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Stock Price Trend Prediction Model Based on Deep Residual Network and Stock Price Graph

  • Heng Liu
  • , Bowen Song
  • Xiamen University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Consider that people often use stock price graph to make decisions, this paper introduce a deep residual network (ResNet) model for prediction, using the stock price graph as input. The results show that the ResNet model has the average accuracy of 0.40, which is higher than the stochastic indicator of 0.33.

Original languageEnglish
Title of host publicationProceedings - 2018 11th International Symposium on Computational Intelligence and Design, ISCID 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages328-331
Number of pages4
ISBN (Electronic)9781538685266
DOIs
Publication statusPublished - 2 Jul 2018
Externally publishedYes
Event11th International Symposium on Computational Intelligence and Design, ISCID 2018 - Hangzhou, China
Duration: 8 Dec 20189 Dec 2018

Publication series

NameProceedings - 2018 11th International Symposium on Computational Intelligence and Design, ISCID 2018
Volume2

Conference

Conference11th International Symposium on Computational Intelligence and Design, ISCID 2018
Country/TerritoryChina
CityHangzhou
Period8/12/189/12/18

Keywords

  • ResNet
  • Stock price graph
  • Stock price time series

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