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Syong Shun Metal Co (2073) Fair Value & Analysis

Basic Materials · TW · Market cap 715M TWD

Price25.50 TWD
Fair Value10.06 TWD
Upside-60.5%
Quality95/100
Evidence: Medium Range 7.51 TWD – 15.02 TWD

Fair value as of: Jun 24, 2026

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Analysis

Syong Shun Metal Co (2073) currently trades at 25.50 TWD, while our model-based Fair Value estimate is 10.06 TWD — implying the stock looks roughly 60.5% 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

Syong Shun Metal Co. Ltd. manufactures and sells steel products in Taiwan. It provides wire rods for use in screws, nuts, steel wire and steel cables, hand tools, and automobiles, as well as in the production of mechanical parts and other products. The company was founded in 2010 and is based in Kaohsiung, Taiwan.

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Frequently asked questions

Is Syong Shun Metal Co (2073) undervalued?
As of Jun 24, 2026, our model estimates a fair value of 10.06 TWD versus a price of 25.50 TWD — about −61% (overvalued). Model-based estimate, not financial advice.
What is the fair value of 2073?
Our 21-model fair value for Syong Shun Metal Co is 10.06 TWD (as of Jun 24, 2026), built from audited fundamentals. The current price is 25.50 TWD.
What is the quality score of 2073?
Syong Shun Metal Co has a Quality Score of 95/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.