Abstract
An automatic stock market categorization system would be invaluable to investors and financial experts, providing them with the opportunity to predict a stock price changes with respect to the other stocks. In recent years, clustering all companies in the stock markets based on their similarities in shape of the stock market has increasingly become popular. However, existing approaches may not be practical because the stock price data are high-dimensional data and the changes in the stock price usually occur with shift, which makes the categorization more complex. In this paper, a hierarchical beta process (HBP) based approach is proposed for stock market trend prediction. Preliminary results show that the approach is promising and outperforms other popular approaches.
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Ghanavati, M., Wong, R.K., Chen, F., Wang, Y., Lee, J. (2016). A Hierarchical Beta Process Approach for Financial Time Series Trend Prediction. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_19
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DOI: https://doi.org/10.1007/978-3-319-42996-0_19
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