Johnson Health Tech .Co., Ltd (1736) Fair Value & Analysis
Consumer Cyclical · TW · Market cap 35.2B TWD
Analysis
Johnson Health Tech .Co., Ltd (1736) currently trades at 117.50 TWD, while our model-based Fair Value estimate is 159.96 TWD — implying the stock looks roughly 36.1% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Johnson Health Tech .Co., Ltd., together with its subsidiaries, manufacturers and sells fitness equipment in the Americas, Europe, Asia, and internationally. The company offers cardiopulmonary resuscitation fitness machines, weight training machines, electric massage chairs and related motors, and instruments and electronic control panels, as well as instruments and electrical chairs, and related motors. It is also involved in buying and selling bodybuilding and weight training machines, and hearing aid; research and development, manufacturing, and sales of massage chair and sports equipment; provision of video streaming and transmission, and catering services; food wholesale; leasing business; and cultural communication activities. The company sells its products under the MATRIX, Vision Fitness, BowFlex, Schwinn, and Horizon brands. Johnson Health Tech .Co., Ltd. was incorporated in 1975 and is headquartered 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.