-
1 Comment
LiJiang YuLong Tourism Co., LTD is currently in a long term downtrend where the price is trading 9.0% below its 200 day moving average.
From a valuation standpoint, the stock is 92.4% more expensive than other stocks from the Consumer Cyclical sector with a price to sales ratio of 9.9.
LiJiang YuLong Tourism Co., LTD's total revenue sank by 4.3% to $157M since the same quarter in the previous year.
Its net income has increased by 17.6% to $22M since the same quarter in the previous year.
Finally, its free cash flow grew by 155.4% to $33M since the same quarter in the previous year.
Based on the above factors, LiJiang YuLong Tourism Co., LTD gets an overall score of 2/5.
| Exchange | SHE |
|---|---|
| CurrencyCode | CNY |
| ISIN | CNE000001KV9 |
| Sector | Consumer Cyclical |
| Industry | Lodging |
| Market Cap | 5B |
|---|---|
| PE Ratio | 26.06 |
| Target Price | 5.99 |
| Dividend Yield | 4.0% |
| Beta | 0.4 |
LiJiang YuLong Tourism Co., LTD. engages in the development and operation of tourism business in China. It operates Hefu Intercontinental Resort Hotel, Hotel Indigo Lijiang Old Town, Hotel Indigo Diqing Moonlight City, LUX Hotel Collection, and Holiday Inn Batang. The company also engages in tourism ropeway and related support services. In addition, it is involved in the construction and insurance business for tourism, real estate, hotel, transportation, catering, and other industries. Further, the company offers motor vehicle, corporate property, freight, health, construction and installation, personal accident, and liability insurance. The company was founded in 2001 and is based in Lijiang City, China.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 002033.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 2025
