Prediction model for stock price trend based on convolution neural network

Issue title: Special Section: Intelligent Algorithms for Complex Information Services - Recent Advances and Future Trends

Guest editors: Andino Maseleno, Xiaohui Yuan and Valentina E. Balas

Article type: Research Article

Authors: Lin, Hongbo^a | Zhao, Jinghua^{b; *} | Liang, Shuang^b | Kang, Huilin^a

Affiliations: [a] School of Economic and Management, Tongji University, Shanghai, China | [b] School of Business, University of Shanghai for Science and Technology, Shanghai, China

Correspondence: [*] Corresponding author. Jinghua Zhao, E-mail: zhaojinghua@usst.edu.cn.

Abstract: Aiming at the image features of stock data, considering the picture features of stock data and the characteristics of CNN’s good at extracting picture features, the paper proposed a stock price trend prediction model CNN-M based on a Convolutional Neural Network. At the same time, based on the excellent image feature extraction ability of the residual network, this paper proposed a residual network-based stock price trend prediction model ResNet-M based on the Conventional Neural Network. The experimental results showed that the prediction ability of the improved residual network-based prediction model Resnet-M is superior to the CNN model.

Keywords: Convolutional neural network, stock price trend prediction, deep residual neural network

DOI: 10.3233/JIFS-179985

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4999-5008, 2020