Ruitai Materials Technology Co (002066) Fair Value & Analysis
Basic Materials · CN · Market cap 5.2B CNY
Fair value as of: Jun 25, 2026
Analysis
Ruitai Materials Technology Co (002066) currently trades at ¥21.55, while our model-based Fair Value estimate is ¥4.22 — implying the stock looks roughly 80.4% overvalued today. We read business quality at 80/100 (high quality), in the Basic Materials 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
Ruitai Materials Technology Co., Ltd. engages in the research, production, sale, and service of refractories in China. The company provides fused cast, basic, alumina-silica, silica, and unshaped refractories. It also offers magnesia, forsterite, fireclay, and insulation bricks; magnesia-hercynite, magnesia-MA spinel, magnesia-alumina-zirconia-lanthana, and magnesia-chrome bricks; spalling resistant, phosphate bonded, and alkali resistant bricks; and corundum/mulite based abrasion resistant, andalusite containing abrasion and thermal shock resistant, abrasion resistant, anti-crust, medium density alkali resistant, low cement high alumina, alumina self-flowing and gunning, and steel fiber reinforced high alumina castables. In addition, the company provides ceramic cup, sialon bonded corundum brick, corundum composite brick, stemming, aluminum silicon carbide, dam/weir, impact plate, magnesia carbon brick, alumina magnesia carbon brick, magnesium spinel brick, and magnesia coating mix…
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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.