Bias effect on predicting market trends with EMD.
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Date
2017
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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.
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Keywords
Finance, Time series, Machine learning, Trend prediction
Citation
FURLANETO, D. C. et al. Bias effect on predicting market trends with EMD. Bias effect on predicting market trends with EMD. Expert Systems With Applications, v. 1, p. 19-26, 2017. Disponível em: <http://www.sciencedirect.com/science/article/pii/S0957417417302087>. Acesso em: 16 jan. 2018.