Hiap Teck Venture Berhad (5072) Fair Value & Analysis
Basic Materials · MY · Market cap 470M MYR
Fair value as of: Jun 25, 2026
Analysis
Hiap Teck Venture Berhad (5072) currently trades at 0.2700 MYR, while our model-based Fair Value estimate is 0.6900 MYR — implying the stock looks roughly 155.6% undervalued today. We read business quality at 95/100 (high quality), in the Basic Materials 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
Hiap Teck Venture Berhad manufactures, rents, distributes, and sells steel pipes, hollow sections, scaffolding equipment and accessories, and other steel products in Malaysia. The company operates through Trading, Manufacturing, Property and Investment, Transportation, Mining and Exploration, and selling of electricity segments. It operates as an importer, exporter, lessor, and a general dealer of steel products, hardware, and building materials. In addition, the company offers transportation services, as well as invests in properties. Further, it is involved in exploring, contracting, mining, processing, and selling of iron ore; the wholesale of industrial, construction, and related machinery and equipment; and geotechnical and civil engineering works, including special foundations, underground works, group improvements, and pollution treatments and control, as well as sale of electricity. The company was incorporated in 1993 and is headquartered in Klang, Malaysia.
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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.