Liaoning SG Automotive Group (600303) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 2.1B CNY
Fair value as of: Jun 25, 2026
Analysis
Liaoning SG Automotive Group (600303) currently trades at ¥2.69, while our model-based Fair Value estimate is ¥0.9800 — implying the stock looks roughly 63.6% overvalued today. We read business quality at 94/100 (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: low).
About the company
Liaoning SG Automotive Group Co., Ltd. manufactures and sells automobiles, and axles and other auto parts in China. It offers pickup vehicles; mini buses; electric buses; special pickup vehicles, including sewer suction pickups, refrigerated pickups, blasting equipment transporter, police pickups, pickup cranes, and pickup wrecker; sanitation vehicles, such as self-loading garbage trucks, sewer flushing/suction vehicles, snow sweepers, and garbage compression trucks; cargo vehicles, including wingspan trailers, cargo tank containers, fuel transportation trailers, and cargo trailers; and modified vehicles, such as recreational vehicles, vending vans, washing vehicles, and machine shop trucks, as well as types of axles. It also provides cargo warehousing services, import and export of goods and technology, heating services, and inspection and testing services. In addition, the company invests in real estate projects. The company offers its vehicles under the Huanghai brand and axles u…
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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.