-
1 Comment
Jiangyin Electrical Alloy Co.,Ltd is currently in a long term uptrend where the price is trading 19.0% above its 200 day moving average.
From a valuation standpoint, the stock is 42.7% cheaper than other stocks from the Basic Materials sector with a price to sales ratio of 2.3.
Jiangyin Electrical Alloy Co.,Ltd's total revenue rose by 14.1% to $450M since the same quarter in the previous year.
Its net income has increased by 102.7% to $31M since the same quarter in the previous year.
Finally, its free cash flow grew by 63.4% to $-947K since the same quarter in the previous year.
Based on the above factors, Jiangyin Electrical Alloy Co.,Ltd gets an overall score of 5/5.
CurrencyCode | CNY |
---|---|
Exchange | SHE |
Industry | Copper |
Sector | Basic Materials |
ISIN | CNE1000034R3 |
Beta | 0.27 |
---|---|
Target Price | None |
Market Cap | 3B |
Dividend Yield | 1.6% |
PE Ratio | 30.12 |
Jiangyin Electrical Alloy Co., Ltd. engages in the research and development, production, and sale of non-ferrous metal alloy products in China. The company offers copper rods, copper bars, flat copper bars, profile coil materials, contact wires, messenger wires, forging series products, stamping series products, CNC series products, and electromagnetic wires. Its products are used in medium and low voltage electric, hydropower station, architecture, nuclear power plant, spaceflight, high-speed rail, wind power generation, vessel, motor, and voltage transformer applications. The company was founded in 1985 and is based in Jiangyin, China.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 300697.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 2024