title: |
Stock Trend Analysis and Trading Strategy |
|
publication: |
||
part of series: |
Advances in Intelligent Systems Research | |
ISBN: |
978-90-78677-01-7 | |
ISSN: |
1951-6851 | |
DOI: |
doi:10.2991/jcis.2006.135 (how to use a DOI) | |
author(s): |
Hongxing He, Jie Chen, Jin Huidong, Chen Shu-Heng |
|
corresponding author: |
||
publication date: |
October 2006 |
|
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: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
|
full text: |