Changzhou Tenglong AutoPartsCo.,Ltd. researches, (603158) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 6.3B CNY
Analysis
Changzhou Tenglong AutoPartsCo.,Ltd. researches, (603158) currently trades at ¥12.61, while our model-based Fair Value estimate is ¥7.87 — implying the stock looks roughly 37.6% overvalued today. We read business quality at 91/100 (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
Changzhou Tenglong AutoPartsCo.,Ltd. researches, develops, manufactures, and sells auto parts in China and internationally. The company offers air-conditioning pipelines, such as compressor suction and exhaust, expansion valve inlet and outlet, heater air conditioning, and condenser connecting pipe assemblies; automotive heat exchange connecting tubes, including heater and oil cooling inlet and outlet pipes, evaporator imported tubes, and inlet pipes; and automotive heat exchange systems comprising filling valves, connectors, pullovers, covers, flanges, brackets, and auto heat exchange accessories. It also provides automotive sensors consisting of diesel engine exhaust temperature, water temperature, and sunlight sensors; EGR cooling parts; auto precision aluminum tubes, such as flat tubes, intercooler tubes, afflux tubes, coaxial tubes, and KOMO pipes; and reservoir package in master cylinder for automobile brake systems. Changzhou Tenglong AutoPartsCo.,Ltd. was founded in 1997 and…
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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.