Gradient Boost with Convolution Neural Network for Stock Forecast

Jialin Liu, Chih-Min Lin and Fei Chao

Papers from arXiv.org

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.

Date: 2019-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1909.09563

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