Jiangsu Kangliyuan Sports Tech. Co (301287) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 2.4B CNY
Fair value as of: Jun 24, 2026
Analysis
Jiangsu Kangliyuan Sports Tech. Co (301287) currently trades at ¥34.90, while our model-based Fair Value estimate is ¥22.92 — implying the stock looks roughly 34.3% overvalued today. We read business quality at 94/100 (high quality), in the Consumer Cyclical 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
Jiangsu Kangliyuan Sports Tech. Co., Ltd. manufactures and sells sporting goods and fitness equipment in China. It offers aerobic fitness equipment, such as treadmills, exercise bikes, magnetic cars, elliptical machines, and rowing machines; strength fitness equipment, including machines, smith trainers, gantries, single parallel bars, and weightlifting beds; and vertical squat trainers, triceps training devices, iso-stretch back and swivel training devices, low pull trainers, horizontal leg bend trainers, leg extension trainers, kick trainers, hips compound training devices, thigh extension training devices, standing calf trainers, butterfly machines, straight arm clip chest training devices, seated chest push trainers, shoulder press training devices, and push shoulder chairs. The company also provides T-shaped rowing trainers, dumbbell flat benches, adjustable abdominal muscle plates and dumbbell chairs, stretching machines, double dumbbell racks, adjustable roman stools, incline…
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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.