Stock Trend Analysis and Trading Strategy

DOI

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.

Copyright

© 2006, 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  - Hongxing He
AU  - Jie Chen
AU  - Jin Huidong
AU  - Chen Shu-Heng
PY  - 2006/10
DA  - 2006/10
TI  - Stock Trend Analysis and Trading Strategy
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.135
DO  - 10.2991/jcis.2006.135
ID  - He2006/10
ER  -