-
1 Comment
Nishimoto Co., Ltd is currently in a long term uptrend where the price is trading 9.3% above its 200 day moving average.
From a valuation standpoint, the stock is 71.4% cheaper than other stocks from the Consumer Defensive sector with a price to sales ratio of 0.3.
Nishimoto Co., Ltd's total revenue sank by 2.2% to $45B since the same quarter in the previous year.
Its net income has increased by 2467.3% to $1B since the same quarter in the previous year.
Based on the above factors, Nishimoto Co., Ltd gets an overall score of 3/5.
| Sector | Consumer Defensive |
|---|---|
| Industry | Food Distribution |
| Exchange | TSE |
| CurrencyCode | JPY |
| ISIN | JP3659350007 |
| Dividend Yield | 2.1% |
|---|---|
| PE Ratio | None |
| Target Price | 2266.7 |
| Market Cap | 82B |
| Beta | 0.15 |
Nishimoto Co., Ltd., together with its subsidiaries, engages in wholesale and distribution of Asian food products and ingredients worldwide. The company operates through Asian Food Global Business, Agricultural and Fishery Product Trading Company Business, and Other Business. It imports and wholesales perishable and frozen processed fruits and vegetables, marine products, etc. to restaurants and food manufacturers, as well as exports produced vegetables and fruits. The company engages in original product sales business using foreign brand foods and characters; sale of supplements, etc.; and catalogue mail order business. Nishimoto Co., Ltd. was formerly known as Nishimoto Trading Holding Co., Ltd. and changed its name to Nishimoto Co., Ltd. in January 2015. The company was founded in 1912 and is headquartered in Kobe, Japan.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 9260.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
