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Big Tree Group (BIGG) Fair Value & Analysis

Consumer Cyclical · US · Market cap $2.2K

Price$0.0001
Fair Value$0.0001
Upside+0.0%
Quality85/100
Evidence: Medium Range $0.0001 – $0.0001

Fair value as of: Jun 26, 2026

Analysis

Big Tree Group (BIGG) 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 85/100 (high quality), in the Consumer Cyclical 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

Big Tree Group, Inc. engages in sourcing and dissemination of useful information. It offers information for legal and real estate categories. The company is based in Deerfield Beach, Florida. Big Tree Group, Inc. is a subsidiary of Lins (HK) International Trading Limited.

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Frequently asked questions

Is Big Tree Group (BIGG) 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 BIGG?
Our 21-model fair value for Big Tree Group is $0.0001 (as of Jun 26, 2026), built from audited fundamentals. The current price is $0.0001.
What is the quality score of BIGG?
Big Tree Group has a Quality Score of 85/100, 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.