-
1 Comment
International Personal Finance PLC is currently in a long term uptrend where the price is trading 35.1% above its 200 day moving average.
International Personal Finance PLC's total revenue sank by 32.4% to $299M since the same quarter in the previous year.
Its net income has dropped by 107.0% to $-3M since the same quarter in the previous year.
Finally, its free cash flow grew by 717.4% to $268M since the same quarter in the previous year.
Based on the above factors, International Personal Finance PLC gets an overall score of 2/5.
| ISIN | GB00B1YKG049 |
|---|---|
| Sector | Financial Services |
| Industry | Credit Services |
| CurrencyCode | PLN |
| Exchange | WAR |
| PE Ratio | 43.75 |
|---|---|
| Beta | 1.86 |
| Market Cap | 1B |
| Dividend Yield | 3.6% |
| Target Price | 308.5 |
International Personal Finance plc, together with its subsidiaries, provides consumer credit in Europe and Mexico. It offers home credit products, such as home credit cash loans with agent service; money transfer loans direct to bank account; home, medical, and life insurance; micro-business loans; and provident-branded digital loans, as well as repayment facility. The company also provides digital loan products, including instalment and monthly repayments loans, online application, and revolving credit facility, as well as mobile wallet services. It offers its products under the Provident, Credit24, hapiloans, and Creditea brands. International Personal Finance plc was founded in 1997 and is headquartered in Leeds, the United Kingdom.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for IPF.WAR 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
