P.C.S. Machine Group (PCSGH) Fair Value & Analysis
Consumer Cyclical · TH · Market cap 4.3B THB
Fair value as of: Jun 25, 2026
Analysis
P.C.S. Machine Group (PCSGH) currently trades at 3.00 THB, while our model-based Fair Value estimate is 7.82 THB — implying the stock looks roughly 160.7% 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
P.C.S. Machine Group Holding Public Company Limited manufactures and distributes automotive parts for original equipment manufacturers (OEMs) of the automotive industry in Asia, Europe, and internationally. It offers forged steel parts, aluminum forging parts, and molds; metal processing and transformation, and metal products; engine parts, transmission systems, brake systems, suspension systems, and metal parts for vehicles; metal parts for electronic products, including aluminum die-casting parts; tools and equipment, such as leak-test and induction hardening units; powertrain and chassis components, including heat treatment, gear making, assembly, cleaning and inspection to OEM specifications; and test and inspection equipment to validate material condition and critical product characteristics. The company's products are used for passenger cars, pickup trucks, trucks, and agricultural machinery. P.C.S. Machine Group Holding Public Company Limited was incorporated in 2013 and is h…
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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.