Hi Sharp Intelligence Technology Co (3128) Fair Value & Analysis
Industrials · TW · Market cap 1.1B TWD
Fair value as of: Jun 24, 2026
Analysis
Hi Sharp Intelligence Technology Co (3128) currently trades at 24.30 TWD, while our model-based Fair Value estimate is 21.10 TWD — implying the stock looks roughly 13.2% overvalued today. We read business quality at 94/100 (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
Hi Sharp Intelligence Technology Co.,Ltd. develops, manufactures, sells, and installs intelligence surveillance products in Taiwan. The company offers camera and network video recorder (NVR)/DVR compliant products; analog products, including hybrid cameras, full color series, value-plus analog cameras, and hybrid DVR; network products, such as Taiwan chipset IPCs, face detection IPCs, value-plus IP cameras, PTZ cameras, 5M thermal camera, access controllers, and NVR products. The company also provides accessories, such as PoE switches, connectors, surveillance MIC, and video encoder, and other products. It provides solutions, which includes facial recognition, people flow management, vehicle applications, parking management surveillance, access control management, SVR, worksite access control, fire detection, railway station surveillance, occupational safety monitoring, and parking lot monitoring. The company was formerly known as Hi Sharp Electronics Co., Ltd. and changed its name …
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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.