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Amazonas Florestal, Ltd (AZFL) Fair Value & Analysis

Healthcare · US · Market cap $1.6M

Price$0.0001
Fair Value$0.0001
Upside+0.0%
Quality89/100
Evidence: Medium

Fair value as of: Jun 26, 2026

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Analysis

Amazonas Florestal, Ltd (AZFL) currently trades at $0.0001, while our model-based Fair Value estimate is $0.0001 — implying the stock looks roughly 0.0% undervalued today. We read business quality at 89/100 (high quality), in the Healthcare 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: medium) — always confirm before acting.

About the company

Amazonas Florestal, Ltd operates and manages timber tracts. The company was formerly known as Ecologic Systems, Inc and changed its name to Amazonas Florestal, Ltd in 2012. Amazonas Florestal, Ltd was incorporated in 2008 and is based in Miami, Florida.

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Frequently asked questions

Is Amazonas Florestal, Ltd (AZFL) undervalued?
As of Jun 26, 2026, our model estimates a fair value of $0.0001 versus a price of $0.0001 — about +0% (undervalued). Model-based estimate, not financial advice.
What is the fair value of AZFL?
Our 21-model fair value for Amazonas Florestal, Ltd is $0.0001 (as of Jun 26, 2026), built from audited fundamentals. The current price is $0.0001.
What is the quality score of AZFL?
Amazonas Florestal, Ltd has a Quality Score of 89/100, 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.