Bias effect on predicting market trends with EMD.

Abstract

Financial time series are notoriously difficult to analyze and predict, given their non-stationary, highly oscillatory nature. In this study, we evaluate the effectiveness of the Ensemble Empirical Mode Decom- position (EEMD), the ensemble version of Empirical Mode Decomposition (EMD), at generating a rep- resentation for market indexes that improves trend prediction. Our results suggest that the promising results reported using EEMD on financial time series were obtained by inadvertently adding look-ahead bias to the testing protocol via pre-processing the entire series with EMD, which affects predictive re- sults. In contrast to conclusions found in the literature, our results indicate that the application of EMD and EEMD with the objective of generating a better representation for financial time series is not suffi- cient to improve the accuracy or cumulative return obtained by the models used in this study.