-
1 Comment
DaikyoNishikawa Corp is currently in a long term uptrend where the price is trading 29.2% above its 200 day moving average.
From a valuation standpoint, the stock is 96.8% cheaper than other stocks from the Other sector with a price to sales ratio of 0.4.
DaikyoNishikawa Corp's total revenue rose by 0.9% to $43B since the same quarter in the previous year.
Its net income has increased by 72.4% to $1B since the same quarter in the previous year.
Based on the above factors, DaikyoNishikawa Corp gets an overall score of 4/5.
| Sector | Consumer Cyclical |
|---|---|
| Industry | Auto Parts |
| Exchange | F |
| CurrencyCode | EUR |
| ISIN | JP3481300006 |
| Beta | 0.37 |
|---|---|
| Dividend Yield | 4.8% |
| Market Cap | 329M |
| PE Ratio | 9.09 |
| Target Price | None |
DaikyoNishikawa Corporation develops, manufactures, and sells automotive parts and synthetic plastic products in Japan. The company offers bumpers, radiator grilles and shroud panels; instrument and decoration panels, rear consoles, and indicators; and engine covers, oil strainers, and intake manifolds. It also provides housing-related plastic parts comprising bath unit parts, as well as gas pipes and iron-core stair mats. In addition, the company engages in the ownership, leasing, and management of movable and immovable property; collecting, transporting, and treating industrial waste; non-life insurance agency business and business related to life insurance solicitation; and provision of incidental services. DaikyoNishikawa Corporation was founded in 1953 and is headquartered in Hiroshima, Japan.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for DK8.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 2026