Gradient Boost with Convolution Neural Network for Stock Forecast
Machine Learning
2019-09-23 v1 Computational Engineering, Finance, and Science
Statistical Finance
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.
Keywords
Cite
@article{arxiv.1909.09563,
title = {Gradient Boost with Convolution Neural Network for Stock Forecast},
author = {Jialin Liu and Chih-Min Lin and Fei Chao},
journal= {arXiv preprint arXiv:1909.09563},
year = {2019}
}
Comments
UKCL2019.11pages