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Calix, Inc (CALX) Fair Value & Analysis

Technology · US · Market cap $2.4B

Price$36.36
Fair Value$6.12
Upside-83.2%
Quality95/100
Evidence: High Range $4.84 – $7.40

Analysis

Calix, Inc (CALX) currently trades at $36.36, while our model-based Fair Value estimate is $6.12 — implying the stock looks roughly 83.2% overvalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).

About the company

Calix, Inc., together with its subsidiaries, engages in the provision of cloud and software platforms, and systems and services in the United States, rest of Americas, Europe, the Middle East, Africa, and the Asia Pacific. The company cloud and software platforms, and systems and services enable broadband service providers (BSPs) to provide a range of services. It offers Calix Cloud platform comprising Calix Engagement Cloud, Calix Operations Cloud, and Calix Service Cloud, which are configurable to display role-based insights and enable BEPs to anticipate and target new revenue-generating services and applications through mobile application, such as CommandIQ for residents and CommandWorx for businesses; Calix Intelligent Access, an access network solution for automated and intelligent networks; and Calix Unlimited Subscriber, a solution for subscriber managed services. It also provides SmartLife managed services, including SmartHome managed services and applications to enhance, op…

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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.