-
1 Comment
Taikisha Ltd is currently in a long term uptrend where the price is trading 10.3% above its 200 day moving average.
From a valuation standpoint, the stock is 56.3% cheaper than other stocks from the Industrials sector with a price to sales ratio of 0.5.
Taikisha Ltd's total revenue rose by 0.5% to $54B since the same quarter in the previous year.
Its net income has dropped by 36.6% to $2B since the same quarter in the previous year.
Based on the above factors, Taikisha Ltd gets an overall score of 3/5.
Exchange | TSE |
---|---|
CurrencyCode | JPY |
ISIN | JP3441200007 |
Sector | Industrials |
Industry | Building Products & Equipment |
Market Cap | 183B |
---|---|
PE Ratio | 17.12 |
Target Price | 3008.3333 |
Dividend Yield | 2.5% |
Beta | 0.19 |
Taikisha Ltd. engages in the environmental systems and painting systems businesses in Japan. The company is involved in the design, supervision and construction of building air conditioning equipment for general offices and industrial air conditioning equipment for production facilities in factories and research laboratories; and manufacture and sale of related equipment and materials. It also engages in the design, supervision, and construction of painting equipment related to the automobile industry, as well as related manufacturing and selling equipment and materials. In addition, the company designs and constructs HVAC systems for various facilities, factories, and research and development facilities. Taikisha Ltd. was founded in 1913 and is headquartered in Tokyo, Japan.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 1979.TSE 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