TJK Intelligent Equipment Manufacturing (Tianjin) Co (300823) Fair Value & Analysis
Industrials · CN · Market cap 2.1B CNY
Fair value as of: Jun 24, 2026
Analysis
TJK Intelligent Equipment Manufacturing (Tianjin) Co (300823) currently trades at ¥15.68, while our model-based Fair Value estimate is ¥6.40 — implying the stock looks roughly 59.2% overvalued today. We read business quality at 92/100 (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
TJK Intelligent Equipment Manufacturing (Tianjin) Co., Ltd. engages in research and development, design, production, and sale of medium and high-end CNC steel bar processing equipment, supporting software, and provided intelligent CNC steel in China and internationally. The company provides excavation machinery, shovel conveyors machinery, hoisting machinery, industrial vehicles, compaction machinery, road construction and maintenance machinery, concrete machinery, excavation machinery, pile machinery, municipal and sanitation machinery, concrete products machinery, aerial work machinery, decoration machinery, steel reinforcement and prestressing machinery, rock drilling machinery, etc. TJK Intelligent Equipment Manufacturing (Tianjin) Co., Ltd. was formerly known as Tjk Machinery (Tianjin) Co., Ltd. and changed its name to TJK Intelligent Equipment Manufacturing (Tianjin) Co., Ltd. in June 2024. The company was founded in 2002 and is based in Tianjin, China.
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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.