Shandong Weida Machinery Co (002026) Fair Value & Analysis
Industrials · CN · Market cap 5.3B CNY
Fair value as of: Jun 25, 2026
Analysis
Shandong Weida Machinery Co (002026) currently trades at ¥11.72, while our model-based Fair Value estimate is ¥14.30 — implying the stock looks roughly 22.0% undervalued today. We read business quality at 91/100 (high quality), in the Industrials 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: high) — always confirm before acting.
About the company
Shandong Weida Machinery Co., Ltd. engages in the manufacture and sale of drill chucks in China and internationally. The company offers various types of drill chucks, including keyless chuck, keyless chuck with lock, key-type drill chuck, keyless automatic duty drill, and accessories, as well as saw blades. It also provides precision castings, such as power tools accessories, machining products, auto parts, hardware and tools, instrument valves, and die-casting and other casting products. In addition, the company offers powder metallurgy parts comprising structure parts, oiliness bearings, chain wheels, pulleys for engines, helix gears and impacted blocks for impacted power, and over loading parts for impacted power drills; and parts for oil pump of automobiles and angular connected axels, angular grinders,combination saw, narrow gutter saw blade, keyhole saw blade, wide gutter saw blade, laser welding saw blade, metallurgical saw blade, non-ferrous metal cutting, woodworking saw, m…
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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.