International Journal of Mathematics Trends and Technology
Volume 71 | Issue 4 | Year 2025 | Article Id. IJMTT-V71I4P103 | DOI : https://doi.org/10.14445/22315373/IJMTT-V71I4P103
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 26 Feb 2025 | 29 Mar 2025 | 15 Apr 2025 | 29 Apr 2025 |
The aims of this paper present a stock price model and a method for forecasting stock prices under the assumption that stock prices follow the log-normally distribution law, simulating stock prices using geometric Brownian motion. The empirical analysis is conducted on stock codes in the VN30 index on the Vietnamese stock market. The experimental results show that the forecasting method presented in the report can be used to forecast stock prices in short-term investment periods of less than one month.
Stock price model, Brownian motion, Forecasting, Log-normal.
[1] Abdelmoula Dmouj, “Stock Price Modelling: Theory and Practice,” Vrije Universiteit Faculty of Sciences Amsterdam, The Netherlands,
pp. 1-40, 2006.
[Google Scholar] [Publisher Link]
[2] Siti Nazifah Zainol Abidin, and Maheran Mohd Jaffar, “Forecasting Share Price of Small Size Companies in Bursa Malaysia Using
Geometric Brownian Motion,” Journal International, Applied Mathematics & Information Sciences, vol. 8, no. 1, pp. 107-112, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Joshua Achiam et al., “Constrained Policy Optimization,” Proceedings of the 34th International Conference on Machine Learning, pp. 22
31, 2017.
[Google Scholar] [Publisher Link]
[4] Andrea Lonza, Reinforcement Learning Algorithms with Python: Learn, Understand, and Develop Smart Algorithms for Addressing AI
Challenges, Packt Publishing, pp. 1-366, 2019.
[Google Scholar] [Publisher Link]
[5] Imre Csiszár, and János Körner, Information Theory: Coding Theorems for Discrete Memoryless Systems, Academic Press, pp. 1-452,
1981. [Google Scholar] [Publisher Link]
[6] Divya Aggarwal, “Random Walk Model and Asymmetric Effect in Korean Composite Stock Price Index,” Afro-Asian Journal of Finance
and Accounting, vol. 8, no. 1, pp. 85-104, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Dang Hung Thang, Advanced Probability, Hanoi National University Publishing House, 2013.
[8] G. Bormetti et al., “A Backward Monte Carlo Approach To Exotic Option Pricing,” European Journal of Applied Mathematics, vol. 29,
no. 1, pp. 146-187, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Hongyang Yang et al., “Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy,” Proceedings of the First
ACM International Conference on AI in Finance, New York New York, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Sham M. Kakade, “A Natural Policy Gradient,” Proceedings of the 15th International Conference on Neural Information Processing
Systems: Natural and Synthetic, Vancouver British Columbia Canada, pp. 1531-1538, 2001.
[Google Scholar] [Publisher Link]
[11] Karl Cobbe et al., “Phasic Policy Gradient,” Arxiv, pp. 1-17, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Laura Graesser, and Wah Loon Keng, Foundations of Deep Reinforcement Learning: Theoryand Practice in Python, 1sted., Addison
Wesley Professional, pp. 1-416, 2019.
[Google Scholar] [Publisher Link]
[13] Maxim Lapan, Deep Reinforcement Learning Hands-On: Apply Modern RL Methodsto Practical Problems of Chatbots, Robotics, Discrete
Optimization, Web Automation, and More, 2nd ed., Packt Publishing Ltd, pp. 1-826, 2020.
[Google Scholar] [Publisher Link]
[14] Nguyen Duy Tien, Probability Models and Applications, Part I: Markov Chains and Applications, Hanoi National University Publishing
House, 2001.
[Google Scholar]
[15] Qing Wang et al., “Divergence-Augmented Policy Optimization,” Advances in Neural Information Processing Systems, vol. 32, pp. 1-12,
2019. [Google Scholar] [Publisher Link]
[16] John Schulman et al., “Trust Region Policy Optimization,” Arxiv, pp. 1-16, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[17] John Schulman et al., “Proximal Policy Optimization Algorithms,” Arxiv, pp. 1-12, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[18] S.O.N. Agwuegbo, A.P. Adewole, and A.N. Maduegbuna, “A Random Walk Model for Stock Market Prices,” Journal of Mathematics
and Statistics, vol. 6, no. 3, pp. 342-346, 2010.
[Google Scholar] [Publisher Link]
[19] Richard S. Sutton, and Andrew G. Barto, Reinforcement Learning: An Introduction, 2nd ed., The MIT Press, pp. 1-552, 2018.
[Google
Scholar] [Publisher Link]
[20] Tran Hung Thao, Introduction to Financial Mathematics, Science and Technology Publishing House, 2009.
[21] Wenhang Bao, and Xiao-yang Liu, “Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis,” Arxiv, pp. 1-9, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] W. Farida Agustini, Ika Restu Affianti, and Endah R.M. Putri, “Stock Price Prediction Using Geometric Brownian Motion,” Journal of
Physics: Conference Series, vol. 974, pp. 1-11, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Xiao-Yang Liu et al., “FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance,” Arxiv, pp.
1-12, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Yuhui Wang et al., “Trust Region-Guided Proximal Policy Optimization,” Arxiv, pp. 1-22, 2019.
[CrossRef] [Google Scholar] [Publisher
Link]
[25] Yuhui Wang et al., “Truly Proximal Policy Optimization,” Arxiv, pp. 1-29, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Zhuoran Xiong et al., “Practical Deep Reinforcement Learning Approach for Stock Trading,” Arxiv, pp. 1-7, 2018.
[CrossRef] [Google
Scholar] [Publisher Link]
Doan Thanh Son, "Forecasting Stock Prices of the Vietnamese Stock Market under the Assumption of Log-Normally Distributed Stock Prices," International Journal of Mathematics Trends and Technology (IJMTT), vol. 71, no. 4, pp. 21-26, 2025. Crossref, https://doi.org/10.14445/22315373/IJMTT-V71I4P103
PDF 
Abstract 
Keywords 
References 
Citation