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Cango Inc (CANG) Fair Value & Analysis

Financial Services · US · Market cap $117M

Price$0.2081
Fair Value$0.2873
Upside+38.1%
Quality95/100
Evidence: Low Range $0.2121 – $0.4241

Fair value as of: Jun 26, 2026

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Analysis

Cango Inc (CANG) currently trades at $0.2081, while our model-based Fair Value estimate is $0.2873 — implying the stock looks roughly 38.1% undervalued today. We read business quality at 95/100 (high quality), in the Financial Services 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

Cango Inc. operates bitcoin mining business with mining operations across North America, the Middle East, South America, and East Africa. It also operates an online international used car export business through AutoCango.com. Cango Inc. was founded in 2010 and is headquartered in Dallas, Texas.

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Frequently asked questions

Is Cango Inc (CANG) undervalued?
As of Jun 26, 2026, our model estimates a fair value of $0.2873 versus a price of $0.2081 — about +38% (undervalued). Model-based estimate, not financial advice.
What is the fair value of CANG?
Our 21-model fair value for Cango Inc is $0.2873 (as of Jun 26, 2026), built from audited fundamentals. The current price is $0.2081.
What is the quality score of CANG?
Cango Inc has a Quality Score of 95/100, 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.