-
1 Comment
Cambria Automobiles plc is currently in a long term uptrend where the price is trading 18.6% above its 200 day moving average.
From a valuation standpoint, the stock is 97.9% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 0.1.
Cambria Automobiles plc's total revenue sank by 0.0% to $175M since the same quarter in the previous year.
Its net income has dropped by 0.0% to $3M since the same quarter in the previous year.
Finally, its free cash flow grew by 16227.8% to $6M since the same quarter in the previous year.
Based on the above factors, Cambria Automobiles plc gets an overall score of 3/5.
ISIN | GB00B4R32X65 |
---|---|
Exchange | LSE |
CurrencyCode | GBP |
Sector | Consumer Cyclical |
Industry | Auto & Truck Dealerships |
Market Cap | 82M |
---|---|
PE Ratio | 7.57 |
Target Price | 84 |
Dividend Yield | 1.0% |
Beta | 1.03 |
Cambria Automobiles plc operates as a retailer of new and used cars, commercial vehicles, and motorbikes in the United Kingdom. The company offers its products under the Doves, Grange, Dees, Invicta, Motorparks, and Pure Triumph brands. It also provides accident repair facilities for its customers through its accident repair centre in Kent or through sub-contract to other accident repair centers; and supplies parts on behalf of manufacturer brands, as well as to other car dealers, independent traders, and repairers. In addition, the company provides maintenance and warranty repair services. It operates through 45 franchises in 28 locations. Cambria Automobiles plc was founded in 2006 and is based in Swindon, the United Kingdom.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for CAMB.LSE 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