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CV Holdings (CVHL) Fair Value & Analysis

Financial Services · US · Market cap $863K

Price$0.0151
Fair Value$0.0150
Upside-0.4%
Quality95/100
Evidence: High Range $0.0135 – $0.0175

Fair value as of: Jun 26, 2026

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Analysis

CV Holdings (CVHL) currently trades at $0.0151, while our model-based Fair Value estimate is $0.0150 — implying the stock looks roughly 0.4% overvalued today. We read business quality at 95/100 (high quality), in the Financial Services 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: high).

About the company

CV Holdings, Inc. operates as a specialty finance company. It owns ownership in finance platforms across multiple businesses, including small-ticket equipment financing and commercial real estate bridge lending. CV Holdings, Inc. was incorporated in 2005 and is based in Newport Beach, California.

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Frequently asked questions

Is CV Holdings (CVHL) undervalued?
As of Jun 26, 2026, our model estimates a fair value of $0.0150 versus a price of $0.0151 — about −0% (overvalued). Model-based estimate, not financial advice.
What is the fair value of CVHL?
Our 21-model fair value for CV Holdings is $0.0150 (as of Jun 26, 2026), built from audited fundamentals. The current price is $0.0151.
What is the quality score of CVHL?
CV Holdings 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.