IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1911.05309.html
   My bibliography  Save this paper

Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling

Author

Listed:
  • Mengying Zhu
  • Xiaolin Zheng
  • Yan Wang
  • Yuyuan Li
  • Qianqiao Liang

Abstract

As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted portfolios can even get better performance. Under these circumstances, we can use multiple classic strategies as multiple strategic arms in multi-armed bandit to naturally establish a connection with the portfolio selection problem. This can also help to maximize the rewards in the bandit algorithm by the trade-off between exploration and exploitation. In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms. Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods. Moreover, we devise a novel reward function based on users' different investment risk preferences, which can be adaptive to various investment styles. Our experimental results demonstrate that our proposed portfolio strategy has marked superiority across representative real-world market datasets in terms of extensive evaluation criteria.

Suggested Citation

  • Mengying Zhu & Xiaolin Zheng & Yan Wang & Yuyuan Li & Qianqiao Liang, 2019. "Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling," Papers 1911.05309, arXiv.org, revised Nov 2019.
    • Handle: RePEc:arx:papers:1911.05309
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1911.05309
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    3. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
    4. Daniel Russo & Benjamin Van Roy, 2014. "Learning to Optimize via Posterior Sampling," Mathematics of Operations Research, INFORMS, vol. 39(4), pages 1221-1243, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hongda Hu & Arthur Charpentier & Mario Ghossoub & Alexander Schied, 2022. "Multiarmed Bandits Problem Under the Mean-Variance Setting," Papers 2212.09192, arXiv.org, revised May 2024.

    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. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    2. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    3. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    4. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
    5. Yasuhiro Nakayama & Tomochika Sawaki, 2023. "Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning," Papers 2311.04946, arXiv.org.
    6. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    7. Yunan Ye & Hengzhi Pei & Boxin Wang & Pin-Yu Chen & Yada Zhu & Jun Xiao & Bo Li, 2020. "Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States," Papers 2002.05780, arXiv.org.
    8. Xiangyu Cui & Xun Li & Yun Shi & Si Zhao, 2023. "Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning," Papers 2312.15385, arXiv.org.
    9. Huanming Zhang & Zhengyong Jiang & Jionglong Su, 2021. "A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management," Papers 2103.11455, arXiv.org.
    10. Kinyua, Johnson D. & Mutigwe, Charles & Cushing, Daniel J. & Poggi, Michael, 2021. "An analysis of the impact of President Trump’s tweets on the DJIA and S&P 500 using machine learning and sentiment analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    11. Yinheng Li & Junhao Wang & Yijie Cao, 2019. "A General Framework on Enhancing Portfolio Management with Reinforcement Learning," Papers 1911.11880, arXiv.org, revised Oct 2023.
    12. Zhaolu Dong & Shan Huang & Simiao Ma & Yining Qian, 2021. "Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning," Papers 2108.01758, arXiv.org.
    13. Zhenhan Huang & Fumihide Tanaka, 2021. "MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management," Papers 2102.03502, arXiv.org, revised Feb 2022.
    14. Saeed Marzban & Erick Delage & Jonathan Yumeng Li & Jeremie Desgagne-Bouchard & Carl Dussault, 2021. "WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio Management," Papers 2109.07005, arXiv.org, revised Sep 2021.
    15. Ben Hambly & Renyuan Xu & Huining Yang, 2023. "Recent advances in reinforcement learning in finance," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 437-503, July.
    16. MohammadAmin Fazli & Mahdi Lashkari & Hamed Taherkhani & Jafar Habibi, 2022. "A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management," Papers 2212.14477, arXiv.org.
    17. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Time your hedge with Deep Reinforcement Learning," Papers 2009.14136, arXiv.org, revised Nov 2020.
    18. Ricard Durall, 2022. "Asset Allocation: From Markowitz to Deep Reinforcement Learning," Papers 2208.07158, arXiv.org.
    19. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    20. Gang Huang & Xiaohua Zhou & Qingyang Song, 2020. "Deep reinforcement learning for portfolio management," Papers 2012.13773, arXiv.org, revised Apr 2022.

    More about this item

    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:1911.05309. 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.