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Shanghai Bolex Food Technology Co (603170) Fair Value & Analysis

Consumer Defensive · CN · Market cap 4.9B CNY

Price¥11.85
Fair Value¥13.79
Upside+16.4%
Quality82/100
Evidence: Medium Range ¥9.59 – ¥17.23

Analysis

Shanghai Bolex Food Technology Co (603170) currently trades at ¥11.85, while our model-based Fair Value estimate is ¥13.79 — implying the stock looks roughly 16.4% undervalued today. We read business quality at 82/100 (high quality), in the Consumer Defensive sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.

About the company

Shanghai Bolex Food Technology Co., Ltd. engages in the research, development, production, and sales of condiments and pre-cooked food in China and internationally. Its products portfolio includes compound seasonings, light cooking sauce packets, light cooking soup packets, and other light cooking solutions, as well as beverage and dessert ingredients comprising jams, popping beads, crystal balls, and tapioca pearls. The company offers its products under various categories, such as breading, bread crumbs, marinades, dusting powder, seasoning sauces, salad dressings, jams, seasoning packets, canned fruits and vegetables, baking premixes, and ready-to-drink beverages. It also provides technical services related to food seasonings. The company serves catering companies, the food industry, and household consumers. The company was founded in 2001 and is headquartered in Shanghai, 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.