Keysino Separation Technology Inc (300899) Fair Value & Analysis
Industrials · CN · Market cap 1.9B CNY
Fair value as of: Jun 24, 2026
Analysis
Keysino Separation Technology Inc (300899) currently trades at ¥31.04, while our model-based Fair Value estimate is ¥7.19 — implying the stock looks roughly 76.8% 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
Keysino Separation Technology Inc. engages in the research, development, and application of membrane separation technology in China and internationally. It offers wastewater solutions, including industrial wastewater standard discharge and reuse, salt control discharge, high salinity wastewater zero liquid discharge, and wastewater plant upgrading; feedwater solutions comprising softened, demineralized, ultrapure water, and sea water desalination; and material separations for vitamin, antibiotic, plant extractions, dairy products, organic acid, additives, amino acid, and sucrose and molasses. The company also provides industrial waste alkali and acid reuse, oiling agent, indigo dye, mother solution, protein, titanium dioxide, nonferrous metal, and catalyst reuse; high brine salt refining; mercerizing alkali purification; hemicellulose, lignin resource, and acidic water-oriented utilization; high brine fine pre-treatment; and PTA recovery. In addition, the company offers KS customize…
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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.