-
1 Comment
Honda India Power Products Limited is currently in a long term uptrend where the price is trading 16.2% above its 200 day moving average.
From a valuation standpoint, the stock is 85.6% cheaper than other stocks from the Industrials sector with a price to sales ratio of 1.2.
Honda India Power Products Limited's total revenue rose by 20.1% to $3B since the same quarter in the previous year.
Its net income has increased by 22.2% to $209M since the same quarter in the previous year.
Finally, its free cash flow grew by 335.4% to $771M since the same quarter in the previous year.
Based on the above factors, Honda India Power Products Limited gets an overall score of 5/5.
| Exchange | NSE |
|---|---|
| CurrencyCode | INR |
| Industry | Specialty Industrial Machinery |
| ISIN | INE634A01018 |
| Sector | Industrials |
| Beta | 0.67 |
|---|---|
| Market Cap | 22B |
| PE Ratio | 36.43 |
| Target Price | None |
| Dividend Yield | 0.5% |
Honda India Power Products Limited engages in the manufacture, marketing, and sale of portable generators, water pumps, general purpose engines, lawn mowers, brush cutters, portable gensets, tillers, and marine engines in India and internationally. The company also engages in marketing of outboard motors. It also exports its products. The company was formerly known as Honda Siel Power Products Limited and changed its name to Honda India Power Products Limited in July 2020. Honda India Power Products Limited was incorporated in 1985 and is headquartered in Gautam Buddha Nagar, India.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for HONDAPOWER.NSE 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
