Comparison of Machine Learning Classifiers for Exchange Rate Trend Forecasting

Abstract

The market of foreign exchange is one of the largest markets worldwide. However, predicting the price of exchange currency pairs is a very difficult problem due to the fact that exchange rate time series demonstrate a highly non-linear and non-stationary behavior, being affected by a series of parameters which are difficult to model efficiently. This study attempts to compare five machine learning and neural network classifiers: Logistic Regression model, Support Vector Classifier, Gaussian Naive Bayes, Random Forest and Multi-layer Perceptron. The most highly correlated features are evaluated and compared for predicting the day ahead trend of the Euro-United States Dollar (EUR-USD) currency pair. Results indicate that model selection is not as significant as the combination of the most important features for the accuracy of the prediction.