Zalaris ASA (ZAL) Fair Value & Analysis
Industrials · NO · Market cap 2.2B NOK
Analysis
Zalaris ASA (ZAL) currently trades at kr 102.00, while our model-based Fair Value estimate is kr 58.35 — implying the stock looks roughly 42.8% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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
Zalaris ASA, together with its subsidiaries, provides full-service outsourced personnel and payroll services. It operates through two segments, Managed Services and Zalaris Consulting. The Managed Services segment offers a range of payroll and human resource (HR) outsourcing services, including payroll processing, time and attendance, travel expenses; and cloud-based HR functionality services, such as talent management, digital personnel archive, HR analytics, and mobile solutions, etc. Its Zalaris Consulting segment implements SAP HCM and Payroll, and SuccessFactors; and provides AMO services, which assists customers with maintenance and support of on-premises SAP solutions. The company also offers business processing outsourcing and consulting services. It operates in Norway, Sweden, Denmark, Finland, Spain, France, Ireland, the United Kingdom, Germany, Latvia, Poland, Hungary, Czechia, India, Singapore, the Philippines, Australia, and internationally. The company was formerly kno…
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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.