-
1 Comment
Grammer AG is currently in a long term uptrend where the price is trading 14.6% above its 200 day moving average.
From a valuation standpoint, the stock is 99.7% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 0.2.
Grammer AG's total revenue sank by 5.6% to $462M since the same quarter in the previous year.
Its net income has dropped by 95.2% to $737K since the same quarter in the previous year.
Finally, its free cash flow grew by 1079.4% to $82M since the same quarter in the previous year.
Based on the above factors, Grammer AG gets an overall score of 3/5.
| Exchange | F |
|---|---|
| CurrencyCode | EUR |
| ISIN | DE0005895403 |
| Sector | Consumer Cyclical |
| Industry | Auto Parts |
| Market Cap | 95M |
|---|---|
| Beta | 0.88 |
| Target Price | 13.7 |
| PE Ratio | None |
| Dividend Yield | None |
Grammer AG engages in the development, production, and sale of components and systems for automotive interiors worldwide. The Automotive business supplies headrests, armrests, center console systems, interior components and operating elements, and thermoplastic solutions to automotive OEMs and their system suppliers. The Commercial Vehicles business develops and produces driver and passenger seats for trucks, tractors, construction machinery, and forklifts for commercial vehicles OEMs, bus and rolling stock OEMs, and railway operators; and seats and seating systems for trains and buses. The company was founded in 1880 and is headquartered in Ursensollen, Germany. Grammer AG is a subsidiary of Ningbo Jifeng Auto Parts Co., Ltd.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for GMM.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
