VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS
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.
 I. Aizenberg, L. Sheremetov, L. Villa-Vargas & J. Martinez-Muñoz, “Multilayer neural network with multi-valued neurons in time series forecasting of oil production”, Neurocomputing, 175, 2016, 980-989.
 A. Sagheer & M. Kotb, “Time series forecasting of petroleum production using deep LSTM recurrent networks”, Neurocomputing, 323, 2019, 203-213.
 S. Pyo, J. Lee, M. Cha & H. Jang, “Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets”, PLoS ONE 12(11), 2017.
 C.Y. Ma, M.H. Chen, Z. Kira & G.A. Regib, “TS-LSTM and temporal-inception: Exploiting spatiotemporal dynamics for activity recognition”, Signal Processing: Image Communication, 71, 2019, 76-87.
 Z. Guo, H. Wang, Q. Liu & J. Yang, “A Feature Fusion Based Forecasting Model for Financial Time Series”, PLoS ONE 9(6), 2014.
 H. Guan, Z. Dai, A. Zhao & J. He, “A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network”, PLoS ONE 13(2), 2018.
 T. Kim & H. Y. Kim, “Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data”, PLoS ONE 14(2), 2019.
 B. Yang, W. Zhang & H. Wang, “Stock Market Forecasting Using Restricted Gene Expression Programming”, Computational Intelligence and Neuroscience, 7198962, 2019.
 S. Hochreiter & J. Schmidhuber, “Long short-term memory”, Neural Computation, 9 (8), 1997, 1735–1780.
 N. Liu & J. Han, “A Deep Spatial Contextual Long-Term Recurrent Convolutional Network for Saliency Detection”, IEEE Transactions on Image Processing, 27(7), 2018, 3264-3274.
 Y. Feng, L. Ma, W. Liu & J. Luo, “Spatio-temporal Video Re-localization by Warp LSTM”, IEEE Conference on Computer Vision and Pattern Recognition, 2019.
 Y. LeCun, Y. Bengio & G. Hinton, “Deep learning”, Nature, 521, 2015, 436–444.
 T. Ho-Phuoc, “CIFAR10 to Compare Visual Recognition Performance between Deep Neural Networks and Humans”, arXiv, 2018.
 S. An, Z. Ling & L. Dai, “Emotional statistical parametric speech synthesis using LSTM-RNNs”, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2017.
 T. Young, D. Hazarika, S. Poria & E. Cambria, “Recent Trends in Deep Learning Based Natural Language Processing”, arXiv, 2017.
 C. Si, W. Chen, W. Wang, L. Wang & T. Tan, “An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition”, IEEE Conference on Computer Vision and Pattern Recognition, 2019.