Research on Deep Learning and Its Application in Stock Price Prediction

Nghiên cứu về Deep learning và ứng dụng trong dự báo giá cổ phiếu chứng khoán

  • Van Hai Hoang Tueba
Keywords: Deep learning, stock price prediction, LSTM, BiLSTM, CNN


The article studies the problem of forecasting the
closing price of a stock based on historical data of a previous
day. The paper uses and compares algorithms based on deep
learning such as LSTM, BiLSTM, and CNN. The dataset
includes data on price, trading volume and some technical
indicators related to VCB, MSN, and HPG shares. The results
show that CNN performs better for predicting the next day’s
closing price than the other architectures.


Achkar, R., Elias-Sleiman, F., Ezzidine, H., & Haidar, N. (2018). Comparison of BPA-MLP and LSTM-RNN for Stocks Prediction. 2018 6th International Symposium on Computational and Business Intelligence (ISCBI). doi:10.1109/iscbi.2018.00019

Althelaya, K. A., El-Alfy, E.-S. M., & Mohammed, S. (2018). Stock Market Forecast Using Multivariate Analysis

with Bidirectional and Stacked (LSTM, GRU). 2018 21st Saudi Computer Society National Computer Conference (NCC). doi:10.1109/ncg.2018.8593076

Aravindpai, P.: CNN vs. RNN vs. ANN – Analyzing 3 Types of Neural Networks in Deep Learning.

Bengio, Y. (2012). Practical Recommendations for GradientBased Training of Deep Architectures. Neural Networks: Tricks of the Trade, 437–478. doi:10.1007/978-3-642-35289- 8 26

Brownlee, J.:Using Learning Rate Schedules for Deep Learning Models in Python with Keras. deep-learning-models-python-keras/

Chen, W., Zhang, Y., Yeo, C. K., Lau, C. T., & Lee, B. S. (2017). Stock market prediction using neural network through news on online social networks. 2017 International Smart Cities Conference (ISC2). doi:10.1109/isc2.2017.8090834

Hoseinzade, E., & Haratizadeh, S. (2019). CNNpred: CNN-based stock market prediction using a diverse

set of variables. Expert Systems with Applications. doi:10.1016/j.eswa.2019.03.029

Jay, A.: How to do Average Directional Index (ADX) in Excel: irectionalindex-adx-in-excel/

Kara, Y., Boyacioglu, M.A., Baykan, O.K.(2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines:The sample of the istanbul stock exchange. Expert Syst. Appl. 38 (5), 5311–5319.

Lee, M.-C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Syst. Appl. 36 (8), 10896–10904.

Luo, L., Chen, X. (2013). Integrating piecewise linear representation and weighted support vector machine for stock trading signal prediction. Appl. Soft Comput.13 (2), 806–816.

Mark, U.: How to Calculate the Elder Ray Technical Indicator using Excel:

Mehtab, S. & Sen, J. (2020). Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries.

Olah, C.: Understanding LSTM Networks

Paialunga, P.: Noise cancellation with Python and Fourier Transform and-fourier-transform-97303314aa71.

Ruder, S. (2017). An overview of gradient descent optimization algorithms.

Sethia, A., & Raut, P. (2018). Application of LSTM, GRU and ICA for Stock Price Prediction. Smart Innovation,

Systems and Technologies, 479–487. doi:10.1007/978-981-13-1747-7 46.

Sonkiya, P. , Bajpai, V. and Bansal, A. (2021). Stock price prediction using BERT and GAN. In Proceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 9 pages.

Sutskever, I., Martens, J., Dahl, G. and Hinton, G. (2013). On the importance of initialization and momentum in deep learning. Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1139-1147, 2013.

Tran .T. T.: Optimizer- bq5QQ9E5D8.

Tsai, C.-F., & Hsiao, Y.-C. (2010). Combining multiple feature selection methods for stock prediction: Union, intersection, and multi- intersection approaches. Decision Support Systems, 50(1), 258–269. doi:10.1016/j.dss.2010.08.028.

Zhang, K., Zhong, G., Dong, J., Wang, S., & Wang, Y. (2019). Stock Market Prediction Based on Generative Adversarial Network. Procedia Computer Science, 147, 400–406. doi:10.1016/j.procs.2019.01.256.

Zhang, Q., Wang, R., Qi, Y. and Wen, F. (2022). A watershed water quality prediction model based on attention mechanism and Bi- LSTM. Environ Sci Pollut Res (2022).

Zhou, X., Pan, Z., Hu, G., Tang, S., & Zhao, C. (2018). Stock Market Prediction on High-Frequency Data Using

Generative Adversarial Nets. Mathematical Problems in Engineering, 2018, 111. doi:10.1155/2018/4907423.