) and Inseego Corp. (INSG) 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.

HLIT currently trades at $17.00 with a QOC of 8.1/10, while INSG trades at $13.15 with a QOC of 5.6/10.

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).