Macfarlane Group (MACF) Fair Value & Analysis
Consumer Cyclical · GB · Market cap 101M GBX
Fair value as of: Jun 24, 2026
Analysis
Macfarlane Group (MACF) currently trades at p0.6600, while our model-based Fair Value estimate is p0.8500 — implying the stock looks roughly 28.8% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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
Macfarlane Group PLC, through its subsidiaries, designs, manufactures, and distributes protective packaging products to businesses in the United Kingdom and Europe. It operates through Packaging Distribution and Manufacturing Operations segments. The Packaging Distribution segment distributes packaging materials in the United Kingdom, Ireland, and Europe. The Manufacturing Operations segment designs, manufactures, and assembles timber, corrugated, and foam-based packaging materials in the United Kingdom. The company also recovers waste paper and corrugated boards for recycling. In addition, it engages in the supply and distribution of equipment for packaging and specialist packaging materials. The company serves e-commerce retail, logistics, electronics, aerospace, automotive, medical, homeware, general industrial, food, and hospitality sectors. Macfarlane Group PLC was incorporated in 1899 and is headquartered in Glasgow, the United Kingdom.
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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.