VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS

  • Nguyen Ngoc Tra The University of Danang, University of Economics, Vietnam
  • Ho Phuoc Tien The University of Danang, University of Science and Technology, Vietnam
  • Nguyen Thanh Dat The University of Danang, University of Economics, Vietnam
  • Nguyen Ngoc Vu The University of Danang, Vietnam

Abstract

The paper attemps to forecast the future trend of Vietnam index (VN-index) by using long-short term memory (LSTM) networks. In particular, an LSTM-based neural network is employed to study the temporal dependence in time-series data of past and present VN index values. Empirical forecasting results show that LSTM-based stock trend prediction offers an accuracy of about 60% which outperforms moving-average-based prediction.

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Published
2019-12-09
How to Cite
TRA, Nguyen Ngoc et al. VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS. Journal of Science and Technology: Issue on Information and Communications Technology, [S.l.], v. 17, n. 12.2, p. 61-65, dec. 2019. ISSN 1859-1531. Available at: <http://ict.jst.udn.vn/index.php/jst/article/view/94>. Date accessed: 20 feb. 2020. doi: https://doi.org/10.31130/ict-ud.2019.94.