-
1 Comment
China Lilang Limited is currently in a long term uptrend where the price is trading 13.3% above its 200 day moving average.
From a valuation standpoint, the stock is 97.1% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 1.8.
China Lilang Limited's total revenue sank by 0.0% to $1B since the same quarter in the previous year.
Its net income has dropped by 0.0% to $212M since the same quarter in the previous year.
Finally, its free cash flow grew by 31.4% to $259M since the same quarter in the previous year.
Based on the above factors, China Lilang Limited gets an overall score of 3/5.
| Sector | Consumer Cyclical |
|---|---|
| Industry | Apparel Manufacturing |
| Exchange | F |
| CurrencyCode | EUR |
| ISIN | KYG211411098 |
| Beta | 0.47 |
|---|---|
| Target Price | None |
| Market Cap | 506M |
| PE Ratio | 8.2 |
| Dividend Yield | 5.3% |
China Lilang Limited, together with its subsidiaries, manufactures and sells branded menswear and related accessories in the People's Republic of China. The company designs, sources, manufactures, and sells business and casual apparel for men under the LILANZ and LESS IS MORE brands. It also offers management, brand management, trading, transaction, and storage services. The company distributes its products online and through a retail and distribution network covering provinces, autonomous regions, and municipalities. The company was founded in 1987 and is headquartered in Jinjiang, the People's Republic of China. China Lilang Limited is a subsidiary of Xiao Sheng International Limited.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 5LX.F 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
