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AUTO& Inc (353590) Fair Value & Analysis

Consumer Cyclical · KR · Market cap 26.0B KRW

Price1,754 KRW
Fair Value2,005 KRW
Upside+14.3%
Quality95/100
Evidence: Low Range 1,370 KRW – 2,639 KRW

Fair value as of: Jun 24, 2026

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Analysis

AUTO& Inc (353590) currently trades at 1,754 KRW, while our model-based Fair Value estimate is 2,005 KRW — implying the stock looks roughly 14.3% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: low) — always confirm before acting.

About the company

AUTO& Inc. engages in manufacturing and sale of automobile supplies and accessories in South Korea. The company also develops an e-commerce platform specialized for O2O-based means of transportation. AUTO& Inc. was founded in 2012 and is based in Anyang-si, South Korea.

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Frequently asked questions

Is AUTO& Inc (353590) undervalued?
As of Jun 24, 2026, our model estimates a fair value of 2,005 KRW versus a price of 1,754 KRW — about +14% (undervalued). Model-based estimate, not financial advice.
What is the fair value of 353590?
Our 21-model fair value for AUTO& Inc is 2,005 KRW (as of Jun 24, 2026), built from audited fundamentals. The current price is 1,754 KRW.
What is the quality score of 353590?
AUTO& Inc 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.