Title: Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data
Authors: Chen, Yi-Ting
Lai, Wan-Ni
Sun, Edward W.
資訊學院
College of Computer Science
Keywords: Convex optimization;Forecasting;Jump detection;High-frequency data;Reinforcement learning;Wavelet
Issue Date: 1-Aug-2019
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.
URI: http://dx.doi.org/10.1007/s10614-019-09881-3
http://hdl.handle.net/11536/152666
ISSN: 0927-7099
DOI: 10.1007/s10614-019-09881-3
Journal: COMPUTATIONAL ECONOMICS
Volume: 54
Issue: 2
Begin Page: 809
End Page: 844
Appears in Collections:Articles