Austral Gold Limited (AGLDF) Fair Value & Analysis
Basic Materials · US · Market cap $91.5M
Fair value as of: Jun 26, 2026
Analysis
Austral Gold Limited (AGLDF) currently trades at $0.1200, while our model-based Fair Value estimate is $0.1300 — implying the stock looks roughly 8.3% 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: high) — always confirm before acting.
About the company
Austral Gold Limited, together with its subsidiaries, engages in the exploration, production, and mining of gold and silver deposits in Chile, Argentina, and internationally. It explores gold and silver. The company holds 100% interest in the Guanaco/Amancaya Mine Complex located to the southeast of Antofagasta in Northern Chile; and Casposo/Manantiales Mine Complex located in in Argentina. It is also involved in Paleocene Belt exploration project located in Chile; and Triassic Choiyoi Belt exploration project located in Argentina. The company was formerly known as Diamond Rose NL and changed its name to Austral Gold Limited in November 2006. Austral Gold Limited was incorporated in 1996 and is based in Sydney, Australia. Austral Gold Limited operates as a subsidiary of Inversiones Financieras Del Sur S.A.
Open the full interactive analysis →
Similar stocks
Frequently asked questions
Is Austral Gold Limited (AGLDF) undervalued?
What is the fair value of AGLDF?
What is the quality score of AGLDF?
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.