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Tangrenshen Group (002567) Fair Value & Analysis

Consumer Defensive · CN · Market cap 4.9B CNY

Price¥3.25
Fair Value¥4.34
Upside+33.5%
Quality88/100
Evidence: Medium Range ¥2.89 – ¥5.78

Fair value as of: Jun 25, 2026

Analysis

Tangrenshen Group (002567) currently trades at ¥3.25, while our model-based Fair Value estimate is ¥4.34 — implying the stock looks roughly 33.5% undervalued today. We read business quality at 88/100 (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

Tangrenshen Group Co., Ltd engages in the research and development, production and sales of feed, live pigs, meat, and animal health products in China. The company sells its products under the Tangrenshen, Meishen, and Camel brand names. Tangrenshen Group Co., Ltd was founded in 1985 and is headquartered in Zhuzhou, China.

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Frequently asked questions

Is Tangrenshen Group (002567) undervalued?
As of Jun 25, 2026, our model estimates a fair value of ¥4.34 versus a price of ¥3.25 — about +34% (undervalued). Model-based estimate, not financial advice.
What is the fair value of 002567?
Our 21-model fair value for Tangrenshen Group is ¥4.34 (as of Jun 25, 2026), built from audited fundamentals. The current price is ¥3.25.
What is the quality score of 002567?
Tangrenshen Group has a Quality Score of 88/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.