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Volex plc (VLX) Fair Value & Analysis

Industrials · GB · Market cap 1.2B GBX

Pricep5.86
Fair Valuep4.13
Upside-29.5%
Quality94/100
Evidence: High Range p2.13 – p6.27

Analysis

Volex plc (VLX) currently trades at p5.86, while our model-based Fair Value estimate is p4.13 — implying the stock looks roughly 29.5% overvalued today. We read business quality at 94/100 (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

Volex plc manufactures and sells power and connectivity in North America, Europe, and Asia. It provides integrated manufacturing services, such as PCB assembly, box build, and complex cable assemblies; electric vehicle charging solutions; consumer cable harness and power products; copper interconnect cables and data transfer cables; and data center power cables and power cords. The company also offers plugs, connectors, and receptacles, custom ruggedised wiring harnesses and battery cables, as well as Integrated Manufacturing Services, such as high level assembly, box builds, electromechanical assemblies and systems, printed circuit board assemblies, video display systems, and embedded systems. Its products are used in complex industrial technology, consumer electricals, electric vehicle, medical, and off-highway. The company sells its products to original equipment manufacturers, contract equipment manufacturers, and distributors. Volex plc was founded in 1892 and is headquartered …

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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.