A denoising method based on the nonlinear relationship between the target variable and input features

Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. •The new denoising method is data-driven.•It outperforms prevalent state-of-the-art denoising methods.•It increases prediction accuracies of machine learning methods by 10% or more.•We get accuracies up to 70% of trend predictions for major Asian stock indexes.