Abstract
Recently, the problem of stock market prediction has attracted a lot of attention. Many studies have been proposed to apply to the problem of stock market prediction. However, achieving good results in prediction is still a challenge in research and there are very few studies applied to Vietnamese stock market data. Therefore, it is necessary to improve or introduce new forms of prediction. Specifically, we have focused on the stock prediction problem for the Vietnamese market in the short and long term. Long short-term memory (LSTM) based on deep learning model has been applied to big data problem such as VN-INDEX. We compared the prediction results of the variants of the LSTM model with each other. The results obtained are very interesting that the Bidirectional LSTM architecture gives good results in short- and long-term prediction for the Vietnamese stock market. In conclusion, the LSTM architecture is very suitable for the stock prediction problem in the long- and short- term.
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References
Nti, I.K., Adekoya, A.F., Weyori, B.A.: A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction. J. Big Data 8(1), 1–28 (2021). https://doi.org/10.1186/s40537-020-00400-y
Jiang, W.: Applications of deep learning in stock market prediction: recent progress. Expert Syst. Appl. 184, 115537 (2021). https://doi.org/10.1016/j.eswa.2021.115537
Ananthi, M., Vijayakumar, K.: Stock market analysis using candlestick regression and market trend prediction (CKRM). J. Ambient Intell. Humaniz. Comput. 12(5), 4819–4826 (2020). https://doi.org/10.1007/s12652-020-01892-5
Wang, X., Phua, P.K.H., Lin, W.: Stock market prediction using neural networks: does trading volume help in short-term prediction? In: Proceedings of the International Joint Conference on Neural Networks, vol. 4, pp. 2438–2442 (2003). https://doi.org/10.1109/IJCNN.2003.1223946
Ariyo, A.A., Adewumi, A.O., Ayo, C.K.: Stock price prediction using the ARIMA model. In: 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 106–112 (2014). https://doi.org/10.1109/UKSim.2014.67
Anaghi, M.F., Norouzi, Y.: A model for stock price forecasting based on ARMA systems. In: 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 265–268 (2012). https://doi.org/10.1109/ICTEA.2012.6462880
Hushani, P.: Using autoregressive modelling and machine learning for stock market prediction and trading. In: Yang, X.-S., Sherratt, S., Dey, N., Joshi, A. (eds.) Third International Congress on Information and Communication Technology. AISC, vol. 797, pp. 767–774. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1165-9_70
S. Siami-Namini, N., Tavakoli, A., Namin, S.: A comparison of ARIMA and LSTM in forecasting time series. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1394–1401 (2018). https://doi.org/10.1109/ICMLA.2018.00227
Huang, J., Chai, J., Cho, S.: Deep learning in finance and banking: a literature review and classification. Front. Bus. Res. China 14 (2020). https://doi.org/10.1186/s11782-020-00082-6
Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018, 7068349 (2018). https://doi.org/10.1155/2018/7068349
Wu, S., et al.: Deep learning in clinical natural language processing: a methodical review. J. Am. Med. Inform. Assoc. 27, 457–470 (2020). https://doi.org/10.1093/jamia/ocz200
Hiransha, M., Gopalakrishnan, E.A., Menon, V.K., Soman, K.P.: NSE stock market prediction using deep-learning models. Procedia Comput. Sci. 132, 1351–1362 (2018). https://doi.org/10.1016/j.procs.2018.05.050
Vargas, M.R., de Lima, B.S.L.P., Evsukoff, A.G.: deep learning for stock market prediction from financial news articles. In: 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 60–65 (2017). https://doi.org/10.1109/CIVEMSA.2017.7995302
Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., Salwana, E.: Deep learning for stock market prediction. Entropy 22 (2020). https://doi.org/10.3390/e22080840
Co, N.T., Son, H.H., Hoang, N.T., Lien, T.T.P., Ngoc, T.M.: Comparison between ARIMA and LSTM-RNN for VN-index prediction. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds.) IHSI 2020. AISC, vol. 1131, pp. 1107–1112. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39512-4_168
Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: the AZFin text system (2009)
Duong, D., Nguyen, T., Dang, M.: Stock market prediction using financial news articles on Ho Chi Minh stock. Exchange (2016). https://doi.org/10.1145/2857546.2857619
Phan, T.N.T., Bertrand, P., Phan, H.H., Vo, X.V.: The role of investor behavior in emerging stock markets: evidence from Vietnam. Quarterly Rev. Econ. Finance (2021). https://doi.org/10.1016/j.qref.2021.07.001
Huynh, H.D., Dang, L.M., Duong, D.: A New Model for Stock Price Movements Prediction Using Deep Neural Network (2017). https://doi.org/10.1145/3155133.3155202
Le, T.D.B., Ngo, M.M., Tran, L.K., Duong, V.N.: Applying LSTM to predict firm performance based on annual reports: an empirical study from the vietnam stock market. In: Ngoc Thach, N., Kreinovich, V., Trung, N.D. (eds.) Data Science for Financial Econometrics. SCI, vol. 898, pp. 613–622. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-48853-6_41
Lien Minh, D., Sadeghi-Niaraki, A., Huy, H.D., Min, K., Moon, H.: Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access 6, 55392–55404 (2018). https://doi.org/10.1109/ACCESS.2018.2868970
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM networks. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks 2005, vol. 4, pp. 2047–2052 (2005). https://doi.org/10.1109/IJCNN.2005.1556215
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), pp. 265–283. USENIX Association, Savannah (2016)
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This research is funded by International School, Vietnam National University, Hanoi (VNU-IS) under project number CS.2021-02.
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Truong, CD., Tran, DQ., Nguyen, VD., Tran, HT., Hoang, TD. (2021). Predicting Vietnamese Stock Market Using the Variants of LSTM Architecture. In: Cong Vinh, P., Huu Nhan, N. (eds) Nature of Computation and Communication. ICTCC 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 408. Springer, Cham. https://doi.org/10.1007/978-3-030-92942-8_11
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