Baiyang Investment Group (002696) Fair Value & Analysis
Consumer Defensive · CN · Market cap 1.9B CNY
Fair value as of: Jun 25, 2026
Analysis
Baiyang Investment Group (002696) currently trades at ¥5.37, while our model-based Fair Value estimate is ¥3.23 — implying the stock looks roughly 39.9% overvalued today. We read business quality at 91/100 (high quality), in the Consumer Defensive 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
Baiyang Investment Group, Inc., together with its subsidiaries, engages in the aquatic product processing and biological products, feed and feed raw materials, and deep-sea fishing businesses in China and internationally. It operates through Feed and Raw Materials, Food Processing, Ocean Fishing, and Biochemical Product segments. The company offers tilapia, shrimp, and grass carp products under the Beifeng and Qiaoyujia brands; aquatic feed products for fish, as well as groupers, California bass, yellow catfish, large yellow croakers, and frogs; and biological products, such as collagen and gelatin for use in health products, cosmetics, beverages, food, and other fields, as well as fish collagen peptide powder and glucosamine chondroitin calcium capsules. It is also involved in aquatic research and development; aquatic fry breeding; trading; offshore fishing; lease; and research and experimental development activities. The company exports its aquatic products to the United States, R…
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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.