-
1 Comment
Lyft, Inc is currently in a long term uptrend where the price is trading 16.4% above its 200 day moving average.
From a valuation standpoint, the stock is 66.4% cheaper than other stocks from the Technology sector with a price to sales ratio of 8.5.
Lyft, Inc's total revenue sank by 44.0% to $570M since the same quarter in the previous year.
Its net income has dropped by 28.7% to $-458M since the same quarter in the previous year.
Finally, its free cash flow fell by 199.9% to $-287M since the same quarter in the previous year.
Based on the above factors, Lyft, Inc gets an overall score of 2/5.
Exchange | NASDAQ |
---|---|
CurrencyCode | USD |
ISIN | US55087P1049 |
Sector | Technology |
Industry | Software - Application |
Market Cap | 5B |
---|---|
PE Ratio | 190.67 |
Target Price | 16.1742 |
Beta | 2.14 |
Dividend Yield | None |
Lyft, Inc. operates a peer-to-peer marketplace for on-demand ridesharing in the United States and Canada. The company operates multimodal transportation networks that offer access to various transportation options through the Lyft platform and mobile-based applications. Its platform provides a ridesharing marketplace that connects drivers with riders; Express Drive, a car rental program for drivers; and a network of shared bikes and scooters in various cities to address the needs of riders for short trips. The company was formerly known as Zimride, Inc. and changed its name to Lyft, Inc. in April 2013. Lyft, Inc. was incorporated in 2007 and is headquartered in San Francisco, California.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for LYFT 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