Making Science Group (MAKS) Fair Value & Analysis
Communication Services · ES · Market cap €70.0M
Fair value as of: Jun 25, 2026
Analysis
Making Science Group (MAKS) currently trades at €7.05, while our model-based Fair Value estimate is €7.39 — implying the stock looks roughly 4.8% undervalued today. We read business quality at 90/100 (high quality), in the Communication Services 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
Making Science Group, S.A., together with its subsidiaries, provides technology consulting and e-commerce and digital transformation marketing services worldwide. The company provides its services to areas of data, analytics, and adtech, digital advertising, software and cloud, salesforce, and eCommerce and marketplaces. It offers Ad-machina, a technology that allows companies advertise their products and services through Google Ads; Nilo, a cloud-based e-commerce platform; Gauss, a platform that offers a suite of solutions that creates a data-driven ecosystem; and Shoptize, a price comparator that allows end users to compare prices of various products. The company also provides custom software development, digital product design, data and analytics, advertising, brand and content strategy, programmatic and paid search, search engine optimization, conversion rate optimization, CRM and marketing automation, network resources, data storage, software administration services, and techni…
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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.