-
1 Comment
Seowon Co., Ltd is currently in a long term downtrend where the price is trading 1.8% below its 200 day moving average.
From a valuation standpoint, the stock is 47.0% cheaper than other stocks from the Industrials sector with a price to sales ratio of 0.5.
Seowon Co., Ltd's total revenue sank by 0.2% to $69B since the same quarter in the previous year.
Its net income has dropped by 345.7% to $-6B since the same quarter in the previous year.
Finally, its free cash flow fell by 649.1% to $-9B since the same quarter in the previous year.
Based on the above factors, Seowon Co., Ltd gets an overall score of 1/5.
Exchange | KO |
---|---|
CurrencyCode | KRW |
Industry | Metal Fabrication |
ISIN | KR7021050000 |
Sector | Industrials |
Target Price | None |
---|---|
Beta | 0.43 |
Market Cap | 60B |
PE Ratio | None |
Dividend Yield | None |
Seowon Co., Ltd. manufactures and sells non-ferrous metals in South Korea. The company offers brass billets, slabs, and ingots, as well as bronze ingots; and recycles brass ash, granular brass and aluminum, clove, sludge, mill berry, oxidized copper, and grinding powder, as well as copper rods for electric wires and copper billets. Its products are used in various applications, such as cars and industrial machinery parts, electrical and electronic equipment parts, ship building components and firefighting facilities, faucets, heat exchange appliances parts, and high-tech equipment and heavy industry products. The company was founded in 1988 and is headquartered in Ansan-Si, South Korea.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 021050.KO using our backtest tool. PyInvesting provides the backtesting software for you to backtest your investment strategy. Our backtest software is written using Python code and allows you to backtest stock, backtest etf, backtest options, backtest crypto and backtest forex online. Our backtesting Python framework is highly robust and gives you a realistic simulation of how your strategy would have performed in the past using backtest data.
© PyInvesting 2025