文献类型：期刊文献

英文题名：Financial quantitative investment using convolutional neural network and deep learning technology

作者：Chen, Chunchun[1];Zhang, Pu[2];Liu, Yuan[3];Liu, Jun[4]

第一作者：Chen, Chunchun

通讯作者：Liu, J[1]

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

第一机构：北京联合大学管理学院

通讯机构：[1]corresponding author), Zhengzhou Univ Light Ind, Sch Econ & Management, Zhengzhou 450001, Henan, Peoples R China.

年份：2020

卷号：390

起止页码：384-390

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20194807738308);Scopus(收录号：2-s2.0-85075352010);WOS:【SCI-EXPANDED(收录号:WOS:000531728800016)】；

语种：英文

外文关键词：Financial investment; Quantitative investment; Convolutional neural network; Deep belief network

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.