Open AccessVN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS
Author(s) -
Nguyen Ngoc Tra,
Ho Phuoc Tien,
Nguyen Van Dat,
Nguyen Ngoc Vu
Publication year - 2019
Publication title -
tạp chí khoa học và công nghệ
Language(s) - English
Resource type - Journals
ISSN - 1859-1531
DOI - 10.31130/ict-ud.2019.94
Subject(s) - long short term memory , index (typography) , artificial neural network , computer science , term (time) , time series , stock market index , series (stratigraphy) , artificial intelligence , econometrics , machine learning , recurrent neural network , data mining , stock market , mathematics , geography , paleontology , context (archaeology) , physics , archaeology , quantum mechanics , world wide web , biology
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.