DLB vs IDCC

Dolby Laboratories vs InterDigital, Inc. — Valuation Comparison 2026

DLB

Patent Owners & Lessors
Dolby Laboratories
Quality
9.0
out of 10
Value Trap
18
SAFE
Price
$55.81
Last close
Models
13/13
Active
VS

IDCC

Patent Owners & Lessors
InterDigital, Inc.
Quality
10.0
out of 10
Value Trap
18
SAFE
Price
$252.09
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType DLB Fair ValueDLB Upside IDCC Fair ValueIDCC Upside
Bayesian DCF Intrinsic $53.91 -3.4% $318.22 +26.2%
Earnings Power Value Intrinsic $36.97 -33.8% $188.81 -25.1%
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 DLB vs IDCC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

DLB vs IDCC — Which Stock Is More Undervalued?

IDCC scores higher with a 10.0/10 quality rating vs DLB's 9.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Dolby Laboratories (DLB) and InterDigital, Inc. (IDCC) 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.

DLB currently trades at $55.81 with a QOC of 9.0/10, while IDCC trades at $252.09 with a QOC of 10.0/10.

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).