SGL Carbon SE (SGL) Fair Value & Analysis
Basic Materials · DE · Market cap €657M
Analysis
SGL Carbon SE (SGL) currently trades at €4.78, while our model-based Fair Value estimate is €3.25 — implying the stock looks roughly 31.9% overvalued today. We read business quality at 94/100 (high quality), in the Basic Materials 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
SGL Carbon SE, together with its subsidiaries, manufactures and sells special graphite, carbon fiber, and composite products in Germany, rest of Europe, the United States, China, rest of Asia, and internationally. The company operates through four segments: Graphite Solutions, Process Technology, Carbon Fibers, and Composite Solutions. It offers products for automotive industries, including body and main part powertrains, motor blocks, and brake systems; carbon-ceramic brake discs; friction materials; gas diffusion layers and bipolar plates; carbon-ceramic brake discs; body shell components; friction materials; chassis components; gas diffusion layers and bipolar plates; and sealing materials. The company provides composite solutions, such as energy storage systems, lead springs, skins and structures, thermoplastic profiles, and friction components; fuel cells for energy conversion, as well as for passenger cars and trains, and ferries for zero emission mobile applications; and carb…
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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.