Bias effect on predicting market trends with EMD. (1st October 2017)

• Record Type: Journal Article
• Authors: Carnelossi Furlaneto, Dennis; Oliveira, Luiz S.; Menotti, David; Cavalcanti, George D.C.
• Is Part Of: Expert systems with applications. Volume 82(2017)
• Page Start: 19
• Page End: 26
• Publication Date: 2017-10-01
• Subjects: Finance -- Time series -- EEMD -- Trend prediction -- Machine learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.03.053
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Ensemble Empirical Mode Decomposition is used for trend prediction of market indexes. Using EMD as a pre-processing step is shown to add look-ahead bias. We designed a protocol that eliminates look-ahead bias. 8 market indexes and 4 different models were tested as part of the study. In contrast with published results, EEMD did not prove to be advantageous in general. 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 Decomposition (EEMD), the ensemble version of Empirical Mode Decomposition (EMD), at generating a representation 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 results. 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 sufficient to improve the accuracy or cumulative return obtained by the models used in this study.