[
Binh Khanh. 2024. Soon put new technology systems into operation to ensure stock transactions. Retrieved from http://tuoitre.vn/som-dua-he-thong-cong-nghe-moi-vao-hoat-dong-de-dam-bao-giao-dich-chung-khoan-20240102084226245.htm
]Search in Google Scholar
[
Cheng, C.-H., Tsai, M.-C., & Chang, C. 2022. A Time Series Model Based on Deep Learning and Integrated Indicator Selection Method for Forecasting Stock Prices and Evaluating Trading Profits. Systems, 10(6). doi: 10.3390/systems10060243.
]Search in Google Scholar
[
Chi, Y. N., & Chi, O. (2021). Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal Arima and Multilayer Perceptron Neural Network. Econometrics, 25(3), 21–41. https://doi.org/10.15611/eada.2021.3.02
]Search in Google Scholar
[
Frattini, A., Bianchini, I., Garzonio, A., & Mercuri, L. 2022. Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data. Risks, 10(12). doi: 10.3390/risks10120225
]Search in Google Scholar
[
Hilpisch, Y. 2020. Python for Algorithmic Trading: From Idea to Cloud Deployment 1st Edition. USA: O’Reilly Media.
]Search in Google Scholar
[
Jošić, H., & Žmuk, B. (2022). A Machine Learning Approach to Forecast International Trade: The Case of Croatia. Business Systems Research Journal, 13(3), 144–160. https://doi.org/10.2478/bsrj-2022-0030
]Search in Google Scholar
[
Khan, M. J. U. R., & Awasthi, A. (2019). Machine learning model development for predicting road transport GHG emissions in Canada. WSB Journal of Business and Finance, 53(2), 55–72. https://doi.org/10.2478/wsbjbf-2019-0022
]Search in Google Scholar
[
Letteri, I. 2023. VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning (arXiv:2307.13422). arXiv. doi: 10.48550/arXiv.2307.13422
]Search in Google Scholar
[
Nhat N. M., & Trung N. D. 2021. Quantitative trading application on Vietnam stock market. Asian Journal of Economics and Banking, 183.
]Search in Google Scholar
[
Simanjuntak, I., Heriyant, H., Rochendi, A., Rahmawati, Y., Salamah, K., & Sulistiyono, S. 2023. Trading Simulation Using Python and Visualization on Streamlit with Machine Learning Decision Tree. Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications, 286–291. doi: 10.1145/3575882.3575937
]Search in Google Scholar
[
Sumi, S. M., Zaman, M. F., & Hirose, H. (2012). A rainfall forecasting method using machine learning models and its application to the Fukuoka city case. International Journal of Applied Mathematics and Computer Science, 22(4), 841–854. https://doi.org/10.2478/v10006-012-0062-1
]Search in Google Scholar
[
Truong Thi Thuy Duong. 2023. Forecasting the direction of stock index fluctuations using enhanced algorithms. Retrieved from http://hvnh.edu.vn//tapchi/vi/thang-5-2023/du-bao-chieu-bien-dong-cua-chi-so-chung-khoan-bang-thuat-toan-tang-cuong-truong-thi-thuy-duong-10755.html
]Search in Google Scholar
[
Tuyen T.D. 2024. Evaluating the performance of the LSTM-GRU complex model: Case study on forecasting an index measuring stock price fluctuation trends on the Ho Chi Minh stock exchange. Can Tho University Science Magazine, 60(1). doi: 10.22144/ctujos.2023.232
]Search in Google Scholar
[
Trung Kien Dang et al 2024. Factors affecting the proftability of food companies listed on the vietnam stock market, 17(1), 70 - 88, Electronic Journal of Applied Statistical Analysis. DOI: 10.1285/i20705948v17n1p69
]Search in Google Scholar
[
Yan, N. (2023). Construction of International Trade and Investment Platform Based on Artificial Intelligence Technology. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns.2023.2.01694
]Search in Google Scholar
