An Ensemble De-noising Method for High Frequency Financial Data

DOI

10.2991/sekeie-14.2014.7How to use a DOI?

Keywords

Ensemble method, wavelet analysis, phase space reconstruction, independent component analysis, high-frequency financial data

Abstract

High-frequency financial data are characterized by unbalanced, non-linear and low signal-noise ratio, which often represents a challenge on the study of financial market microstructure. There has been little research on the de-noising method for high-frequency financial data, with the wavelet analysis as the current major method. Considering that the effect of wavelet analysis is restricted by the signal-noise ratio, we introduced phase space reconstruction and independent component analysis method for analyzing high-frequency financial data. The qualitative and quantitative analyses have shown that high-frequency financial data is chaotic in the time series and suitable to use the phase space reconstruction method. Furthermore, we propose the ensemble de-noising method for the high-frequency financial data. The numerical experiments results show that the de-noising effectiveness of our proposed methods is better than that of wavelet analysis. The improvement is about 2 times and more from the view of prediction precision based on the support vector machine. Our proposed ensemble de-noising method may also become a basis for general studies of financial market microstructure.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chaoyong Wang
AU  - Yanfeng Sun
PY  - 2014/03
DA  - 2014/03
TI  - An Ensemble De-noising Method for High Frequency Financial Data
BT  - Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEKEIE 2014)
PB  - Atlantis Press
SP  - 27
EP  - 33
SN  - 1951-6851
UR  - https://doi.org/10.2991/sekeie-14.2014.7
DO  - 10.2991/sekeie-14.2014.7
ID  - Wang2014/03
ER  -