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Arcadia Minerals Limited (AM7) Fair Value & Analysis

Basic Materials · AU · Market cap A$6.6M

PriceA$0.0400
Fair ValueA$0.0280
Upside-30.0%
Quality77/100
Evidence: Low Range A$0.0280 – A$0.0280

Fair value as of: Jun 26, 2026

Analysis

Arcadia Minerals Limited (AM7) currently trades at A$0.0400, while our model-based Fair Value estimate is A$0.0280 — implying the stock looks roughly 30.0% overvalued today. We read business quality at 77/100 (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: low).

About the company

Arcadia Minerals Limited explores and develops mineral properties in Namibia. The company explores for tantalum, lithium, lithium in brine and clay, copper, gold, nickel, and platinum group elements deposits. Arcadia Minerals Limited was incorporated in 2020 and is based in Victoria Park, Australia.

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Frequently asked questions

Is Arcadia Minerals Limited (AM7) undervalued?
As of Jun 26, 2026, our model estimates a fair value of A$0.0280 versus a price of A$0.0400 — about −30% (overvalued). Model-based estimate, not financial advice.
What is the fair value of AM7?
Our 21-model fair value for Arcadia Minerals Limited is A$0.0280 (as of Jun 26, 2026), built from audited fundamentals. The current price is A$0.0400.
What is the quality score of AM7?
Arcadia Minerals Limited has a Quality Score of 77/100, 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.