Tera Autotech Corporation (6234) Fair Value & Analysis
Industrials · TW · Market cap 4.3B TWD
Fair value as of: Jun 24, 2026
Analysis
Tera Autotech Corporation (6234) currently trades at 45.90 TWD, while our model-based Fair Value estimate is 33.02 TWD — implying the stock looks roughly 28.1% 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: medium).
About the company
Tera Autotech Corporation engages in the research and development, design, manufacture, sale, and import and export of industrial automation equipment in Taiwan and China. The company offers warehousing and conveying equipment, including cleanroom, normal environment, semiconductor, and peripheral equipment; and whole plant automated logistic equipment for LCD, solar, 3C, and PCB drill industries. It also provides automatic re-grind, product inspection, RGV, and lock screw machines; drill products, such as standard, micro, large, UC, and slot drills, as well as router bits; CNC machines, welding equipment, and chamber processing machines; and measurement equipment comprising CMM products, laser trackers, and laser interferon meters. The company was formerly known as Gauss Automation Corporation and changed its name to Tera Autotech Corporation in 1990. Tera Autotech Corporation was incorporated in 1979 and is based in Taichung, Taiwan.
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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.