SỐ 244 | Tháng 7/2026
Tối ưu hóa chiến lược giao dịch chứng khoán Việt Nam bằng phân tích đa khung thời gian
Trần Trung Minh, Nguyễn Như Quỳnh, Phạm Thị Hồng Nhung, Phan Văn Khải
Tóm tắt:
Mục tiêu nghiên cứu: Nghiên cứu xây dựng một khuôn khổ đánh giá đa khung thời gian cho giao dịch chứng khoán Việt Nam, sử dụng thuật toán di truyền (Genetic Algorithm -GA) để tinh chỉnh các tham số và kiểm định tính bền vững.
Thiết kế nghiên cứu/phương pháp/tiếp cận: Các phân tích được thực hiện với dữ liệu chỉ số VN-Index giai đoạn 2014–2024. Tính bền vững của chiến lược được đánh giá thông qua các giai đoạn thị trường khác nhau.
Kết quả nghiên cứu chính: Các chiến lược được tối ưu hóa bằng GA đạt hiệu suất vượt trội và có ý nghĩa thống kê so với chiến lược mặc định. Khi tối ưu hóa đồng thời lợi nhuận và rủi ro, chiến lược dựa trên tỷ lệ Calmar đạt mức độ bền vững cao nhất, với tỷ lệ thành công 83,33% trên dữ liệu kiểm thử.
Giá trị đóng góp mới: Nghiên cứu đóng góp một khung phương pháp hiện đại trong việc ứng dụng thống kê và học máy vào quản lý danh mục đầu tư tại thị trường mới nổi.
Tài liệu tham khảo:
- Achelis, S. B. (2001). Technical analysis from A to Z (2nd ed.). McGraw-Hill.
- Che-Ngoc, H., Do-Thi, N., & Nguyen-Trang, T. (2023). Profitability of Ichimoku-based trading rule in Vietnam stock market in the context of the COVID-19 outbreak. Computational Economics, 62(4), 1781-1799. https://doi.org/10.1007/s10614-022-10319-6.
- Conti, J. P. J., & Lopes, H. S. (2019). Algorithmic trading using genetic algorithms in the Brazilian stock exchange. Sociedade Brasileira de Inteligência Computacional, 7. https://doi.org/10.21528/cbic2019-112.
- De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons. https://doi.org/10.1007/s11408-019-00341-4.
- Deb, K. (2011). Multi-objective optimisation using evolutionary algorithms: an introduction. In Multi-objective evolutionary optimisation for product design and manufacturing (pp. 3-34). London: Springer London. https://doi.org/10.1007/978-0-85729-652-8_1.
- Fayek, M. B., El-Boghdadi, H. M., & Omran, S. M. (2013). Multi-objective optimization of technical stock market indicators using gas. International Journal of Computer Applications, 68(20). https://doi.org/10.5120/11698-7428.
- Fu, T. C., Chung, C. P., & Chung, F. L. (2013). Adopting genetic algorithms for technical analysis and portfolio management. Computers & Mathematics with Applications, 66(10), 1743-1757. https://doi.org/10.1016/j.camwa.2013.08.012
- Hidayat, Y., Lesmana, E., Putra, A. S., Napitupulu, H., & Supian, S. (2018). Portfolio optimization by using linear programing models based on genetic algorithm. In IOP conference series: materials science and engineering (Vol. 300, No. 1, p. 012001). IOP Publishing. https://doi.org/10.1088/1757-899X/300/1/012001.
- Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press. https://doi.org/10.7551/mitpress/1090.001.0001.
- Kaucic, M., Moradi, M., & Mirzazadeh, M. (2019). Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures. Financial Innovation, 5(1), 26. https://doi.org/10.1186/s40854-019-0140-6.
- Lambert, D. R. (1983). Commodity channel index: Tool for trading cyclic trends. Technical Analysis of Stocks & Commodities, 1(1), 47.
- Le, T. T., & Luong, A. T. (2020). A test of return predictability in the Vietnamese stock market. International Journal of Financial Research, 11(2), 390-404. https://doi.org/10.5430/ijfr.v11n2p390.
- Liu, P. (2023). Optimizing Trading Strategies with Bayesian optimization. In Quantitative Trading Strategies Using Python: Technical Analysis, Statistical Testing, and Machine Learning(pp. 257-301). Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842-9675-2_9.
- Lo, A. W. (2002). The statistics of Sharpe ratios. Financial analysts journal, 58(4), 36-52. https://doi.org/10.2469/faj.v58.n4.2453.
- Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. The Journal of Portfolio Management, 30, 15-29. https://doi.org/10.3905/jpm.2004.442611.
- Macedo, L. L., Godinho, P., & Alves, M. J. (2020). A comparative study of technical trading strategies using a genetic algorithm. Computational Economics, 55(1), 349-381. https://doi.org/10.1007/S10614-016-9641-9.
- Marshall, B. R., & Cahan, R. H. (2005). Is technical analysis profitable on a stock market which has characteristics that suggest it may be inefficient?. Research in International Business and Finance, 19(3), 384-398. https://doi.org/10.1016/j.ribaf.2005.05.001.
- Nguyen, T. D., Nguyen, N. M., & Tran, M. (2025). Multi-objective optimization for algorithmic trading in the Vietnamese stock market. Bulletin of Electrical Engineering and Informatics, 14(4), 2559-2570. https://doi.org/10.11591/eei.v14i4.9288.
- Phan, H. M., Le, T. N. Q., Dam, V. D. H., Tran, M. S., Truong, T. H. L., & Le, Q. A. (2023). Herd behavior in Vietnam’s stock market: Impacts of COVID-19. Cogent Economics & Finance, 11(2), 2266616. https://doi.org/10.1080/23322039.2023.2266616.
- Shaik, M., & Maheswaran, S. (2017). Market efficiency of ASEAN stock markets. Asian Economic and Financial Review, 7(2), 109. https://doi.org/10.18488/journal.aefr/2017.7.2/102.2.109.122.
- Sharpe, W. F. (1994). The Sharpe Ratio. The Journal of Portfolio Management, 21(1), 49-58. https://doi.org/10.3905/JPM.1994.409501.
- Shi, F., Liao, H., Wang, S., Alfarisi, O., & Qu, F. (2025). Optimization of drilling rate based on genetic algorithms and machine learning models. Geoenergy Science and Engineering, 247, 213747. https://doi.org/10.1016/j.geoen.2025.213747.
- Sivanandam, S. N., & Deepa, S. N. (2008). Genetic algorithms. In Introduction to genetic algorithms (pp. 15-37). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-73190-0_2.
- Srikamdee, S., Rimcharoen, S., & Leelathakul, N. (2013). Prediction of the stock exchange of Thailand index using an evolutionary algorithm portfolio. In 2013 5th International Conference on Knowledge and Smart Technology (KST) (pp. 144-149). IEEE. https://doi.org/10.1109/KST.2013.6512804.
- Sun, Q., Wei, X., & Yang, X. (2024). GraphSAGE with deep reinforcement learning for financial portfolio optimization. Expert Systems with Applications, 238, 122027. https://doi.org/10.1016/j.eswa.2023.122027.
- Vapnik, V. N., & Chervonenkis, A. Y. (2015). On the uniform convergence of relative frequencies of events to their probabilities. In Measures of complexity: Festschrift for Alexey Chervonenkis (pp. 11-30). Springer International Publishing. https://doi.org/10.1007/978-3-319-21852-6_3.
- Wafi, A. S. (2015). Profitability of technical analysis rules in emerging and developed markets. China-USA Business Review, 14(10), 495-503. https://doi.org/10.17265/1537-1514/2015.10.002.
- Wilder, J. W. (1978). New concepts in technical trading systems. Greensboro, NC.
- Xu, X., Zhang, Y. (2024). High-frequency CSI300 futures trading volume predicting through the neural network. Asian Journal of Economics and Banking, 8(1), 26-53. https://doi.org/10.1108/AJEB-05-2022-0051.
- Yu, H., Nartea, G. V., Gan, C., & Yao, L. J. (2013). Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets. International Review of Economics & Finance, 25, 356-371. https://doi.org/10.1016/j.iref.2012.07.016.
Optimizing Stock Trading Strategies in Vietnam Using Multi-Timeframe Analysis
Abstract:
Purpose: This study develops a multi-timeframe evaluation framework for stock trading in Vietnam, using Genetic Algorithms (GA) for parameter tuning and robustness testing.
Design/methodology/approach: Historical data of the VN-Index from 2014 to 2024 are used for empirical analysis. Strategy robustness is evaluated across different market periods.
Findings: GA-optimized strategies achieve statistically significant superior performance compared to default strategies. When jointly optimizing return and risk, the Calmar ratio-based strategy demonstrates the highest robustness, achieving an 83.33% success rate on test data.
Originality/value: The study contributes a modern methodological framework for applying statistics and machine learning to portfolio management in emerging markets.