-
1 Comment
Fujian Acetron New Materials Co., Ltd is currently in a long term uptrend where the price is trading 21.8% above its 200 day moving average.
From a valuation standpoint, the stock is 13.8% cheaper than other stocks from the None sector with a price to sales ratio of 10.7.
Fujian Acetron New Materials Co., Ltd's total revenue rose by 29.9% to $109M since the same quarter in the previous year.
Its net income has increased by 22.2% to $6M since the same quarter in the previous year.
Based on the above factors, Fujian Acetron New Materials Co., Ltd gets an overall score of 4/5.
| Sector | Basic Materials |
|---|---|
| Industry | Chemicals |
| ISIN | CNE100003506 |
| Exchange | SHE |
| CurrencyCode | CNY |
| Beta | 0.18 |
|---|---|
| Market Cap | 3B |
| PE Ratio | 407.4 |
| Target Price | 30 |
| Dividend Yield | 0.1% |
Fujian Acetron New Materials Co., Ltd. engages in the research, development, production, and sale of vacuum evaporation and sputter coating materials in China. The company offers compounds, fluoride materials, oxide materials, targets, metal and alloy materials, organics, and coating accessories for the optics/optic communication industry; and sulfide and other materials. It also provides targets for building/automotive glass coating film, flat panel display, solar energy/photovoltaic, and decorative/tool coating industries. The company also exports its products primarily to Japan, the United States, Germany, and Korea. Fujian Acetron New Materials Co., Ltd. was founded in 2002 and is headquartered in Fuzhou, China.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 300706.SHE 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
