Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets
Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets
复制标题
DOI：
10.1016/j.eswa.2019.01.083
复制
发表时间：
2019-07-01
影响因子：
8.5
通讯作者：
Neves, Rui Ferreira
中科院分区：
文献类型：
--
作者：
Nobre, Joao;Neves, Rui Ferreira
当投资于金融市场时，确定一个交易信号是至关重要的，它可以为投资者提供金融市场的最佳进入和退出点，然而这是一项艰巨的任务，并已成为金融领域非常受欢迎的研究课题。本文提出了一个专家系统，在金融领域，结合主成分分析（PCA），离散小波变换（DWT），极端梯度提升（XGBoost）和多目标优化遗传算法（MOO-GA），以实现高回报，低风险水平。PCA用于降低金融输入数据集的维数，DWT用于对每个特征进行降噪。然后将得到的数据集馈送到XGBoost二元分类器，该分类器的超参数由MOO-GA优化。分析了主成分分析的重要性，结果表明，它大大提高了系统的性能。为了进一步改善PCA方法的结果，将PCA和DWT方法应用于一个系统中，结果表明，该系统在五个分析的金融市场中的三个市场中均优于买入并持有（B&H）策略，投资组合的平均收益率为49.26%，而B&H策略的平均收益率为32.41%。(C)2019爱思唯尔有限公司版权所有。
When investing in financial markets it is crucial to determine a trading signal that can provide the investor with the best entry and exit points of the financial market, however this is a difficult task and has become a very popular research topic in the financial area. This paper presents an expert system in the financial area that combines Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT), Extreme Gradient Boosting (XGBoost) and a Multi-Objective Optimization Genetic Algorithm (MOO-GA) in order to achieve high returns with a low level of risk. PCA is used to reduce the dimensionality of the financial input data set and the DWT is used to perform a noise reduction to every feature. The resultant data set is then fed to an XGBoost binary classifier that has its hyperparameters optimized by a MOO-GA. The importance of the PCA is analyzed and the results obtained show that it greatly improves the performance of the system. In order to improve even more the results obtained in the system using PCA, the PCA and the DWT are then applied together in one system and the results obtained show that this system is capable of outperforming the Buy and Hold (B&H) strategy in three of the five analyzed financial markets, achieving an average rate of return of 49.26% in the portfolio, while the B&H achieves on average 32.41%. (C) 2019 Elsevier Ltd. All rights reserved.