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Forecasting Forex Trend Indicators with Fuzzy Rough Sets

Author

Listed:
  • 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))

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.

Suggested Citation

  • J. C. Garza Sepúlveda & F. Lopez-Irarragorri & S. E. Schaeffer, 2023. "Forecasting Forex Trend Indicators with Fuzzy Rough Sets," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 229-287, June.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:1:d:10.1007_s10614-022-10281-3
    DOI: 10.1007/s10614-022-10281-3
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    References listed on IDEAS

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