Solar Applied Materials Technology Corporation (1785) Fair Value & Analysis
Industrials · TW · Market cap 81.4B TWD
Analysis
Solar Applied Materials Technology Corporation (1785) currently trades at 149.50 TWD, while our model-based Fair Value estimate is 66.44 TWD — implying the stock looks roughly 55.6% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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
Solar Applied Materials Technology Corporation manufactures, processes, recycles, refines, and trades sputtering for thin film materials, precious metal materials, and automotive chemicals in Taiwan, China, and internationally. It operates through: Taiwan Solar, Kunshan Solar, and Others segments. It offers precious metal chemicals, including gold, potassium, silver cyanide, Tetraammine dichloropalladium, silver potassium cyanide, silver cyanide, and silver nitrate and plate; and precious metal materials consisting of sterling gold and silver, as well as gold, silver, and platinum bullions. It also provides green certified products, such as potassium gold and silver cyanide, gold slug and kilo bar, and ITO-P and R target; precision machining parts for electrochemical deposition, dry etching, ion implanting, and physical vapor deposition; silver alloy and coated bonding wires for semiconductor IC and LED packaging; probe materials for wafer, packaging, PCB, and LED testing; recycling…
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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.