Groundhog Inc (6906) Fair Value & Analysis
Technology · TW · Market cap 3.0B TWD
Fair value as of: Jun 24, 2026
Analysis
Groundhog Inc (6906) currently trades at 88.80 TWD, while our model-based Fair Value estimate is 53.19 TWD — implying the stock looks roughly 40.1% 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
Groundhog Inc. engages in application design, development, implementation, and installation of other information software services on intelligence platform in Taiwan, the Kingdom of Saudi Arabia, Indonesia, Africa, India, Australia, and internationally. The company offers CovMo, a cloud-based platform for geo-location; RealMotion, a crowd management solution that provides current and historical mobility insights into near-real-time human movement, empowering tourism, transport, urban planning professionals, as well as government authorities and public safety agencies; MI-DSP, an AI advertising platform; MI-DMP, a market intelligence platform; and DDKT, an audience analytics platform. It also provides mobile network technologies; algorithms and software system development; data mining; network optimization; big data and RAN analytics; mobility and mathematical modeling; time series analysis; cloud computing; customized research. The company was founded in 2001 and is based in Taipei,…
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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.