Financial quantitative investment using convolutional neural network and deep learning technology

被引:34
|
作者
Chen, Chunchun [1 ]
Zhang, Pu [2 ]
Liu, Yuan [3 ]
Liu, Jun [4 ]
机构
[1] Beijing Union Univ, Sch Management, Beijing 100101, Peoples R China
[2] China Dev Bank, Planning & Dev Off, Hebei Branch, Shijiazhuang, Hebei, Peoples R China
[3] Univ Int Business & Econ, Sch Banking & Finance, Beijing 100029, Peoples R China
[4] Zhengzhou Univ Light Ind, Sch Econ & Management, Zhengzhou 450001, Henan, Peoples R China
关键词
Financial investment; Quantitative investment; Convolutional neural network; Deep belief network;
D O I
10.1016/j.neucom.2019.09.092
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In order to make financial investment more stable and more profitable, convolutional neural network (CNN) and deep learning technology are used to quantify financial investment, so as to obtain more robust investment and returns. With the continuous development of in-depth learning technology, people are applying it more and more widely. Deep learning is put forward on the basis of neural network. It contains more hidden layers, shows more powerful learning ability, and can abstract data at a higher level, so as to obtain more accurate data. CNN is a multi-layer network structure which simulates the operation mechanism of biological vision system. Its special structure can obtain more useful feature descriptions from original data and is very effective in extracting data. Therefore, in this study, the two technologies are combined to quantify financial investment. The results show that the convolution neural network and deep learning algorithm can obtain relatively accurate investment strategies, thus ensuring investment returns and reducing investment risks. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:384 / 390
页数:7
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