IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2303.02298.html
   My bibliography  Save this paper

Continuous-Time Path-Dependent Exploratory Mean-Variance Portfolio Construction

Author

Listed:
  • Zhou Fang

Abstract

In this paper, we present an extended exploratory continuous-time mean-variance framework for portfolio management. Our strategy involves a new clustering method based on simulated annealing, which allows for more practical asset selection. Additionally, we consider past wealth evolution when constructing the mean-variance portfolio. We found that our strategy effectively learns from the past and performs well in practice.

Suggested Citation

  • Zhou Fang, 2023. "Continuous-Time Path-Dependent Exploratory Mean-Variance Portfolio Construction," Papers 2303.02298, arXiv.org.
  • Handle: RePEc:arx:papers:2303.02298
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2303.02298
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marc Sabate-Vidales & David v{S}iv{s}ka & Lukasz Szpruch, 2020. "Solving path dependent PDEs with LSTM networks and path signatures," Papers 2011.10630, arXiv.org.
    2. Mike Ludkovski & Glen Swindle & Eric Grannan, 2022. "Large Scale Probabilistic Simulation of Renewables Production," Papers 2205.04736, arXiv.org.
    3. Rian Dolphin & Barry Smyth & Yang Xu & Ruihai Dong, 2021. "Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities," Papers 2107.03926, arXiv.org.
    4. Yanwei Jia & Xun Yu Zhou, 2021. "Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms," Papers 2111.11232, arXiv.org, revised Jul 2022.
    5. Duan Li & Wan‐Lung Ng, 2000. "Optimal Dynamic Portfolio Selection: Multiperiod Mean‐Variance Formulation," Mathematical Finance, Wiley Blackwell, vol. 10(3), pages 387-406, July.
    6. Yuri F. Saporito & Zhaoyu Zhang, 2020. "PDGM: a Neural Network Approach to Solve Path-Dependent Partial Differential Equations," Papers 2003.02035, arXiv.org, revised Apr 2020.
    7. Haoran Wang, 2019. "Large scale continuous-time mean-variance portfolio allocation via reinforcement learning," Papers 1907.11718, arXiv.org, revised Aug 2019.
    8. Yanwei Jia & Xun Yu Zhou, 2021. "Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach," Papers 2108.06655, arXiv.org, revised Feb 2022.
    9. Bruno Dupire, 2019. "Functional Itô calculus," Quantitative Finance, Taylor & Francis Journals, vol. 19(5), pages 721-729, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiangyu Cui & Xun Li & Yun Shi & Si Zhao, 2023. "Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning," Papers 2312.15385, arXiv.org.
    2. Wu, Bo & Li, Lingfei, 2024. "Reinforcement learning for continuous-time mean-variance portfolio selection in a regime-switching market," Journal of Economic Dynamics and Control, Elsevier, vol. 158(C).
    3. Qi Feng & Man Luo & Zhaoyu Zhang, 2021. "Deep Signature FBSDE Algorithm," Papers 2108.10504, arXiv.org, revised Aug 2022.
    4. Haoran Wang & Xun Yu Zhou, 2020. "Continuous‐time mean–variance portfolio selection: A reinforcement learning framework," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1273-1308, October.
    5. Haoran Wang & Shi Yu, 2021. "Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning," Papers 2105.09264, arXiv.org.
    6. Yanwei Jia & Xun Yu Zhou, 2022. "q-Learning in Continuous Time," Papers 2207.00713, arXiv.org, revised Apr 2023.
    7. Alexandre Pannier, 2023. "Path-dependent PDEs for volatility derivatives," Papers 2311.08289, arXiv.org, revised Jan 2024.
    8. Yanwei Jia, 2024. "Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty," Papers 2404.12598, arXiv.org.
    9. Zhou Fang & Haiqing Xu, 2023. "Over-the-Counter Market Making via Reinforcement Learning," Papers 2307.01816, arXiv.org.
    10. Min Dai & Yu Sun & Zuo Quan Xu & Xun Yu Zhou, 2024. "Learning to Optimally Stop Diffusion Processes, with Financial Applications," Papers 2408.09242, arXiv.org, revised Sep 2024.
    11. Zhou Fang & Haiqing Xu, 2023. "Market Making of Options via Reinforcement Learning," Papers 2307.01814, arXiv.org.
    12. Yanwei Jia & Xun Yu Zhou, 2021. "Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms," Papers 2111.11232, arXiv.org, revised Jul 2022.
    13. Xiang Meng, 2019. "Dynamic Mean-Variance Portfolio Optimisation," Papers 1907.03093, arXiv.org.
    14. Luca De Gennaro Aquino & Sascha Desmettre & Yevhen Havrylenko & Mogens Steffensen, 2024. "Equilibrium control theory for Kihlstrom-Mirman preferences in continuous time," Papers 2407.16525, arXiv.org, revised Oct 2024.
    15. Xiangyu Cui & Xun Li & Duan Li & Yun Shi, 2014. "Time Consistent Behavior Portfolio Policy for Dynamic Mean-Variance Formulation," Papers 1408.6070, arXiv.org, revised Aug 2015.
    16. Suping Cao & Fucai Qian & Xiaomei Wang, 2016. "Exact optimal solution for a class of dual control problems," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(9), pages 2078-2087, July.
    17. Zhang, Xili & Zhang, Weiguo & Xiao, Weilin, 2013. "Multi-period portfolio optimization under possibility measures," Economic Modelling, Elsevier, vol. 35(C), pages 401-408.
    18. Chendi Ni & Yuying Li & Peter A. Forsyth, 2023. "Neural Network Approach to Portfolio Optimization with Leverage Constraints:a Case Study on High Inflation Investment," Papers 2304.05297, arXiv.org, revised May 2023.
    19. Briec, Walter & Kerstens, Kristiaan, 2009. "Multi-horizon Markowitz portfolio performance appraisals: A general approach," Omega, Elsevier, vol. 37(1), pages 50-62, February.
    20. Xin Huang & Duan Li & Daniel Zhuoyu Long, 2020. "Scenario-decomposition Solution Framework for Nonseparable Stochastic Control Problems," Papers 2010.08985, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2303.02298. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.