Sliding window-based analysis of multiple foreign exchange trading systems by using soft computing techniques

Considerable effort has been made by researchers from various areas of science to forecast financial time series such as stock market and foreign exchange market. Recent studies have shown that the market can be outperformed by trading systems built with soft computing techniques. This paper aims to compare different trading systems based on support vector regression (SVR), growing hierarchical self-organizing maps (GHSOM) and genetic algorithms (G A) when tested against nine currency pairs of the foreign exchange market (Forex). The experiments were performed using the sliding window strategy. The results showed that the GA-based trading systems outperformed the SVR+GHSOM model when evaluated by four performance metrics, including an statistical test.