Datalogic S.p.A (DLGCF) Fair Value & Analysis
Technology · US · Market cap $342M
Fair value as of: Jun 24, 2026
Analysis
Datalogic S.p.A (DLGCF) currently trades at $6.90, while our model-based Fair Value estimate is $5.65 — implying the stock looks roughly 18.1% 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
Datalogic S.p.A. designs and produces barcode readers, mobile computers, detection, measurement and safety sensors, vision and laser marking systems, and RFID products in Italy, the Americas, the Asia Pacific, rest of Europe, the Middle East, and Africa. It offers fixed retail scanners, including single plane, multi-plane imaging, and presentation scanners; accessories, and general duty and industrial handheld scanners; mobile computers comprising vehicle mounted and handheld computers, and wearable scanners; laser marking systems; and related software and tools. The company also provides stationery industrial scanners, such as 1D reader scanners, 2D imager readers, linear cameras, dimensioners, connectivity products, and system integration solutions; OEM barcode readers; and data sensing products that include sensors, safety, and machine vision products. It serves the retail, manufacturing, transportation and logistics, and healthcare sectors, as well as offers products for data en…
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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.