论文标题
基于对外汇市场大量每日回报的预测机器学习
Machine learning based forecasting of significant daily returns in foreign exchange markets
论文作者
论文摘要
在定量分析中，资产价值预测始终吸引了研究人员之间的巨大兴趣。现代机器学习模型的出现引入了解决这个经典问题的新工具。在本文中，我们将机器学习算法应用于迄今未探索的预测货币汇率波动显着波动的问题。我们使用10年的四个主要货币对的数据对九种现代机器学习算法进行分析。一个关键的贡献是为此目的使用异常检测方法的新颖使用。数值实验表明，离群检测方法基本上优于传统机器学习和金融技术。此外，我们表明，最近提出的一种新的离群检测方法PKDE可产生最佳的总体结果。我们的发现跨越不同的货币对，显着性水平以及时间范围，表明该方法的稳健性。
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.
