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Gradient Boost with Convolution Neural Network for Stock Forecast

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arxiv 1909.09563 v1 pith:PA5Z2US2 submitted 2019-09-19 cs.LG cs.CEq-fin.ST

Jialin Liu , Chih-Min Lin , Fei Chao This is my paper · ORCID
classification cs.LGcs.CEq-fin.ST
keywords economyforecaststockmarketmethodstasklearningmethod
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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