GeoVision Inc (3356) Fair Value & Analysis
Industrials · TW · Market cap 4.6B TWD
Fair value as of: Jun 24, 2026
Analysis
GeoVision Inc (3356) currently trades at 61.50 TWD, while our model-based Fair Value estimate is 31.62 TWD — implying the stock looks roughly 48.6% 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
GeoVision Inc., together with its subsidiaries, engages in conducting research, development, sales, and after-sales service of remote digital monitoring systems internationally. The company offers IP cameras; HD analog, standalone NVR, and HD DVR products; access control products, such as software, IP control panels and kits, controllers, readers, and accessories; license plate recognition products; video management, including VMS, NVR, recording server, edge recording manager, large scale monitoring, integration, backup management, video analytics, and mobile server; AI and cloud solutions; and surveillance systems. It also provides standalone and decoder products; PoE solutions; and IP speakers and IO boxes. The company serves the retail, commercial, government, transportation, hospitals, gaming, and residence industries through distributors, system integrators, and installers. GeoVision Inc. was founded in 1998 and is headquartered in Taipei, 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.