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M-Grass Ecology And Environment (Group) Co (300355) Fair Value & Analysis

Industrials · CN · Market cap 4.7B CNY

Price¥2.90
Fair Value¥0.3700
Upside-87.2%
Quality88/100
Evidence: Medium Range ¥0.2800 – ¥1.02

Analysis

M-Grass Ecology And Environment (Group) Co (300355) currently trades at ¥2.90, while our model-based Fair Value estimate is ¥0.3700 — implying the stock looks roughly 87.2% overvalued today. We read business quality at 88/100 (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

M-Grass Ecology And Environment (Group) Co., Ltd. engages in the seed industry technology and ecological restoration businesses. The company is involved in grassland restoration, mine and barren mountain/slope restoration, desert and sand land control, saline land improvement and soil rehabilitation, dump/wasteland rehabilitation, water- efficient landscaping and sponge cities, and sports turf planting activities. It also provides seeds for ecological restoration in indigenous plant seedlings, sports and landscaping turf planting, pasture grass seeds, and technological services. M-Grass Ecology And Environment (Group) Co., Ltd. was formerly known as Inner Mongolia M-Grass Ecology and changed its name to M-Grass Ecology And Environment (Group) Co., Ltd. in February 2022. The company was founded in 2001 and is based in Hohhot, 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.