IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2307.03391.html
   My bibliography  Save this paper

On Unified Adaptive Portfolio Management

Author

Listed:
  • Chi-Lin Li
  • Chung-Han Hsieh

Abstract

This paper introduces a unified framework for adaptive portfolio management, integrating dynamic Black-Litterman (BL) optimization with the general factor model, Elastic Net regression, and mean-variance portfolio optimization, which allows us to generate investors views and mitigate potential estimation errors systematically. Specifically, we propose an innovative dynamic sliding window algorithm to respond to the constantly changing market conditions. This algorithm allows for the flexible window size adjustment based on market volatility, generating robust estimates for factor modeling, time-varying BL estimations, and optimal portfolio weights. Through extensive ten-year empirical studies using the top 100 capitalized assets in the S&P 500 index, accounting for turnover transaction costs, we demonstrate that this combined approach leads to computational advantages and promising trading performances.

Suggested Citation

  • Chi-Lin Li & Chung-Han Hsieh, 2023. "On Unified Adaptive Portfolio Management," Papers 2307.03391, arXiv.org, revised Apr 2024.
    • Handle: RePEc:arx:papers:2307.03391
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2307.03391
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Barua, Ronil & Sharma, Anil K., 2022. "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, vol. 49(C).
    2. Guiso, Luigi & Sapienza, Paola & Zingales, Luigi, 2018. "Time varying risk aversion," Journal of Financial Economics, Elsevier, vol. 128(3), pages 403-421.
    3. Ang, Andrew, 2014. "Asset Management: A Systematic Approach to Factor Investing," OUP Catalogue, Oxford University Press, number 9780199959327.
    4. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    5. Michael J. Best & Robert R. Grauer, 1991. "Sensitivity Analysis for Mean-Variance Portfolio Problems," Management Science, INFORMS, vol. 37(8), pages 980-989, August.
    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. Hsieh, Chung-Han, 2024. "On solving robust log-optimal portfolio: A supporting hyperplane approximation approach," European Journal of Operational Research, Elsevier, vol. 313(3), pages 1129-1139.

    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. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
    2. Carlo A. Favero & Alessandro Melone, 2019. "Asset Pricing vs Asset Expected Returning in Factor Models," Working Papers 651, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, December.
    4. Alexander Berglund & Massimo Guidolin & Manuela Pedio, 2020. "Monetary policy after the crisis: A threat to hedge funds' alphas?," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 219-238, May.
    5. Thierry Roncalli, 2018. "Keep up the momentum," Journal of Asset Management, Palgrave Macmillan, vol. 19(5), pages 351-361, September.
    6. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    7. Gala, Vito D. & Pagliardi, Giovanni & Zenios, Stavros A., 2023. "Global political risk and international stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 78-102.
    8. Jang Ho Kim & Woo Chang Kim & Frank J. Fabozzi, 2017. "Penalizing variances for higher dependency on factors," Quantitative Finance, Taylor & Francis Journals, vol. 17(4), pages 479-489, April.
    9. Marie Brière & Ariane Szafarz, 2021. "When it rains, it pours: Multifactor asset management in good and bad times," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(3), pages 641-669, September.
    10. Schlag, Christian & Zeng, Kailin, 2019. "Horizontal industry relationships and return predictability," SAFE Working Paper Series 256, Leibniz Institute for Financial Research SAFE.
    11. Zaremba, Adam & Czapkiewicz, Anna, 2017. "The cross section of international government bond returns," Economic Modelling, Elsevier, vol. 66(C), pages 171-183.
    12. Marie Briere & Ariane Szafarz, 2015. "Factor-Based v. Industry-Based Asset Allocation: The Contest," Working Papers CEB 15-035, ULB -- Universite Libre de Bruxelles.
    13. Paweł Sakowski & Robert Ślepaczuk & Mateusz Wywiał, 2016. "Can We Invest Based on Equity Risk Premia and Risk Factors from Multi-Factor Models?," Working Papers 2016-09, Faculty of Economic Sciences, University of Warsaw.
    14. Wolfgang Bessler & Georgi Taushanov & Dominik Wolff, 2021. "Factor investing and asset allocation strategies: a comparison of factor versus sector optimization," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 488-506, October.
    15. Hua Fan, John & Michalski, Lachlan, 2020. "Sustainable factor investing: Where doing well meets doing good," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 230-256.
    16. Mark H.A. Davis & Sébastien Lleo, 2021. "Risk‐sensitive benchmarked asset management with expert forecasts," Mathematical Finance, Wiley Blackwell, vol. 31(4), pages 1162-1189, October.
    17. Kewei Hou & Chen Xue & Lu Zhang, 2017. "Replicating Anomalies," NBER Working Papers 23394, National Bureau of Economic Research, Inc.
    18. Francesco Chincoli & Massimo Guidolin, 2017. "Linear and nonlinear predictability in investment style factors: multivariate evidence," Journal of Asset Management, Palgrave Macmillan, vol. 18(6), pages 476-509, October.
    19. Ko, Hyungjin & Son, Bumho & Lee, Jaewook, 2024. "A novel integration of the Fama–French and Black–Litterman models to enhance portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    20. Marie Briere & Ariane Szafarz, 2017. "Factor Investing: The Rocky Road from Long-Only to Long-Short," Working Papers CEB 17-013, ULB -- Universite Libre de Bruxelles.

    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:2307.03391. 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.