A LSTM-based method for stock returns prediction: A case study of China stock market

Kai Chen, Yi Zhou, Fangyan Dai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

424 Scopus citations

Abstract

Prediction of stock market has attracted attention from industry to academia [1, 2]. Various machine learning algorithms such as neural networks, genetic algorithms, support vector machine, and others are used to predict stock prices.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
EditorsFeng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2823-2824
Number of pages2
ISBN (Electronic)9781479999255
DOIs
StatePublished - Dec 22 2015
Event3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
Duration: Oct 29 2015Nov 1 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

Other

Other3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Country/TerritoryUnited States
CitySanta Clara
Period10/29/1511/1/15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software

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