Depo Auto Parts Industrial Co (6605) Fair Value & Analysis
Consumer Cyclical · TW · Market cap 23.4B TWD
Analysis
Depo Auto Parts Industrial Co (6605) currently trades at 139.50 TWD, while our model-based Fair Value estimate is 307.88 TWD — implying the stock looks roughly 120.7% undervalued today. We read business quality at 93/100 (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: high) — always confirm before acting.
About the company
Depo Auto Parts Industrial Co., Ltd. manufactures and sells automotive and other related lighting products in Taiwan, Asia, and the United States. The company offers stamping, plastic injections, and painting and other transportation equipment. It also engages in the manufactures and trades of automotive and motorcycle parts; automotive and motorcycle light fixtures and bulbs; automotive plastic parts; trades of auto parts; supplies automotive and automotive light equipment; automotive instrument clusters; car and signal lights; automotive parts and components; light products; research and development of automotive electronic devices; and other related products. In addition, the company is involved in the investment business. The company was formerly known as Ming Yang Corp. and changed its name to Depo Auto Parts Industrial Co., Ltd. in August 2002. Depo Auto Parts Industrial Co., Ltd. was founded in 1969 and is headquartered in Taipei, Taiwan.
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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.