-
1 Comment
Wonlim Corporation is currently in a long term uptrend where the price is trading 22.2% above its 200 day moving average.
From a valuation standpoint, the stock is 52.5% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 0.7.
Wonlim Corporation's total revenue rose by 29.5% to $20B since the same quarter in the previous year.
Its net income has dropped by 35.3% to $1B since the same quarter in the previous year.
Finally, its free cash flow grew by 284.9% to $3B since the same quarter in the previous year.
Based on the above factors, Wonlim Corporation gets an overall score of 4/5.
| Sector | Consumer Cyclical |
|---|---|
| Industry | Packaging & Containers |
| Exchange | KO |
| CurrencyCode | KRW |
| ISIN | KR7005820006 |
| Target Price | None |
|---|---|
| Beta | 0.5 |
| Market Cap | 29B |
| PE Ratio | None |
| Dividend Yield | 2.8% |
Wonlim Corporation manufactures and sells industrial packaging materials primarily in South Korea. The company offers sandwich, laminating, cement, PE, box, and FIBC bags. It also engages in the wholesale business of medical devices and investment in securities, as well as real estate, finance, and leasing activities. In addition, the company is involved in finance, and leasing activities. Further, it engages in the wholesale business of medical devices. Additionally, the company is involved in investment in securities related businesses. Wonlim Corporation was founded in 1968 and is headquartered in Seoul, South Korea.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 005820.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
