-
1 Comment
Qingdao Copton Technology Company Limited is currently in a long term uptrend where the price is trading 5.0% above its 200 day moving average.
From a valuation standpoint, the stock is 50.2% cheaper than other stocks from the Basic Materials sector with a price to sales ratio of 2.0.
Qingdao Copton Technology Company Limited's total revenue sank by 4.4% to $235M since the same quarter in the previous year.
Its net income has increased by 79.6% to $24M since the same quarter in the previous year.
Finally, its free cash flow grew by 148.3% to $20M since the same quarter in the previous year.
Based on the above factors, Qingdao Copton Technology Company Limited gets an overall score of 4/5.
| Exchange | SHG |
|---|---|
| CurrencyCode | CNY |
| Sector | Basic Materials |
| Industry | Specialty Chemicals |
| ISIN | CNE100002763 |
| Market Cap | 3B |
|---|---|
| PE Ratio | 37.85 |
| Beta | 0.52 |
| Target Price | 29.1 |
| Dividend Yield | None |
Qingdao Copton Technology Company Limited produces and sells lubricants and car care products in China. It offers vehicle oil for passenger cars, commercial vehicles, and construction machineries; motorcycle products, such as four and two stroke series; ship products; and diesel engine and transmission oils. The company also provides hydraulic, gear, turbine, and air compressor oil series; and grease and other products to steel, cement, power, and mining industries. In addition, it offers oil selecting assistant and anti-counterfeiting verification services. The company was founded in 2003 and is based in Qingdao, China.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 603798.SHG 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
