Forecasting Forex Trend Indicators with Fuzzy Rough Sets

J. C. Garza Sepúlveda, F. Lopez-Irarragorri and S. E. Schaeffer ()
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J. C. Garza Sepúlveda: Universidad Autónoma de Nuevo León (UANL)
F. Lopez-Irarragorri: Universidad Autónoma de Nuevo León (UANL)
S. E. Schaeffer: Universidad Autónoma de Nuevo León (UANL)

Computational Economics, 2023, vol. 62, issue 1, No 10, 229-287

Abstract: Abstract 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.

Keywords: Foreign exchange; Fuzzy rough sets; Classification; Forecasting (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-022-10281-3

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