-
1 Comment
King-Strong New Material Technology Co., Ltd is currently in a long term downtrend where the price is trading 5.3% below its 200 day moving average.
From a valuation standpoint, the stock is 50.1% more expensive than other stocks from the Industrials sector with a price to sales ratio of 7.6.
King-Strong New Material Technology Co., Ltd's total revenue rose by 8.2% to $85M since the same quarter in the previous year.
Its net income has increased by 179.8% to $11M since the same quarter in the previous year.
Finally, its free cash flow grew by 65.0% to $34M since the same quarter in the previous year.
Based on the above factors, King-Strong New Material Technology Co., Ltd gets an overall score of 3/5.
ISIN | CNE100002PB7 |
---|---|
Sector | Industrials |
Industry | Metal Fabrication |
CurrencyCode | CNY |
Exchange | SHE |
Market Cap | 4B |
---|---|
Dividend Yield | 0.4% |
Target Price | None |
PE Ratio | 35.09 |
Beta | 0.44 |
Guangdong Kingstrong Technology Co., Ltd. operates in the new material industry in China and internationally. It offers power amplifier and filter modules; launch component; power add-on; frequency hopping filter components; TR components; switch co-site filtering components; and solid state transmitter. The company provides thermal barrier coating, carbon fiber products, absorbing material, and photelectric products. The company was formerly known as King-Strong New Material Technology Co., Ltd. and changed its name to Guangdong Kingstrong Technology Co., Ltd. in October 2021. Guangdong Kingstrong Technology Co., Ltd. was founded in 1998 and is based in Foshan, China.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 300629.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