Abstract
In the Forex market, the price of the currencies increases and decreases rapidly based on many economic and political factors such as commercial balance, the growth index, the inflation rate, and the employment indicators. Having a good strategy to buy and sell can make a profit from the above changes. A successful strategy in Forex should take into consideration the relation between benefits and risks. In this work, we propose an intraweek foreign exchange speculation strategy for currency markets based on a combination of technical indicators. This system has a two-level decision and is composed of the Probit regression model and rules discovery using Random Forest. There are two minimum requirements for a trading strategy: a rule to enter the market and a rule to exit it. Our proposed system, to enter the currency market, should validate two conditions. First, it should validate Random Forest access rules over the following week while in the second one the predicted value of the next day using Probit should be positive. To exit the currency market just one negative warning from Probit or Random Forest is enough. This system was used to develop dynamic portfolio trading systems. The profitability of the model was examined for USD/(EUR, JYN, BRP) variation within the period from January 2014 to January 2016. The proposed system allows improving the prediction accuracy. This indicates a good prediction of the behavior market and it helps to identify the good times to enter it or to leave it.
Highlights
The strong fluctuations in the financial markets make the stock market a risky area for investors
The last decade has witnessed an increase in the reliance on artificial intelligence prediction in different markets such as the Foreign Exchange (Forex) market, including Random Forest (RF), Linear Regression, Genetic algorithms (GA), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) approach to analysis and forecasting of financial time series
In order to justify our choice of using Random Forest classifier, we evaluate the results obtained over 90 days by Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Random Forest algorithm
Summary
The strong fluctuations in the financial markets make the stock market a risky area for investors. Speculation is considered as a balancing factor that regulates supply and demand, while leading to price equilibrium consistent with the real state of the economy This strategy is based on buying when the price is lower than the average value and selling when the price is higher. We propose a secured investment strategy in two stages: Firstly, we have opted for a temporal approach without any prescriptive hypothesis on financial market trends. This approach is based only on the observations made on the evolution of exchange rates and various temporary indicators. This strategy is a means of confidence to determine the market direction
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