-
1 Comment
E1 Corporation is currently in a long term uptrend where the price is trading 23.7% above its 200 day moving average.
From a valuation standpoint, the stock is 79.7% cheaper than other stocks from the Energy sector with a price to sales ratio of 0.1.
E1 Corporation's total revenue sank by 27.4% to $955B since the same quarter in the previous year.
Its net income has dropped by 44.1% to $11B since the same quarter in the previous year.
Finally, its free cash flow grew by 207.8% to $148B since the same quarter in the previous year.
Based on the above factors, E1 Corporation gets an overall score of 3/5.
| Exchange | KO |
|---|---|
| CurrencyCode | KRW |
| ISIN | KR7017940008 |
| Sector | Energy |
| Industry | Oil & Gas Refining & Marketing |
| Market Cap | 353B |
|---|---|
| PE Ratio | None |
| Target Price | 67000 |
| Dividend Yield | 5.7% |
| Beta | 0.65 |
E1 Corporation imports, stores, trades, and sells liquefied petroleum gas (LPG) in South Korea. The company transports LPG from oil-producing countries; and stores and manages LPG at Yeosu, Daesan, and Incheon terminals. It is also involved in the solar and wind power business; hydrogen charging business; electric vehicle charging business; investment activities; and digital technology and data-based business. In addition, the company offers Orange Cards; and Orange Plus, a charging station brand that combines LPG, hydrogen, and electric charging stations with vehicle-related convenience services. It also exports its products. E1 Corporation was founded in 1984 and is headquartered in Seoul, South Korea.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 017940.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
