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
相关论文
共 50 条
  • [31] Deep Co-investment Network Learning for Financial Assets
    Wang, Yue
    Zhang, Chenwei
    Wang, Shen
    Yu, Philip S.
    Bai, Lu
    Cui, Lixin
    2018 9TH IEEE INTERNATIONAL CONFERENCE ON BIG KNOWLEDGE (ICBK), 2018, : 41 - 48
  • [32] Exploration of Stock Portfolio Investment Construction Using Deep Learning Neural Network
    Xie, Zizheng
    Wang, Yi
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [33] Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network
    Jeong Hyun Lee
    Ijin Joo
    Tae Wook Kang
    Yong Han Paik
    Dong Hyun Sinn
    Sang Yun Ha
    Kyunga Kim
    Choonghwan Choi
    Gunwoo Lee
    Jonghyon Yi
    Won-Chul Bang
    European Radiology, 2020, 30 : 1264 - 1273
  • [34] Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network
    Lee, Jeong Hyun
    Joo, Ijin
    Kang, Tae Wook
    Paik, Yong Han
    Sinn, Dong Hyun
    Ha, Sang Yun
    Kim, Kyunga
    Choi, Choonghwan
    Lee, Gunwoo
    Yi, Jonghyon
    Bang, Won-Chul
    EUROPEAN RADIOLOGY, 2020, 30 (02) : 1264 - 1273
  • [35] Reverse Image Search Using Deep Unsupervised Generative Learning and Deep Convolutional Neural Network
    Kiran, Aqsa
    Qureshi, Shahzad Ahmad
    Khan, Asifullah
    Mahmood, Sajid
    Idrees, Muhammad
    Saeed, Aqsa
    Assam, Muhammad
    Refaai, Mohamad Reda A.
    Mohamed, Abdullah
    APPLIED SCIENCES-BASEL, 2022, 12 (10):
  • [36] Detecting brain tumors using deep learning convolutional neural network with transfer learning approach
    Anjum, Sadia
    Hussain, Lal
    Ali, Mushtaq
    Alkinani, Monagi H.
    Aziz, Wajid
    Gheller, Sabrina
    Abbasi, Adeel Ahmed
    Marchal, Ali Raza
    Suresh, Harshini
    Duong, Tim Q.
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2022, 32 (01) : 307 - 323
  • [37] Tea disease recognition technology based on a deep convolutional neural network feature learning method
    Feng, Yuhan
    INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2024, 19 (01) : 15 - 27
  • [38] IMPROVING DEEP REINFORCEMENT LEARNING FOR FINANCIAL TRADING USING NEURAL NETWORK DISTILLATION
    Tsantekidis, Avraam
    Passalis, Nikolaos
    Tefas, Anastasios
    PROCEEDINGS OF THE 2020 IEEE 30TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2020,
  • [39] INVERSE DESIGN OF AIRFOILS USING CONVOLUTIONAL NEURAL NETWORK AND DEEP NEURAL NETWORK
    Kumar, Amit
    Vadlamani, Nagabhushana Rao
    PROCEEDINGS OF ASME 2021 GAS TURBINE INDIA CONFERENCE (GTINDIA2021), 2021,
  • [40] Landslide Susceptibility Mapping Using Deep Neural Network and Convolutional Neural Network
    Gong, Sung-Hyun
    Baek, Won-Kyung
    Jung, Hyung-Sup
    KOREAN JOURNAL OF REMOTE SENSING, 2022, 38 (06) : 1723 - 1735