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Vestand Inc (VSTD) Fair Value & Analysis

Consumer Cyclical · US · Market cap $3.2M

Price$0.2256
Fair Value$0.0700
Upside-69.0%
Quality92/100
Evidence: Low Range $0.0500 – $0.0900

Fair value as of: Jun 25, 2026

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Analysis

Vestand Inc (VSTD) currently trades at $0.2256, while our model-based Fair Value estimate is $0.0700 — implying the stock looks roughly 69.0% overvalued today. We read business quality at 92/100 (high quality), in the Consumer Cyclical 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: low).

About the company

Vestand Inc. together with its subsidiaries, owns and operates Japanese restaurants in California. It offers bone broth, ramen, rice bowls, appetizers, sushi rolls, bento boxes, and other Japanese cuisines. Vestand Inc. was formerly known as Yoshiharu Global Co. and changed its name to Vestand Inc. in September 2025. The company was founded in 2016 and is based in Brea, California.

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Frequently asked questions

Is Vestand Inc (VSTD) undervalued?
As of Jun 25, 2026, our model estimates a fair value of $0.0700 versus a price of $0.2256 — about −69% (overvalued). Model-based estimate, not financial advice.
What is the fair value of VSTD?
Our 21-model fair value for Vestand Inc is $0.0700 (as of Jun 25, 2026), built from audited fundamentals. The current price is $0.2256.
What is the quality score of VSTD?
Vestand Inc has a Quality Score of 92/100, 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.