2020/09/21 by Firuz Kamalov, Kamalov, Firuz, Ikhlaas Gurrib +1
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Currency Recognition and Detection #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Market Dynamics and Volatility #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2009.10065
openalex publication_date 2020/09/21 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Asset value 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 hitherto unexplored question of forecasting instances of significant fluctuations in currency exchange rates. We perform analysis of nine modern machine learning algorithms using data on four major currency pairs over a 10 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 best overall results. Our findings hold across different currency pairs, significance levels, and time horizons indicating the robustness of the proposed method.