FineMat Applied Materials Co (6698) Fair Value & Analysis
Technology · TW · Market cap 2.5B TWD
Fair value as of: Jun 24, 2026
Analysis
FineMat Applied Materials Co (6698) currently trades at 47.75 TWD, while our model-based Fair Value estimate is 49.36 TWD — implying the stock looks roughly 3.4% undervalued today. We read business quality at 80/100 (high quality), in the Technology 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: medium) — always confirm before acting.
About the company
FineMat Applied Materials Co., Ltd., together with its subsidiaries, develops, manufactures, and sells high precision metal masks and other metal products in Mainland China, Taiwan, and internationally. The company offers high-precision metal mask stretching for displays; flexible OLED display metal mask production and stretching; recycle and cleaning services for OLED and semiconductor components; and various precision-etched application products, as well as full-size precision frame manufacturing services. It also provides design consultation, mask etching, frame preparation, mesh spot welding, and cleaning and regeneration services. In addition, the company engages in professional investment activities; and the manufacture and sale of microwaves, semiconductor and electronic components, communication equipment and apparatus, and computers and its peripherals. It serves the optoelectronic, semiconductor, cooling/manifold plate, medical, and encoder disk industries. The company was…
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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.