Shandong Jinjing Science & Technology Stock Co (600586) Fair Value & Analysis
Basic Materials · CN · Market cap 6.5B CNY
Fair value as of: Jun 24, 2026
Analysis
Shandong Jinjing Science & Technology Stock Co (600586) currently trades at ¥4.60, while our model-based Fair Value estimate is ¥1.95 — implying the stock looks roughly 57.6% overvalued today. We read business quality at 95/100 (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
Shandong Jinjing Science & Technology Stock Co.,Ltd, together with its subsidiaries, engages in the development, production, fabrication, and operation of glass, soda ash, and its derivatives in China and internationally. It operates through Glass Plate and Soda Ash Plate segments. The company offers ultra white glass, off-line coated glass, online coated glass, sun film glass, color glass, fire glass, automotive glass, low salt and heavy glass soda, light soda, and baking soda, as well as baking soda detergent. It is also involved in the photovoltaic glass business and sandstone businesses; and mining, commerce, and investment activities. The company serves the construction, automotive, solar, industrial products, and other market segments. It exports its products to Europe, America, Japan, South Korea, Southeast Asia, Australia, and the Middle East. Shandong Jinjing Science & Technology Stock Co.,Ltd was founded in 1999 and is based in Zibo, 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.