-
1 Comment
SNT Corporation is currently in a long term uptrend where the price is trading 7.4% above its 200 day moving average.
From a valuation standpoint, the stock is 38.8% cheaper than other stocks from the Industrials sector with a price to sales ratio of 0.7.
SNT Corporation's total revenue sank by 13.5% to $4B since the same quarter in the previous year.
Its net income has increased by 77.4% to $218M since the same quarter in the previous year.
Finally, its free cash flow grew by 145.2% to $335M since the same quarter in the previous year.
Based on the above factors, SNT Corporation gets an overall score of 4/5.
Sector | Industrials |
---|---|
Industry | Metal Fabrication |
Exchange | TSE |
CurrencyCode | JPY |
ISIN | JP3379600004 |
Market Cap | 15B |
---|---|
Dividend Yield | None |
Target Price | 740 |
Beta | 0.28 |
SNT Corporation manufactures and sells forging parts, scaffolding parts, and logistics equipment in Japan and internationally. The company offers forged products, including tube, knuckle, connecting rod, pinion, gear, camshaft, sprocket, axle shafts, trunnion socket, bracket, and lower rotation shaft. It also manufactures and sells frame scaffolding and supporting materials for use at construction sites, as well as leases temporary construction equipment. In addition, the company engages in steel palette logistics containers for use in transportation related business. The company was formerly known as Higashinihon-Tanko Co., Ltd. and changed its name to SNT Corporation in 1990. SNT Corporation was incorporated in 1948 and is headquartered in Kawasaki, Japan.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 6319.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