Neurones S.A (NRO) Fair Value & Analysis
Technology · FR · Market cap €932M
Analysis
Neurones S.A (NRO) currently trades at €35.20, while our model-based Fair Value estimate is €51.83 — implying the stock looks roughly 47.2% undervalued today. We read business quality at 82/100 (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
Neurones S.A. provides infrastructure, application, and consulting services in France. The company offers cloud, infrastructure management, DevOps, cybersecurity, user support, IT service management, information system governance, and automation services. It also provides digital, UX/UI, agile, devOps, mobility, and data; SAP integration and outsourcing content management, business process management, and paperless processes; IT consulting for the finance, big data, and AI and blockchain core businesses; and IT training and working with change. In addition, the company offers strategy consulting, management and digital transformation consulting, digital marketing consulting, and RH transformation consulting services. It serves banking/insurance, services/consumer goods, energy/utilities/healthcare, technology/media/telecoms, industry/construction and civil engineering, and public sector markets. Neurones S.A. was founded in 1984 and is headquartered in Nanterre, France.
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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.