-
1 Comment
Daio Paper Corporation is currently in a long term uptrend where the price is trading 0.3% above its 200 day moving average.
From a valuation standpoint, the stock is 20.6% cheaper than other stocks from the Basic Materials sector with a price to sales ratio of 0.6.
Daio Paper Corporation's total revenue rose by 11.8% to $151B since the same quarter in the previous year.
Its net income has increased by 63.5% to $7B since the same quarter in the previous year.
Based on the above factors, Daio Paper Corporation gets an overall score of 4/5.
| Exchange | TSE |
|---|---|
| CurrencyCode | JPY |
| ISIN | JP3440400004 |
| Sector | Basic Materials |
| Industry | Paper & Paper Products |
| Dividend Yield | 1.6% |
|---|---|
| PE Ratio | None |
| Beta | 0.21 |
| Target Price | 906.6667 |
| Market Cap | 151B |
Daio Paper Corporation engages in paper and paperboard business Japan, East Asia, Southeast Asia, Brazil, and internationally. It operates through Paper and Paperboard Home and Personal Care, and Others segments. The company offers newsprint, publication paper, printing paper, communication paper, recycled-content colored construction paper, wrapping paper; containerboard and corrugated container; functional materials; and home and personal care products, such as disposable diapers for babies and adults, sanitary napkins, absorbent liners for light incontinence, various tissue products, wet wipes, and cleaning supplies. It is also involved in research and development of cellulose nanofiber. Daio Paper Corporation was incorporated in 1943 and is headquartered in Tokyo, Japan.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 3880.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 2026