...

  • Home
  • Articles
    • Current Issue
    • Archives
  • Authors
    • Author Guidelines
    • Policies
    • Downloads
  • Editors
  • Reviewers
...

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 71 | Issue 4 | Year 2025 | Article Id. IJMTT-V71I4P101 | DOI : https://doi.org/10.14445/22315373/IJMTT-V71I4P101

An Application of Markov Chains in Optimizing Stock Portfolios in the Vietnamese Stock Market


Dinh Thi Kim Nhung
Received Revised Accepted Published
24 Feb 2025 27 Mar 2025 12 Apr 2025 28 Apr 2025
Abstract

Markov chains are widely used as statistical models in various fields. In this paper, the author introduces the application of Markov chains to optimize stock portfolios. Markov chains are used to forecast the future returns of stocks. From this, investors can decide which stocks to invest in to optimize profits. Empirical analysis was conducted on some stock codes listed on the Vietnamese Stock Exchange.  

Keywords

Markov chains, Investment portfolio, Return on investment. 

References

[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,” Applied Mathematics & Information Sciences, vol. 8, no. 1, pp. 107-112, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jashua 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 Csiszar, and Janos Korner, Information Theory: Coding Theorems for Discrete Memoryless Systems, Cambridge University Press, 2011.
[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, Stochastic Processes and Stochastic Computation, Hanoi National University Publishing House, 2006.
[8] Grant McQueen, and Steven Thorley, “Are Stock Returns Predictable? A Test Using Markov Chains,” The Journal of Finance, vol. 46, no. 1, pp. 239-263, 1991.
[CrossRef] [Google Scholar] [Publisher Link]
[9] 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]
[10] 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, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Kevin J. Doubleday, and Julius N. Esunge, “Application of Markov Chains to Stock Trends,” Journal of Mathematics and Statistics, vol. 7, no. 2, pp. 103-106, 2011.
[Google Scholar] [Publisher Link]
[12] Sham M. Kakade, “A Natural Policy Gradient,” Advances in Neural Information Processing Systems, pp. 1-8, 2001.
[Google Scholar] [Publisher Link]
[13] Karl W. Cobbe et al., “Phasic Policy Gradient,” Proceedings of the 38th International Conference on Machine Learning, pp. 1-8, 2021.
[Google Scholar] [Publisher Link]
[14] Laura Graesser, and Wah Loon Keng, Foundations of Deep Reinforcement Learning: Theory and Practice in Python, Addison-Wesley Data & Analytics Series, 2019.
[Google Scholar] [Publisher Link]
[15] Maxim Lapan, Deep Reinforcement Learning Hands-On: Apply Modern RL Methods to Practical Problems of Chatbots, Robotics, Discrete Optimization, Web Automation, and More, Second Edition, Packt Publishing, 2020.
[Google Scholar] [Publisher Link]
[16] Milan Svoboda, and Ladislav Lukas, “Application of Markov Chain Analysis to Trend Prediction of Stock Indices,” Proceedings of 30th International Conference Mathematical Methods in Economics, pp. 1-6, 2012.
[Google Scholar] [Publisher Link]
[17] Nguyen Duy Tien, Probability Models and Applications. Part I: Markov Chains and Applications, Hanoi National University Publishing House, 2000.
[18] Qing Wang et al., “Divergence-Augmented Policy Optimization,” Advances in Neural Information Processing Systems, 2019.
[Google Scholar] [Publisher Link]
[19] John Schulman et al., “Trust Region Policy Optimization,” Proceedings of the 32nd International Conference on Machine Learning, pp. 1889-1897, 2015.
[Google Scholar] [Publisher Link]
[20] John Schulman et al., “Proximal Policy Optimization Algorithms,” arxiv, pp.1-12, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] 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]
[22] Richard S. Sutton, and Andrew G. Barto, Reinforcement Learning: An Introduction, Second Edition, The MIT Press, 1998.
[Google Scholar] [Publisher Link]
[23] 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]
[24] W. Farida Agustini, Ika Restu Affianti, and Endah RM Putri, “Stock Price Prediction using Geometric Brownian Motion,” Journal of Physics: Conference Series, pp. 1-12, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] 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]
[26] Yuhui Wang et al., “Trust Region-Guided Proximal Policy Optimization,” Advances in Neural Information Processing Systems, 2019.
[Google Scholar] [Publisher Link]
[27] Yuhui Wang, Hao He, and Xiaoyang Tan, “Truly Proximal Policy Optimization,” Proceedings of the 35th Uncertainly in Artificial Intelligence Conference, vol. 115, pp. 113-122, 2020.
[Google Scholar] [Publisher Link]

Citation :

Dinh Thi Kim Nhung, "An Application of Markov Chains in Optimizing Stock Portfolios in the Vietnamese Stock Market," International Journal of Mathematics Trends and Technology (IJMTT), vol. 71, no. 4, pp. 1-7, 2025. Crossref, https://doi.org/10.14445/22315373/IJMTT-V71I4P101

  • PDF
  • Abstract
  • Keywords
  • References
  • Citation
Abstract Keywords References Citation
  • Home
  • Authors Guidelines
  • Paper Submission
  • APC
  • Archives
  • Downloads
  • Open Access
  • Publication Ethics
  • Copyrights Infringement
  • Journals
  • FAQ
  • Contact Us

Follow Us

Copyright © 2025 Seventh Sense Research Group® . All Rights Reserved