Shanghai Highly (Group) Co (600619) Fair Value & Analysis
Industrials · CN · Market cap 16.2B CNY
Analysis
Shanghai Highly (Group) Co (600619) currently trades at ¥14.58, while our model-based Fair Value estimate is ¥10.69 — implying the stock looks roughly 26.7% overvalued today. We read business quality at 84/100 (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
Shanghai Highly (Group) Co., Ltd., together with its subsidiaries, researches, develops, manufactures, and sells heat and cooling components and automotive parts in China and internationally. It operates through three segments: Compressor and Related Refrigeration Equipment; Automotive Parts; and Trade and Real Estate Leasing. The company offers compressors for household AC, light commercial AC, and home appliances with heating and cooling functions; AC and refrigerator compressors; refrigeration castings and forgings for internal compressor castings and forgings; equipment thermal management for high temperature equipment and environment; freezer and cold storage solutions for supermarkets and cold chain warehouses; and heat pump systems for indoor and building heating. It also provides automotive AC and energy vehicle thermal management systems for fuel and energy vehicles; energy vehicle AC electric compressors for energy passenger cars, commercial vehicles, and buses; vehicle an…
Open the full interactive analysis →
Similar stocks
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.