-
1 Comment
Amcorp Global Limited is currently in a long term downtrend where the price is trading 15.7% below its 200 day moving average.
From a valuation standpoint, the stock is 93.6% cheaper than other stocks from the Real Estate sector with a price to sales ratio of 0.6.
Amcorp Global Limited's total revenue sank by 21.2% to $16M since the same quarter in the previous year.
Its net income has dropped by 312.2% to $-7M since the same quarter in the previous year.
Finally, its free cash flow fell by 75.1% to $4M since the same quarter in the previous year.
Based on the above factors, Amcorp Global Limited gets an overall score of 1/5.
Industry | Real Estate-Development |
---|---|
ISIN | SG2F79993489 |
Sector | Real Estate |
Exchange | SG |
CurrencyCode | SGD |
Target Price | None |
---|---|
PE Ratio | None |
Beta | 0.05 |
Dividend Yield | 0.0% |
Market Cap | 54M |
Amcorp Global Limited, an investment holding company, operates as a real estate developer and investor in Singapore, Malaysia, and Australia. The company operates through three segments: Property Development, Hotel Operations, and Investment Properties. It undertakes residential, commercial, and industrial property development projects, as well as invests in properties, such as hotels in Australia. The company was formerly known as TEE Land Limited and changed its name to Amcorp Global Limited in April 2020. Amcorp Global Limited was incorporated in 2012 and is based in Singapore. Amcorp Global Limited operates as a subsidiary of Amcorp Supreme Pte. Ltd.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for S9B.SG 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 2024