Abstract
This paper outlines a data mining approach to the analysis and prediction of the trend of stock prices. The approach consists of three steps, namely, partitioning, analysis and prediction. A 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. Using our trend prediction methodology, we propose a trading strategy TTP (Trading based on Trend Prediction). Some results of applying TTP to stock trading are reported. The trading performance is compared with some practical trading strategies and other machine learning methods. Given the volatility nature of stock prices the methodology achieved limited success for a few countries and time periods. Further analysis of the results may lead to further improvement in the methodology. Although the proposed approach is designed for stock trading, it can be applied to the trend analysis of any time series, such as the time series of economic indicators.
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He, H., Chen, J., Jin, H., Chen, SH. (2007). Trading Strategies Based on K-means Clustering and Regression Models. In: Chen, SH., Wang, P.P., Kuo, TW. (eds) Computational Intelligence in Economics and Finance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72821-4_7
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DOI: https://doi.org/10.1007/978-3-540-72821-4_7
Publisher Name: Springer, Berlin, Heidelberg
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