Bosung Power Technology Co (006910) Fair Value & Analysis
Industrials · KR · Market cap 386B KRW
Fair value as of: Jun 25, 2026
Analysis
Bosung Power Technology Co (006910) currently trades at 7,280 KRW, while our model-based Fair Value estimate is 9,344 KRW — implying the stock looks roughly 28.4% undervalued today. We read business quality at 95/100 (high quality), in the Industrials 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
Bosung Power Technology Co., Ltd operates in the domestic power industry in South Korea, the Philippines, Myanmar, and internationally. The company offers smart grid station, standard energy storage system (ESS), power system connected ESS, and Renewable energy type ESS systems; and lithium ion battery and battery management system (BMS), as well as ESS product specification. It also manufactures and supplies steel frames for domestic and overseas plant construction; designs, processes, manufactures, and assembles power transmission facility from steel and steel pipes; and develops power equipment, which include gas insulated load switch, polymer lightning arrester, cutout switch, dry type transformer neutral point ground reactor, wire fuse, automatic switch, and underground low pressure junction box and gas insulated load switch. The company was formerly known as Bosung Electric Co., Ltd. and changed its name to Bosung Power Technology Co., Ltd in January 2000. Bosung Power Technol…
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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.