IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v20y2020i3p515-526.html
   My bibliography  Save this article

Personalized goal-based investing via multi-stage stochastic goal programming

Author

Listed:
  • Woo Chang Kim
  • Do-Gyun Kwon
  • Yongjae Lee
  • Jang Ho Kim
  • Changle Lin

Abstract

In this paper, we propose a goal-based investment model that is suitable for personalized wealth management. The model only requires a few intuitive inputs such as size of wealth, investment amount, and consumption goals from individual investors. In particular, a priority level can be assigned to each consumption goal and the model provides a holistic solution based on a sequential approach starting with the highest priority. This allows strict prioritization by maximizing the probability of achieving higher priority goals that are not affected by goals with lower priorities. Furthermore, the proposed model is formulated as a linear program that efficiently finds the optimal financial plan. With its simplicity, flexibility, and computational efficiency, the proposed goal-based investment model provides a new framework for automated investment management services.

Suggested Citation

  • Woo Chang Kim & Do-Gyun Kwon & Yongjae Lee & Jang Ho Kim & Changle Lin, 2020. "Personalized goal-based investing via multi-stage stochastic goal programming," Quantitative Finance, Taylor & Francis Journals, vol. 20(3), pages 515-526, March.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:3:p:515-526
    DOI: 10.1080/14697688.2019.1662079
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2019.1662079
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697688.2019.1662079?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Lorenzo Reus & Guillermo Alexander Sepúlveda-Hurtado, 2023. "Foreign exchange trading and management with the stochastic dual dynamic programming method," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
    2. Jang Ho Kim & Yongjae Lee & Woo Chang Kim & Frank J. Fabozzi, 2022. "Goal-based investing based on multi-stage robust portfolio optimization," Annals of Operations Research, Springer, vol. 313(2), pages 1141-1158, June.
    3. Cardillo, Giovanni & Chiappini, Helen, 2024. "Robo-advisors: A systematic literature review," Finance Research Letters, Elsevier, vol. 62(PA).
    4. Bin Zhou & Jie Jiang & Hailin Sun, 2024. "Dynamic stochastic projection method for multistage stochastic variational inequalities," Computational Optimization and Applications, Springer, vol. 89(2), pages 485-516, November.
    5. Jang Ho Kim & Woo Chang Kim & Frank J. Fabozzi, 2021. "Sparse factor model based on trend filtering," Annals of Operations Research, Springer, vol. 306(1), pages 321-342, November.
    6. Zack Jourdan & J. Ken. Corley & Randall Valentine & Arthur M. Tran, 2023. "Fintech: A content analysis of the finance and information systems literature," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-21, December.
    7. Chul Jang & Andrew Clare & Iqbal Owadally, 2024. "Liability-driven investment for pension funds: stochastic optimization with real assets," Risk Management, Palgrave Macmillan, vol. 26(3), pages 1-32, September.
    8. Das, Sanjiv R. & Ostrov, Daniel & Radhakrishnan, Anand & Srivastav, Deep, 2022. "Dynamic optimization for multi-goals wealth management," Journal of Banking & Finance, Elsevier, vol. 140(C).

    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:taf:quantf:v:20:y:2020:i:3:p:515-526. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    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.