Walsin Lihwa Corporation (1605) Fair Value & Analysis
Basic Materials · TW · Market cap 175B TWD
Analysis
Walsin Lihwa Corporation (1605) currently trades at 38.15 TWD, while our model-based Fair Value estimate is 15.07 TWD — implying the stock looks roughly 60.5% overvalued today. We read business quality at 90/100 (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: high).
About the company
Walsin Lihwa Corporation engages in the manufacture and sale of wires and cables, and stainless-steel products in Asia, the United States, Europe, and internationally. The company operates through Wires and Cables; Stainless Steel; Resource; Real Estate; and Administration and Investing segments. It also offers copper wires and rods, power cables, communication cables, optical cables, industrial cables, steel cable and wire, and submarine cables for use in power, telecommunication, electrical engineering, information household appliance, construction, ship, heavy machinery, solar power, wind power, and electric vehicles; and copper rods, connectors, and components. In addition, the company provides stainless steel products, including billets, slabs, ingots, hot-rolled bars, cold- and hot-rolled coils, wire rods, cold-finished bars, seamless pipes and tubes, and precision foils. Further, the company engages in the production and sale of nickel pig iron and nickel matte. Further, it d…
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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.