-
1 Comment
Wolford Aktiengesellschaft is currently in a long term uptrend where the price is trading 8.3% above its 200 day moving average.
From a valuation standpoint, the stock is 99.2% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 0.5.
Wolford Aktiengesellschaft's total revenue sank by 0.0% to $30M since the same quarter in the previous year.
Its net income has dropped by 0.0% to $-6M since the same quarter in the previous year.
Finally, its free cash flow fell by 193.9% to $-9M since the same quarter in the previous year.
Based on the above factors, Wolford Aktiengesellschaft gets an overall score of 2/5.
Exchange | F |
---|---|
CurrencyCode | EUR |
ISIN | AT0000834007 |
Sector | Consumer Cyclical |
Industry | Apparel Manufacturing |
Market Cap | 56M |
---|---|
Beta | 0.1 |
PE Ratio | None |
Target Price | None |
Dividend Yield | None |
Wolford Aktiengesellschaft produces and markets skinwear for women in Austria, Germany, the United Kingdom, Ireland, France, Italy, rest of Europe, North America, Asia, and Oceania. The company offers legwear, lingerie, dresses, and sweaters. It also provides ready-to-wear products comprising body-hugging products, such as bodysuits and shirts; beachwear; and accessories consisting of scarves and belts. The company sells its products through boutiques, department stores, concession shop-in-shops, specialty retailers, online, private labels, and factory outlets. The company was founded in 1950 and is headquartered in Bregenz, Austria. Wolford Aktiengesellschaft is a subsidiary of FFG Wisdom (Luxembourg) S.à r.l.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for WOF.F 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