-
1 Comment
Zhejiang Wanliyang Co., Ltd is currently in a long term uptrend where the price is trading 3.6% above its 200 day moving average.
From a valuation standpoint, the stock is 67.0% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 1.7.
Zhejiang Wanliyang Co., Ltd's total revenue rose by 31.4% to $2B since the same quarter in the previous year.
Its net income has increased by 99.0% to $231M since the same quarter in the previous year.
Finally, its free cash flow grew by 291.0% to $114M since the same quarter in the previous year.
Based on the above factors, Zhejiang Wanliyang Co., Ltd gets an overall score of 5/5.
ISIN | CNE100000QB9 |
---|---|
Exchange | SHE |
CurrencyCode | CNY |
Sector | Consumer Cyclical |
Industry | Auto Parts |
Beta | 0.7 |
---|---|
Market Cap | 9B |
PE Ratio | 39.33 |
Target Price | 9 |
Dividend Yield | 4.2% |
Zhejiang Wanliyang Co., Ltd. engages in the research and development, production, and sale of automotive transmissions and transmission/drive system products for new energy vehicles in China and internationally. It offers automotive transmission products, including passenger car transmissions and commercial vehicle transmissions, which are used in passenger cars, SUVs, MPVs, micro trucks, light trucks, medium trucks, heavy trucks, buses, and other models. The company was formerly known as Zhejiang Wanliyang Transmission Company Ltd. and changed its name to Zhejiang Wanliyang Co., Ltd. in January 2016. Zhejiang Wanliyang Co., Ltd. was founded in 2003 and is headquartered in Hangzhou, China.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 002434.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