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

Nguyen Ngoc Tra | Zendy; Ho Phuoc Tien | Zendy; Dat Thanh Nguyen | Zendy; Nguyen Ngoc Vu | Zendy

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

Author(s) -

Nguyen Ngoc Tra,

Ho Phuoc Tien,

Dat Thanh Nguyen,

Nguyen Ngoc Vu

Publication year - 2019

Publication title -

the university of danang - journal of science and technology

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.

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