Metalpha Technology Holding (MATH) Fair Value & Analysis
Financial Services · US · Market cap $43.8M
Fair value as of: Jun 26, 2026
Analysis
Metalpha Technology Holding (MATH) currently trades at $0.9800, while our model-based Fair Value estimate is $1.96 — implying the stock looks roughly 100.0% undervalued today. We read business quality at 95/100 (high quality), in the Financial Services 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
Metalpha Technology Holding Limited, together with its subsidiaries, provides wealth management services in Hong Kong. It offers digital asset-based wealth management services; executes cryptocurrency-related transactions, including the issuance of derivative products to over-the-counter clients; proprietary trading services; crypto derivative market making services for clients, as well as facilitates the trading of crypto derivative products; traditional financial derivative products; and securities advisory and asset management services. The company also provides licensed services. It serves institutional investors and high-net-worth individuals. The company was formerly known as Dragon Victory International Limited and changed its name to Metalpha Technology Holding Limited in November 2022. Metalpha Technology Holding Limited was incorporated in 2015 and is headquartered in Wan Chai, Hong Kong.
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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.