STOCK MARKET FORECASTING BASED ON WAVELET AND LEAST SQUARES SUPPORT VECTOR MACHINE

Xia Liang, Haoran Zhu, Xun Liang

Abstract

In this paper, we propose a novel method using wavelet transform to denoise the input of least squares support vector machine for classification of closing price of stocks. The proposed method classifies closing price as either down or up. We have tested the proposed approach using passed three-year data of 10 stocks randomly selected from sample stock of hs300 index and compared the proposed method with other machine learning methods. Good classification percentage of almost 99% was achieved by WT-SVM model. We observed that the performance of stock price prediction can be significantly enhanced by using hybrized WT in comparison with a single model.

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Paper Citation


in Harvard Style

Liang X., Zhu H. and Liang X. (2011). STOCK MARKET FORECASTING BASED ON WAVELET AND LEAST SQUARES SUPPORT VECTOR MACHINE . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-54-6, pages 46-53. DOI: 10.5220/0003436500460053


in Bibtex Style

@conference{iceis11,
author={Xia Liang and Haoran Zhu and Xun Liang},
title={STOCK MARKET FORECASTING BASED ON WAVELET AND LEAST SQUARES SUPPORT VECTOR MACHINE},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2011},
pages={46-53},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003436500460053},
isbn={978-989-8425-54-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - STOCK MARKET FORECASTING BASED ON WAVELET AND LEAST SQUARES SUPPORT VECTOR MACHINE
SN - 978-989-8425-54-6
AU - Liang X.
AU - Zhu H.
AU - Liang X.
PY - 2011
SP - 46
EP - 53
DO - 10.5220/0003436500460053