  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.
