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XCF Global, Inc (SAFX) Fair Value & Analysis

Utilities · US · Market cap $173M

Price$0.4566
Fair Value$0.1900
Upside-58.4%
Quality87/100
Evidence: Medium Range $0.1400 – $0.2300

Fair value as of: Jun 24, 2026

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Analysis

XCF Global, Inc (SAFX) currently trades at $0.4566, while our model-based Fair Value estimate is $0.1900 — implying the stock looks roughly 58.4% overvalued today. We read business quality at 87/100 (high quality), in the Utilities sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: medium).

About the company

XCF Global, Inc. manufactures and sells renewable fuels in the United States. It develops and operates clean fuel production facilities to produce clean-burning and sustainable biofuels, sustainable aviation fuels, and renewable diesel and naphtha products; and sells environmental credits. XCF Global, Inc. was founded in 2016 and is headquartered in Houston, Texas.

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Frequently asked questions

Is XCF Global, Inc (SAFX) undervalued?
As of Jun 24, 2026, our model estimates a fair value of $0.1900 versus a price of $0.4566 — about −58% (overvalued). Model-based estimate, not financial advice.
What is the fair value of SAFX?
Our 21-model fair value for XCF Global, Inc is $0.1900 (as of Jun 24, 2026), built from audited fundamentals. The current price is $0.4566.
What is the quality score of SAFX?
XCF Global, Inc has a Quality Score of 87/100, 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.