Jiangsu Tongli Tianqi Technology Co (605286) Fair Value & Analysis
Industrials · CN · Market cap 4.9B CNY
Analysis
Jiangsu Tongli Tianqi Technology Co (605286) currently trades at ¥28.72, while our model-based Fair Value estimate is ¥20.11 — implying the stock looks roughly 30.0% overvalued today. We read business quality at 81/100 (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: medium).
About the company
Jiangsu Tongli Tianqi Technology Co., Ltd. researches, develops, produces, and sells elevator components in China and internationally. The company provides escalator components, straight elevator components and elevator metal materials. It also offers skirts, covers, railings, handrail swivels, drive assemblies, handrail guides, and ladder guides for escalators; guide rail brackets, tractor brackets, counterweight frames, protective screen components, buffer brackets, car upper/lower beams, straight beams, car roof/bottom, car bottom brackets, and car walls for straight ladders; and handrails, siding boards, cold-formed profiles, public transport type cover, and stents. In addition, it is involved in the research, development, production and sales of electrochemical energy storage container systems; new energy power plant business; and component research and development. The company was formerly known as Jiangsu Tongli Risheng Machinery Co., Ltd. and change its name to Jiangsu Tongl…
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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.