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Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data

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Abstract

High-frequency data is a big data in finance in which a large amount of intra-day transactions arriving irregularly in financial markets are recorded. Given the high frequency and irregularity, such data require efficient tools to filter out the noise (i.e. jumps) arising from the anomaly, irregularity, and heterogeneity of financial markets. In this article, we use a recurrently adaptive separation algorithm, which is based on the maximal overlap discrete wavelet transform (MODWT) and that can effectively: (1) identify the time-variant jumps, (2) extract the time-consistent patterns from the noise (jumps), and (3) denoise the marginal perturbations. In addition, the proposed algorithm enables reinforcement learning to optimize a multiple-criteria decision or convex programming when reconstructing the wavelet-denoised data. Using simulated data, we show the proposed approach can perform efficiently in comparison with other conventional methods documented in the literature. We also apply our method in an empirical study by using high-frequency data from the US stock market and confirm that the proposed method can significantly improve the accuracy of predictive analytics models for financial market returns.

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Notes

  1. http://www.whartonwrds.com/datasets/nyse-taq/.

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Acknowledgements

The authors thank the journal editor and two anonymous reviewers for providing valuable comments. Some of this work was previously described at the conferences of the Asia-Pacific Association of Derivatives and the Institute for Operations Research and the Management Sciences (INFORMS). The authors thank the participants of these conferences for their valuable and insightful comments.

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Authors and Affiliations

  1. InfoTech, Frankfurt am Main, Germany

    Yi-Ting Chen

  2. School of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, ROC

    Yi-Ting Chen

  3. SKEMA Business School, Université Côte d’Azur, Nice, France

    Wan-Ni Lai

  4. KEDGE Business School, 680 Cours de la Libèration, 33405, Talance Cedex, France

    Edward W. Sun

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  1. Yi-Ting Chen
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  2. Wan-Ni Lai
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  3. Edward W. Sun
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Correspondence to Edward W. Sun.

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This work was supported by the InfoTech research project funded under USt-IdNr. DE320245686 and DAAD (Grant No. ST34-AP).

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Chen, YT., Lai, WN. & Sun, E.W. Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data. Comput Econ 54, 809–844 (2019). https://doi.org/10.1007/s10614-019-09881-3

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