Stock Trend Analysis and Trading Strategy
Hongxing He 0, Jie Chen, Jin Huidong, Chen Shu-Heng
0Mathematical and Information Sciences, CSIRO, Australia
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- https://doi.org/10.2991/jcis.2006.135How to use a DOI?
- Data Mining, Clustering, k-means, Time Series, Stock Trading
- 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.
- 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 -