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AIC Mines Limited (IAUFF) Fair Value & Analysis

Basic Materials · US · Market cap $415M

Price$0.5100
Fair Value$0.1400
Upside-72.5%
Quality80/100
Evidence: Medium Range $0.1200 – $0.1800

Fair value as of: Jun 24, 2026

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Analysis

AIC Mines Limited (IAUFF) currently trades at $0.5100, while our model-based Fair Value estimate is $0.1400 — implying the stock looks roughly 72.5% overvalued today. We read business quality at 80/100 (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: medium).

About the company

AIC Mines Limited engages in the exploration, development, and production of mines in Australia. The company operates through Mining, and Exploration and Corporate segments. It explores for gold, copper, silver, and zinc deposits. AIC Mines Limited was incorporated in 1993 and is based in Subiaco, Australia.

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Frequently asked questions

Is AIC Mines Limited (IAUFF) undervalued?
As of Jun 24, 2026, our model estimates a fair value of $0.1400 versus a price of $0.5100 — about −73% (overvalued). Model-based estimate, not financial advice.
What is the fair value of IAUFF?
Our 21-model fair value for AIC Mines Limited is $0.1400 (as of Jun 24, 2026), built from audited fundamentals. The current price is $0.5100.
What is the quality score of IAUFF?
AIC Mines Limited has a Quality Score of 80/100, 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.