CL Workshop Group (NWGL) Fair Value & Analysis
Basic Materials · US · Market cap $4.1M
Fair value as of: Jun 25, 2026
Analysis
CL Workshop Group (NWGL) currently trades at $0.2200, while our model-based Fair Value estimate is $0.1700 — implying the stock looks roughly 22.7% 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
CL Workshop Group Limited, an integrated forestry company, engages in management and harvesting, and down-stream wood-processing and distribution activities in Europe, Africa, Asia, South America, North America, and China. It operates through two segments, Direct Purchase and Original Design Manufacturer Services; and Manufacturing segments. The company offers logs, decking and flooring, sawn timber, recycled and synthesized charcoal, machine-made charcoal, and essential oil products. It also provides IT and business consultancy services. In addition, it engages in trading carbon credits. The company serves importers, retailers, and processors through its sales network. The company was formerly known as Nature Wood Group Limited and changed its name to CL Workshop Group Limited in December 2025. The company was incorporated in 2011 and is headquartered in Macau. As of October 22, 2025, CL Workshop Group Limited operates as a subsidiary of TUTU Business Services Limited.
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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.