Orvana Minerals Corp (ORVMF) Fair Value & Analysis
Basic Materials · US · Market cap $158M
Fair value as of: Jun 24, 2026
Analysis
Orvana Minerals Corp (ORVMF) currently trades at $1.12, while our model-based Fair Value estimate is $1.34 — implying the stock looks roughly 19.6% 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
Orvana Minerals Corp., a mining and exploration company, engages in the evaluation, development, and mining of gold, copper, silver, and other precious and base metal deposits. It owns and operates El Valle and Carlés mines that produces copper concentrate located in the Rio Narcea Gold Belt, northern Spain. The company also owns Don Mario Mine containing 10 contiguous mineral concessions covering an area of approximately 53,325 hectares situated in Don Mario district, southeastern Bolivia. In addition, it owns interest in the Taguas property, which consist of 15 mining concessions located in San Juan, Argentina. The company is based in Toronto, Canada. Orvana Minerals Corp. operates as a subsidiary of Fabulosa Mines Limited.
Open the full interactive analysis →
Similar stocks
Frequently asked questions
Is Orvana Minerals Corp (ORVMF) undervalued?
What is the fair value of ORVMF?
What is the quality score of ORVMF?
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.