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Star Minerals Limited (SMS) Fair Value & Analysis

Basic Materials · AU · Market cap A$10.1M

PriceA$0.0400
Fair ValueA$0.0960
Upside+140.0%
Quality95/100
Evidence: Low Range A$0.0760 – A$0.1160

Fair value as of: Jun 26, 2026

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Analysis

Star Minerals Limited (SMS) currently trades at A$0.0400, while our model-based Fair Value estimate is A$0.0960 — implying the stock looks roughly 140.0% undervalued today. We read business quality at 95/100 (high quality), in the Basic Materials 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: low) — always confirm before acting.

About the company

Star Minerals Limited, explores, develops, and evaluates gold, copper, and base metals properties in Australia. Its flagship project is the Tumblegum South Gold Project located south of Meekatharra in Western Australia. Star Minerals Limited was incorporated in 2021 and is based in Leederville, Australia.

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Frequently asked questions

Is Star Minerals Limited (SMS) undervalued?
As of Jun 26, 2026, our model estimates a fair value of A$0.0960 versus a price of A$0.0400 — about +140% (undervalued). Model-based estimate, not financial advice.
What is the fair value of SMS?
Our 21-model fair value for Star Minerals Limited is A$0.0960 (as of Jun 26, 2026), built from audited fundamentals. The current price is A$0.0400.
What is the quality score of SMS?
Star Minerals Limited has a Quality Score of 95/100, 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.