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KARIN (KARIN) Fair Value & Analysis

Consumer Cyclical · CZ · Market cap 1.0B CZK

Price146.00 CZK
Fair Value130.42 CZK
Upside-10.7%
Quality83/100
Evidence: Medium Range 97.82 CZK – 163.03 CZK

Fair value as of: Jun 24, 2026

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Analysis

KARIN (KARIN) currently trades at 146.00 CZK, while our model-based Fair Value estimate is 130.42 CZK — implying the stock looks roughly 10.7% overvalued today. We read business quality at 83/100 (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: medium).

About the company

Karo Invest A.S. processes, produces, and sells industrial leather products in the Czech Republic and other European Union countries. It offers buffalo and cowhide products primarily for furniture manufacturers. The company was founded in 2007 and is based in Prague, the Czech Republic.

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Frequently asked questions

Is KARIN (KARIN) undervalued?
As of Jun 24, 2026, our model estimates a fair value of 130.42 CZK versus a price of 146.00 CZK — about −11% (overvalued). Model-based estimate, not financial advice.
What is the fair value of KARIN?
Our 21-model fair value for KARIN is 130.42 CZK (as of Jun 24, 2026), built from audited fundamentals. The current price is 146.00 CZK.
What is the quality score of KARIN?
KARIN has a Quality Score of 83/100, 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.