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Gradient Boost with Convolution Neural Network for Stock Forecast

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  • Jialin Liu
  • Chih-Min Lin
  • Fei Chao

Abstract

Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, which lead economy forecasting to become a challenging task. Ensemble learning and deep learning are the most methods to solve the stock forecast task. In this paper, we present a model combining the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The experimental results on six market indexes show that the proposed method has better performance against current popular methods.

Suggested Citation

  • Jialin Liu & Chih-Min Lin & Fei Chao, 2019. "Gradient Boost with Convolution Neural Network for Stock Forecast," Papers 1909.09563, arXiv.org.
    • Handle: RePEc:arx:papers:1909.09563
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    References listed on IDEAS

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    1. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
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    Cited by:

    1. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.

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