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Lion One Metals Limited (LIO) Fair Value & Analysis

Basic Materials · CA · Market cap C$56.4M

PriceC$0.1200
Fair ValueC$0.2700
Upside+125.0%
Quality92/100
Evidence: Low Range C$0.2100 – C$0.4100

Fair value as of: Jun 26, 2026

Analysis

Lion One Metals Limited (LIO) currently trades at C$0.1200, while our model-based Fair Value estimate is C$0.2700 — implying the stock looks roughly 125.0% undervalued today. We read business quality at 92/100 (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

Lion One Metals Limited engages in mine development and exploration of mineral properties in Fiji. Its primary asset is the 100% owned Tuvatu Gold project, which comprise four exploration licenses located on the island of Viti Levu in Fiji. The company is headquartered in North Vancouver, Canada.

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Frequently asked questions

Is Lion One Metals Limited (LIO) undervalued?
As of Jun 26, 2026, our model estimates a fair value of C$0.2700 versus a price of C$0.1200 — about +125% (undervalued). Model-based estimate, not financial advice.
What is the fair value of LIO?
Our 21-model fair value for Lion One Metals Limited is C$0.2700 (as of Jun 26, 2026), built from audited fundamentals. The current price is C$0.1200.
What is the quality score of LIO?
Lion One Metals Limited has a Quality Score of 92/100, 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.