Ferroglobe PLC (GSM) Fair Value & Analysis
Basic Materials · US · Market cap $731M
Analysis
Ferroglobe PLC (GSM) currently trades at $3.58, while our model-based Fair Value estimate is $0.4700 — implying the stock looks roughly 86.9% overvalued today. We read business quality at 95/100 (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: low).
About the company
Ferroglobe PLC produces and sells silicon metal, and silicon and manganese-based alloys. It provides silicone metal that are used in a range of applications, including construction-related products, electronics, personal care items, and health care, as well as by primary and secondary aluminum producers. The company also offers silicomanganese, which is used as a deoxidizing agent in the steel manufacturing process; and ferromanganese that is used as a deoxidizing, desulphurizing, and degassing agent in the removal of nitrogen and other harmful elements from steel. In addition, it offers ferrosilicon products that are used to produce stainless steel, carbon steel, and various other steel alloys, as well as to manufacture electrodes and aluminum; calcium silicon for deoxidation and desulfurization of liquid steel, cast iron pipes coating production, and welding process of powder metal and in pyrotechnics, as well as control the shape, size, and distribution of oxide and sulfide inclu…
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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.