Forecasting Forex Trend Indicators with Fuzzy Rough Sets

We propose a machine-learning approach for Forex prices that forecasts trends in terms of whether or not the closing price will change for more than a threshold and whether that change is an increase or a decrease. Instead of using the prices as such, we carry out the forecast solely in terms of indicators that are popular among small-scale traders; our goal is to determine whether these convey sufficient information for a precise forecast for different change thresholds and horizons. Fuzzy rough sets are used to represent and select among multiple economic indicators and to construct a classifier to forecast price changes. High-quality forecasts are feasible for short horizons and for small thresholds of change for all fifteen currency pairs studied in the experiments.