Knowit AB (KNOW) Fair Value & Analysis
Technology · SE · Market cap 2.4B SEK
Fair value as of: Jun 25, 2026
Analysis
Knowit AB (KNOW) currently trades at kr 78.50, while our model-based Fair Value estimate is kr 106.93 — implying the stock looks roughly 36.2% undervalued today. We read business quality at 95/100 (high quality), in the Technology 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: medium) — always confirm before acting.
About the company
Knowit AB (publ) operates as a consultancy company in Sweden and internationally. It operates through five segments: Solutions, Experience, Connectivity, Insight, and Other. The company offers management consulting, such as profitability programs and management; commercial excellence and pricing strategy; insight and trend analysis; market, category, and offer strategy; working capital and supply chain management; finance transformation; sourcing and procurement; digital hr and payroll; digital and agile transformation; data analytics; and organization and leadership solutions. It also provides IT and digital product development including web development, mobile and app development, cloud, AI and data, internet of things, networks and data centers, embedded systems, application management, DevOps and CI/CD automation, systems integration, and test automation. In addition, the company offers cloud strategy, cloud foundation, migration and modernization, build in the cloud, and manage…
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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.