9th Joint International Conference on Information Sciences (JCIS-06)

Stock Trend Analysis and Trading Strategy

Authors
Hongxing He 0, Jie Chen, Jin Huidong, Chen Shu-Heng
Corresponding Author
Hongxing He
0Mathematical and Information Sciences, CSIRO, Australia
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DOI
https://doi.org/10.2991/jcis.2006.135How to use a DOI?
Keywords
Data Mining, Clustering, k-means, Time Series, Stock Trading
Abstract
This paper outlines a data mining approach to analysis and prediction of the trend of stock prices. The approach consists of three steps, namely partitioning, analysis and prediction. A modification of the commonly used k-means clustering algorithm is used to partition stock price time series data. After data partition, linear regression is used to analyse the trend within each cluster. The results of the linear regression are then used for trend prediction for windowed time series data. The approach is efficient and effective at predicting forward trends of stock prices. Using our trend prediction methodology, we propose a trading strategy TTP (Trading based on Trend Prediction). Some preliminary results of applying TTP to stock trading are reported.
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Publication Date
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ISBN
978-90-78677-01-7
DOI
https://doi.org/10.2991/jcis.2006.135How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hongxing He
AU  - Jie Chen
AU  - Jin Huidong
AU  - Chen Shu-Heng
PY  - NaN/NaN
DA  - NaN/NaN
TI  - Stock Trend Analysis and Trading Strategy
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/jcis.2006.135
DO  - https://doi.org/10.2991/jcis.2006.135
ID  - HeNaN/NaN
ER  -