Forecasting NIKKEI 225 index with support vector machine (Q1433515)

This paper investigates the predictability of financial movement direction with a Support Vector Machine (SVM) by forecasting the weekly movement direction of the NIKKEI 225 index. The forecasting ability of SVMs is evaluated by comparing its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Neural Networks. Experimental results show that SVMs outperform these classification methods.