-
1 Comment
Jiangsu Maysta Chemical Co., Ltd is currently in a long term downtrend where the price is trading 14.2% below its 200 day moving average.
From a valuation standpoint, the stock is 32.0% more expensive than other stocks from the Basic Materials sector with a price to sales ratio of 5.3.
Jiangsu Maysta Chemical 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 131.0% to $49M since the same quarter in the previous year.
Finally, its free cash flow fell by 69.4% to $10M since the same quarter in the previous year.
Based on the above factors, Jiangsu Maysta Chemical Co., Ltd gets an overall score of 2/5.
| Sector | Basic Materials |
|---|---|
| Industry | Specialty Chemicals |
| ISIN | CNE100002Y41 |
| Exchange | SHG |
| CurrencyCode | CNY |
| Market Cap | 2B |
|---|---|
| PE Ratio | 49.56 |
| Beta | 0.61 |
| Target Price | 16.72 |
| Dividend Yield | None |
Jiangsu Maysta Chemical Co., Ltd. engages in the research and development, production, and sale of organosilicon surfactants in China and internationally. The company offers PU foam stabilizers for rigid foams, slabstock foams, HR foams, shoe soles, OCF, and rigid foam and HR foam cell openers; and PU catalysts for specialties, rigid, and flexible foams. Its products are used in various sectors, such as home appliances, furniture, architecture, automobiles, etc. The company was founded in 2000 and is headquartered in Nanjing, China. Jiangsu Maysta Chemical Co., Ltd. is a subsidiary of Foshan Shunde District Demei Chemical Group Co., Ltd.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 603041.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
