-
1 Comment
Hyosung TNC Corporation is currently in a long term uptrend where the price is trading 100.6% above its 200 day moving average.
From a valuation standpoint, the stock is 66.1% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 0.5.
Hyosung TNC Corporation's total revenue sank by 0.3% to $1T since the same quarter in the previous year.
Its net income has increased by 139.8% to $88B since the same quarter in the previous year.
Finally, its free cash flow grew by 2928.6% to $172B since the same quarter in the previous year.
Based on the above factors, Hyosung TNC Corporation gets an overall score of 4/5.
| ISIN | KR7298020009 |
|---|---|
| Exchange | KO |
| CurrencyCode | KRW |
| Sector | Consumer Cyclical |
| Industry | Textile Manufacturing |
| Market Cap | 2T |
|---|---|
| Beta | 1.56 |
| PE Ratio | None |
| Target Price | 503333.34 |
| Dividend Yield | None |
Hyosung TNC Corporation manufactures and sells fiber in South Korea and internationally. It offers spandex, nylon, and polyesterThe company also provides general apparel, workwear, and industrial fabrics; and dyed products comprising nylon fabrics, polyester fabrics, nylon/polyester stretch fabrics, and cotton-blend fabrics. In addition, it offers marketing services in the trading sector with steel and chemicals, markets. Further, the company operates Sevit Island, a floating structure built on a floating body on top of the water. Hyosung TNC Corporation was founded in 1957 and is headquartered in Seoul, South Korea.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 298020.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 2026