Application of Support Vector Machine to Forex Monitoring

Abstract

Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

Foreign Exchange Trading with Support Vector Machines

“…Subsequent applications in time series prediction (Müller et al (1999)) indicate the potential that SVMs have with respect to economics and finance. In predicting Australian foreign exchange rates, Kamruzzaman and Sarker (2003b) showed that a moving average-trained SVM has advantages over an Artificial Neural Network (ANN) based model, which was shown to have advantages over ARIMA models (2003a). Furthermore, Kamruzzaman et al (2003) had a closer look at SVM regression and investigated how it performs with different standard kernel functions.…”