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Hongchang International Co (HCIL) Fair Value & Analysis

Consumer Defensive · US · Market cap $80.9M

Price$0.1070
Fair Value$0.0500
Upside-53.3%
Quality92/100
Evidence: Low Range $0.0400 – $0.0700

Fair value as of: Jun 26, 2026

Analysis

Hongchang International Co (HCIL) currently trades at $0.1070, while our model-based Fair Value estimate is $0.0500 — implying the stock looks roughly 53.3% overvalued today. We read business quality at 92/100 (high quality), in the Consumer Defensive 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

Hongchang International Co., Ltd engages in the food trading and biotechnology businesses in China, the United States, and internationally. It offers technical and consultation services. The company is based in Fuzhou, China.

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Frequently asked questions

Is Hongchang International Co (HCIL) undervalued?
As of Jun 26, 2026, our model estimates a fair value of $0.0500 versus a price of $0.1070 — about −53% (overvalued). Model-based estimate, not financial advice.
What is the fair value of HCIL?
Our 21-model fair value for Hongchang International Co is $0.0500 (as of Jun 26, 2026), built from audited fundamentals. The current price is $0.1070.
What is the quality score of HCIL?
Hongchang International Co 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.