-
1 Comment
TESEC Corporation is currently in a long term uptrend where the price is trading 93.6% above its 200 day moving average.
From a valuation standpoint, the stock is 81.7% cheaper than other stocks from the Technology sector with a price to sales ratio of 2.5.
TESEC Corporation's total revenue sank by 3.3% to $916M since the same quarter in the previous year.
Its net income has increased by 54.5% to $-25M since the same quarter in the previous year.
Based on the above factors, TESEC Corporation gets an overall score of 3/5.
| Exchange | F |
|---|---|
| CurrencyCode | EUR |
| ISIN | None |
| Sector | |
| Industry |
| Beta | 1.75 |
|---|---|
| Market Cap | 100M |
| PE Ratio | 42.26 |
| Target Price | None |
| Dividend Yield | 58.% |
TESEC Corporation designs, develops, manufactures, and sells semiconductor test equipment in Japan and internationally. It offers various test systems, including discrete device test systems, thermal resistance testers, inductive load testers, dynamic test systems, IPD/IPM test systems, and internal gate resistance testers. The company also provides various handlers comprising micro electro mechanical systems, film frame test, gravity, pick and place, discrete, and TAB handlers, as well as die sorters. In addition, it offers after-sales services, such as tester calibration, overhaul, and training services, as well as spare parts, warranty, maintenance, and resale services. The company was formerly known as TES Corporation and changed its name to TESEC Corporation in 1980. TESEC Corporation was founded in 1969 and is headquartered in Tokyo, Japan.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for TEW.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
