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GCN-based stock relations analysis for stock market prediction.
Zhao, Cheng; Liu, Xiaohui; Zhou, Jie; Cen, Yuefeng; Yao, Xiaomin.
  • Zhao C; School of Economics, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
  • Liu X; College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
  • Zhou J; School of Economics, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
  • Cen Y; School of Information and Electronic Engineering, Zhejiang University of Science & Technology, Hangzhou, Zhejiang, China.
  • Yao X; College of Entrepreneurship, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
PeerJ Comput Sci ; 8: e1057, 2022.
Article in English | MEDLINE | ID: covidwho-1994469
ABSTRACT
Most stock price predictive models merely rely on the target stock's historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: PeerJ Comput Sci Year: 2022 Document Type: Article Affiliation country: Peerj-cs.1057

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: PeerJ Comput Sci Year: 2022 Document Type: Article Affiliation country: Peerj-cs.1057