Tiong Nam Logistics Holdings (8397) Fair Value & Analysis
Industrials · MY · Market cap 402M MYR
Fair value as of: Jun 24, 2026
Analysis
Tiong Nam Logistics Holdings (8397) currently trades at 0.7600 MYR, while our model-based Fair Value estimate is 1.36 MYR — implying the stock looks roughly 78.9% undervalued today. We read business quality at 96/100 (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
Tiong Nam Logistics Holdings Berhad, an investment holding company, trades in diesel and petrol in Malaysia. The company operates in four segments: Logistics and Warehousing Services, Investment, Property development, and Dormitory. It provides container haulage; cross-border and heavy transportation services; trucking and crane services; integrated logistics services; logistics solutions comprising comprehensive warehouse facilities and management; and trucking services, including general, cross-border, and heavy haulage, as well as air and sea freight forwarding, and last-mile delivery for e-commerce. The company also offers transportation and related services, such as forwarding, handling, forklifts services, labour, repairs, loading and unloading services. In addition, it provides coldroom facilities; stuffing and unstuffing; project management; custom clearance and related services, telephone, postages, and travelling charges; mobile cranes; and warehousing services, property i…
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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.