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Improving model performance with the integrated wavelet denoising method

  • Yi-Ting Chen , Edward W. Sun EMAIL logo and Min-Teh Yu EMAIL logo
Published/Copyright: January 10, 2015

Abstract

Intelligent pattern recognition imposes new challenges in high-frequency financial data mining due to its irregularities and roughness. Based on the wavelet transform for decomposing systematic patterns and noise, in this paper we propose a new integrated wavelet denoising method, named smoothness-oriented wavelet denoising algorithm (SOWDA), that optimally determines the wavelet function, maximal level of decomposition, and the threshold rule by using a smoothness score function that simultaneously detects the global and local extrema. We discuss the properties of our method and propose a new evaluation procedure to show its robustness. In addition, we apply this method both in simulation and empirical investigation. Both the simulation results based on three typical stylized features of financial data and the empirical results in analyzing high-frequency financial data from Frankfurt Stock Exchange confirm that SOWDA significantly (based on the RMSE comparison) improves the performance of classical econometric models after denoising the data with the discrete wavelet transform (DWT) and maximal overlap discrete wavelet transform (MODWT) methods.


Corresponding authors: Edward W. Sun, KEDGE Business School, 680 Cours de la Libèration, 33405 Talance Cedex, France, e-mail: ; and Min-Teh Yu, National Chiao Tung University, No. 1001, Daxue Rd., Hsinchu 530010, Taiwan, e-mail:

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Supplemental Material

The online version of this article (DOI: 10.1515/snde-2014-0057) offers supplementary material, available to authorized users.


Published Online: 2015-1-10
Published in Print: 2015-9-1

©2015 by De Gruyter

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