Dolby Laboratories, Inc (DLB) Fair Value & Analysis
Industrials · US · Market cap $5.1B
Analysis
Dolby Laboratories, Inc (DLB) currently trades at $52.11, while our model-based Fair Value estimate is $84.52 — implying the stock looks roughly 62.2% undervalued today. We read business quality at 95/100 (high quality), in the Industrials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Dolby Laboratories, Inc. engages in the design and manufacture of audio, imaging, accessibility, and other hardware and software solutions for television, broadcast, and live entertainment industries in the United States and internationally. The company develops and licenses its audio technologies, such as AAC, HE-AAC, and extended HE-AAC, a digital audio codec solution; AVC, a digital video codec used in STBs, mobile devices, cameras, and broadcast television services and other products; and Dolby Atmos and Dolby Vision include encoding technologies that artists use to create more compelling and immersive audio and video experiences. It also provides DD+, an advanced surround sound audio codec technology; Dolby AC-4, an audio codec that uses cutting edge compression; and HEVC, a digital video codec that compresses video. In addition, the company offers Dolby Cinemas, a premium large format cinemas that deliver a Dolby branded premium cinema offering with Dolby Vision, Dolby Atmos, …
Open the full interactive analysis →
Similar stocks
How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.