-
1 Comment
Golden Tobacco Limited is currently in a long term uptrend where the price is trading 23.7% above its 200 day moving average.
From a valuation standpoint, the stock is 16.2% more expensive than other stocks from the Consumer Defensive sector with a price to sales ratio of 4.0.
Golden Tobacco Limited's total revenue rose by 75.3% to $47M since the same quarter in the previous year.
Its net income has dropped by 103.9% to $-5M since the same quarter in the previous year.
Finally, its free cash flow fell by 55.4% to $13M since the same quarter in the previous year.
Based on the above factors, Golden Tobacco Limited gets an overall score of 2/5.
Sector | Consumer Defensive |
---|---|
ISIN | INE973A01010 |
Industry | Tobacco |
Exchange | NSE |
CurrencyCode | INR |
Market Cap | 654M |
---|---|
PE Ratio | 14.02 |
Beta | 0.26 |
Target Price | None |
Dividend Yield | None |
Golden Tobacco Limited engages in the manufacturing, purchasing, processing, selling, and marketing tobacco and tobacco related products in India. The company offers cigarettes, slim/super slim cigarettes, cigars, and cigarillos products, as well as packed type cigarettes. It sells its products under the Panama, Chancellor, Golden's Gold Flake, Taj Chhap, Style, Esquire, Flair, June, and Just Black brand names. The company also exports its products to the United States, Europe, Russia, Singapore, Cambodia, Japan, the Middle East, and internationally. Golden Tobacco Limited was founded in 1930 and is based in Vadodara, India.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for GOLDENTOBC.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