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A hybrid two-stage financial stock forecasting algorithm based on clustering and ensemble learning

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Abstract

This paper investigates the problem of the stock closing price forecasting for the stock market. Based on existing two-stage fusion models in the literature, two new prediction models based on clustering have been proposed, where k-means clustering method is adopted to cluster several common technical indicators. In addition, ensemble learning has also been applied to improve the prediction accuracy. Finally, a hybrid prediction model, which combines both the k-means clustering and ensemble learning, has been proposed. The experimental results on a number of Chinese stocks demonstrate that the hybrid prediction model obtains the best predicting accuracy of the stock price. The k-means clustering on the stock technical indicators can further enhance the prediction accuracy of the ensemble learning.

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Acknowledgements

This work has been supported by National Natural Science Foundation of China under Grants 61772543, U1435222, 61625202, and 61272056.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Ying Xu & Cuijuan Yang

  2. Computer Science, National University of Defense Technology, Changsha, 410073, China

    Shaoliang Peng

  3. Graduate School of Engineering, Osaka Prefecture University, Osaka, 599-8531, Japan

    Yusuke Nojima

Authors
  1. Ying Xu
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  2. Cuijuan Yang
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  3. Shaoliang Peng
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  4. Yusuke Nojima
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Correspondence to Ying Xu.

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Xu, Y., Yang, C., Peng, S. et al. A hybrid two-stage financial stock forecasting algorithm based on clustering and ensemble learning. Appl Intell 50, 3852–3867 (2020). https://doi.org/10.1007/s10489-020-01766-5

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