Grade Upon Technology Corp (6739) Fair Value & Analysis
Technology · TW · Market cap 27.2B TWD
Analysis
Grade Upon Technology Corp (6739) currently trades at 1,120 TWD, while our model-based Fair Value estimate is 357.39 TWD — implying the stock looks roughly 68.1% overvalued today. We read business quality at 95/100 (high quality), in the Technology 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
Grade Upon Technology Corp engages in the research, design, development, manufacturing, sales, and leasing of intelligent automation systems and electronic components for high-tech industries in Taiwan, Singapore, Mainland China, and internationally. It provides customized integrated solutions, leveraging IoT sensors, AI-powered machine vision, machine hearing, and machine olfaction to enable equipment networking, data acquisition, and AI-based anomaly detection systems. The company also develops remote operations systems and intelligent collaborative control systems. Further, it offers green AI energy control system, green pipe energy saving system, NDU energy monitoring system, AI control solution, AI robot operation system, remote control system, AI camera, AI microphone, micro-pollution monitoring system VOC (ppb), AI vibration monitoring, equip param monitoring system, and big data analytics system. It serves semiconductors, packaging and testing, printed circuit board (PCB), a…
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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.