-
1 Comment
Juli Sling Co., Ltd is currently in a long term uptrend where the price is trading 6.4% above its 200 day moving average.
From a valuation standpoint, the stock is 64.4% cheaper than other stocks from the Industrials sector with a price to sales ratio of 1.8.
Juli Sling Co., Ltd's total revenue rose by 17.7% to $638M since the same quarter in the previous year.
Its net income has increased by 1416.1% to $15M since the same quarter in the previous year.
Finally, its free cash flow fell by 69.2% to $70M since the same quarter in the previous year.
Based on the above factors, Juli Sling Co., Ltd gets an overall score of 4/5.
| Exchange | SHE |
|---|---|
| CurrencyCode | CNY |
| Sector | Industrials |
| Industry | Specialty Industrial Machinery |
| ISIN | CNE100000KR8 |
| Market Cap | 15B |
|---|---|
| PE Ratio | None |
| Beta | 0.82 |
| Target Price | 15.05 |
| Dividend Yield | None |
Juli Sling Co., Ltd. researches, develops, manufactures, and sells rigging products in the People's Republic of China and internationally. The company offers synthetic fiber lifting belt slings, steel wire ropes, wire rope slings, chain slings, steel pull rods, cable slings, metallurgical clamps, beam slings, sling connectors, sling manufacturing equipment, etc. Its products are used in manufacturing, mining, construction, transportation, steel, metallurgy, mining, ocean, maritime, offshore oil, electricity, petroleum, bridges, venues, aerospace, ports, shipbuilding, engineering machinery, and other fields. Juli Sling Co., Ltd. was founded in 1985 and is based in Baoding, the People's Republic of China.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 002342.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 2026
