-
1 Comment
Namoi Cotton Limited is currently in a long term uptrend where the price is trading 6.5% above its 200 day moving average.
From a valuation standpoint, the stock is 55.8% cheaper than other stocks from the Consumer Defensive sector with a price to sales ratio of 3.0.
Namoi Cotton Limited's total revenue sank by 63.8% to $13M since the same quarter in the previous year.
Its net income has dropped by 7.7% to $-4M since the same quarter in the previous year.
Finally, its free cash flow grew by 65.5% to $-4M since the same quarter in the previous year.
Based on the above factors, Namoi Cotton Limited gets an overall score of 3/5.
Sector | Consumer Defensive |
---|---|
Industry | Farm Products |
Exchange | AU |
ISIN | AU000000NAM1 |
CurrencyCode | AUD |
Beta | 0.5 |
---|---|
Market Cap | 160M |
PE Ratio | 25.73 |
Target Price | 0.45 |
Dividend Yield | 1.9% |
Namoi Cotton Limited, together with its subsidiaries, engages in the ginning and supply chain and marketing of cotton in Australia, China, Japan, South Korea, and Thailand. It operates a network of cotton gins in New South Wales and southern Queensland. The company is also involved in the purchase and sale of lint cotton from growers. In addition, it provides warehousing and logistics services; cottonseeds and moss/mote; and cotton classing services. The company was formerly known as Namoi Cotton Co-operative Ltd and changed its name to Namoi Cotton Limited in October 2017. Namoi Cotton Limited was founded in 1962 and is headquartered in Toowoomba, Australia.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for NAM.AU 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