-
1 Comment
Superdry plc is currently in a long term uptrend where the price is trading 44.8% above its 200 day moving average.
From a valuation standpoint, the stock is 93.7% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 0.3.
Superdry plc's total revenue sank by 0.0% to $185M 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 299.0% to $10M since the same quarter in the previous year.
Based on the above factors, Superdry plc gets an overall score of 3/5.
| Exchange | LSE |
|---|---|
| CurrencyCode | GBP |
| ISIN | GB00B60BD277 |
| Sector | Consumer Cyclical |
| Industry | Apparel Manufacturing |
| PE Ratio | None |
|---|---|
| Target Price | 10.96 |
| Beta | 2.16 |
| Market Cap | 3M |
| Dividend Yield | None |
Superdry plc designs, produces, markets, and sells clothing, footwear, and accessories primarily under the Superdry brand for men and women in the United Kingdom and internationally. It operates through Retail and Wholesale segments. The Company operates through stores, concessions, various Internet sites, multi-brand independents and distributors, franchise, and license stores. It operates 213 owned, and 410 franchised and licensed stores; and 18 international websites. The company was formerly known as SuperGroup Plc and changed its name to Superdry plc in January 2018. Superdry plc was founded in 1985 and is headquartered in Cheltenham, the United Kingdom.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for SDRY.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
