Currency Exchange Rate Forecasting Using Support Vector Classifiers

Currency exchange markets are among the largest markets worldwide. Due to their decentrilised nature and the variation of the engaged stakeholders (i.e., from big funds and corporates to small enterprises and citizens), the currency exchange rate forecasting is a challenging task, crucial in decision making and democratisation of this domain. The main objective of this paper is to explore dependence patterns among various econometric time series, directly, or indirectly, related to currency exchange rate time series. Thereafter, on top of this dependence analysis, we deliver machine learning algorithms (e.g., support vector classifiers) to predict future values of currency exchange rates. Under this framework, the notions of time series autocorrelation and cross-correlation are utilised. However, both exhibit limitations because they cannot depict non-linear dependances. To tackle this caveat, we analyse the absolute values of the returns (i.e., percentage change) of the examined time series. The efficiency of the proposed procedure is demonstrated with eight currency exchange rate time series and eleven exogenous econometric time series related to the former. Finally, we trained eight support vector classifiers, one for each currency pair, all of which reached an accuracy level of approximately 80%.