Machine learning-based forecasting of significant daily returns in foreign exchange markets
Financial forecasting has always attracted an enormous amount of interest among researchers in quantitative analysis. The advent of modern machine learning models has introduced new tools to tackle this classical problem. In this paper, we apply machine learning algorithms to a hitherto unexplored question of forecasting instances of significant fluctuations in currency exchange rates. We carry out an extensive comparative study of ten modern machine learning methods. In our experiments, we use data on four major currency pairs over a 20-year period. A key contribution is the novel use of outlier detection methods for this purpose. Numerical experiments show that outlier detection methods substantially outperform traditional machine learning and finance techniques. In addition, we show that a recently proposed new outlier detection method PKDE produces the best overall results. Our findings hold across different currency pairs, significance levels, and time horizons indicating the robustness of the proposed method. Copyright © 2022 Inderscience Enterprises Ltd.
This work is not available in the CUD collection. The version of the scholarly record of this work is published in International Journal of Business Intelligence and Data Mining (2022), available online at: https://doi.org/10.1504/IJBIDM.2022.126505
forecasting, foreign exchange, KDE, kernel density estimation, machine learning, neural networks, outlier detection, tail events
Kamalov, F., & Gurrib, I. (2022). Machine learning-based forecasting of significant daily returns in foreign exchange markets. International Journal of Business Intelligence and Data Mining, 21(4), 465-483. doi:10.1504/ijbidm.2022.126505