Master-Pack Group (7029) Fair Value & Analysis
Consumer Cyclical · MY · Market cap 87.9M MYR
Fair value as of: Jun 24, 2026
Analysis
Master-Pack Group (7029) currently trades at 1.68 MYR, while our model-based Fair Value estimate is 1.49 MYR — implying the stock looks roughly 11.3% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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
Master-Pack Group Berhad, an investment holding company, manufactures, distributes, and sells corrugated cartons and wooden packaging materials in Malaysia, Vietnam, and internationally. It provides sheet board, slotted type, telescope type, folder type, interior fitment, die-cut, and palletized shipping container products, as well as wooden pallet. The company also offers other packaging materials, including PE form, PU form, corrupad, cushion pad, edge protector, air bubble bag, plastic bag, strapping band, newsprint paper, and bubble pack products, as well as manufactures and sells wooden packaging boxes. In addition, it is involved in property letting activities. The company was formerly known as Hunza Consolidation Berhad. Master-Pack Group Berhad was incorporated in 1989 and is based in Nibong Tebal, Malaysia.
Open the full interactive analysis →
Similar stocks
Frequently asked questions
Is Master-Pack Group (7029) undervalued?
What is the fair value of 7029?
What is the quality score of 7029?
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.