IDEAS home Printed from https://ideas.repec.org/a/eee/jbfina/v113y2020ics0378426620300261.html
   My bibliography  Save this article

Debiased expert forecasts in continuous-time asset allocation

Author

Listed:
  • Davis, Mark
  • Lleo, Sébastien

Abstract

Expert forecasts are an essential component of asset management and an important research topic. However, the effect of behavioral biases on expert forecasts is generally ignored. This paper examines the effect of biased expert forecasts on asset allocations. We find that biases have a significant impact on portfolios, explaining nearly 70% of excess risk-taking in our implementation. To address the effect of behavioral biases, we propose an integrated behavioral continuous-time portfolio selection model which we solve in closed form. The model applies general principles to identify and reduce the impact of five main behavioral biases. This paper concludes with a new personal fractional Kelly decomposition to account for the effect of opinions on the optimal asset allocation.

Suggested Citation

  • Davis, Mark & Lleo, Sébastien, 2020. "Debiased expert forecasts in continuous-time asset allocation," Journal of Banking & Finance, Elsevier, vol. 113(C).
    • Handle: RePEc:eee:jbfina:v:113:y:2020:i:c:s0378426620300261
    DOI: 10.1016/j.jbankfin.2020.105759
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378426620300261
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbankfin.2020.105759?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.

    References listed on IDEAS

    as
    1. Rudiger Frey & Abdelali Gabih & Ralf Wunderlich, 2013. "Portfolio Optimization under Partial Information with Expert Opinions: a Dynamic Programming Approach," Papers 1303.2513, arXiv.org, revised Feb 2014.
    2. Klayman, Joshua & Soll, Jack B. & Gonzalez-Vallejo, Claudia & Barlas, Sema, 1999. "Overconfidence: It Depends on How, What, and Whom You Ask, , , , , , , , ," Organizational Behavior and Human Decision Processes, Elsevier, vol. 79(3), pages 216-247, September.
    3. Yihong Xia, 2001. "Learning about Predictability: The Effects of Parameter Uncertainty on Dynamic Asset Allocation," Journal of Finance, American Finance Association, vol. 56(1), pages 205-246, February.
    4. M. J. Brennan, 1998. "The Role of Learning in Dynamic Portfolio Decisions," Review of Finance, European Finance Association, vol. 1(3), pages 295-306.
    5. Mark H A Davis & Sébastien Lleo, 2014. "Risk-Sensitive Investment Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9026, September.
    6. Rüdiger Frey & Abdelali Gabih & Ralf Wunderlich, 2012. "Portfolio Optimization Under Partial Information With Expert Opinions," World Scientific Book Chapters, in: Matheus R Grasselli & Lane P Hughston (ed.), Finance at Fields, chapter 11, pages 265-282, World Scientific Publishing Co. Pte. Ltd..
    7. Dimitris Bertsimas & Vishal Gupta & Ioannis Ch. Paschalidis, 2012. "Inverse Optimization: A New Perspective on the Black-Litterman Model," Operations Research, INFORMS, vol. 60(6), pages 1389-1403, December.
    8. Rüdiger Frey & Abdelali Gabih & Ralf Wunderlich, 2012. "Portfolio Optimization Under Partial Information With Expert Opinions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 1-18.
    9. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    10. Abdelali Gabih & Hakam Kondakji & Jorn Sass & Ralf Wunderlich, 2014. "Expert Opinions and Logarithmic Utility Maximization in a Market with Gaussian Drift," Papers 1402.6313, arXiv.org.
    11. Thomas M. Cover, 1991. "Universal Portfolios," Mathematical Finance, Wiley Blackwell, vol. 1(1), pages 1-29, January.
    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. Benchimol, Jonathan & El-Shagi, Makram & Saadon, Yossi, 2022. "Do expert experience and characteristics affect inflation forecasts?," Journal of Economic Behavior & Organization, Elsevier, vol. 201(C), pages 205-226.
    2. Kexin Chen & Hoi Ying Wong, 2024. "Duality in optimal consumption–investment problems with alternative data," Finance and Stochastics, Springer, vol. 28(3), pages 709-758, July.
    3. Jan Obłój & Thaleia Zariphopoulou, 2021. "In memoriam: Mark H. A. Davis and his contributions to mathematical finance," Mathematical Finance, Wiley Blackwell, vol. 31(4), pages 1099-1110, October.
    4. Lleo, Sébastien & Runggaldier, Wolfgang J., 2024. "On the separation of estimation and control in risk-sensitive investment problems under incomplete observation," European Journal of Operational Research, Elsevier, vol. 316(1), pages 200-214.
    5. 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.
    6. Abdelali Gabih & Hakam Kondakji & Ralf Wunderlich, 2018. "Asymptotic Filter Behavior for High-Frequency Expert Opinions in a Market with Gaussian Drift," Papers 1812.03453, arXiv.org, revised Mar 2020.
    7. Kexin Chen & Hoi Ying Wong, 2022. "Duality in optimal consumption--investment problems with alternative data," Papers 2210.08422, arXiv.org, revised Jul 2023.
    8. S'ebastien Lleo & Wolfgang J. Runggaldier, 2023. "On the Separation of Estimation and Control in Risk-Sensitive Investment Problems under Incomplete Observation," Papers 2304.08910, arXiv.org, revised Nov 2023.

    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. Katia Colaneri & Stefano Herzel & Marco Nicolosi, 2021. "The value of knowing the market price of risk," Annals of Operations Research, Springer, vol. 299(1), pages 101-131, April.
    2. 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.
    3. Jörn Sass & Dorothee Westphal & Ralf Wunderlich, 2017. "Expert Opinions And Logarithmic Utility Maximization For Multivariate Stock Returns With Gaussian Drift," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-41, June.
    4. Abdelali Gabih & Hakam Kondakji & Ralf Wunderlich, 2018. "Asymptotic Filter Behavior for High-Frequency Expert Opinions in a Market with Gaussian Drift," Papers 1812.03453, arXiv.org, revised Mar 2020.
    5. Jorn Sass & Dorothee Westphal & Ralf Wunderlich, 2016. "Expert Opinions and Logarithmic Utility Maximization for Multivariate Stock Returns with Gaussian Drift," Papers 1601.08155, arXiv.org, revised Mar 2016.
    6. Abdelali Gabih & Hakam Kondakji & Ralf Wunderlich, 2023. "Power Utility Maximization with Expert Opinions at Fixed Arrival Times in a Market with Hidden Gaussian Drift," Papers 2301.06847, arXiv.org, revised Jun 2024.
    7. Abdelali Gabih & Hakam Kondakji & Ralf Wunderlich, 2022. "Well Posedness of Utility Maximization Problems Under Partial Information in a Market with Gaussian Drift," Papers 2205.08614, arXiv.org, revised Jul 2024.
    8. Abdelali Gabih & Ralf Wunderlich, 2023. "Portfolio Optimization in a Market with Hidden Gaussian Drift and Randomly Arriving Expert Opinions: Modeling and Theoretical Results," Papers 2308.02049, arXiv.org, revised Jun 2024.
    9. Jorn Sass & Dorothee Westphal & Ralf Wunderlich, 2018. "Diffusion Approximations for Expert Opinions in a Financial Market with Gaussian Drift," Papers 1807.00568, arXiv.org, revised Mar 2020.
    10. Abdelali Gabih & Hakam Kondakji & Jorn Sass & Ralf Wunderlich, 2014. "Expert Opinions and Logarithmic Utility Maximization in a Market with Gaussian Drift," Papers 1402.6313, arXiv.org.
    11. Szölgyenyi Michaela, 2015. "Dividend maximization in a hidden Markov switching model," Statistics & Risk Modeling, De Gruyter, vol. 32(3-4), pages 143-158, December.
    12. Jakub W. Jurek & Luis M. Viceira, 2011. "Optimal Value and Growth Tilts in Long-Horizon Portfolios," Review of Finance, European Finance Association, vol. 15(1), pages 29-74.
    13. Lundtofte, Frederik, 2005. "Can An ”Estimation Factor” Help Explain Cross-Sectional Returns?," Working Papers 2005:18, Lund University, Department of Economics.
    14. S'ebastien Lleo & Wolfgang J. Runggaldier, 2023. "On the Separation of Estimation and Control in Risk-Sensitive Investment Problems under Incomplete Observation," Papers 2304.08910, arXiv.org, revised Nov 2023.
    15. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    16. Frederik Lundtofte, 2009. "Can An 'Estimation Factor' Help Explain Cross-Sectional Returns?," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 36(5-6), pages 705-724.
    17. Kexin Chen & Hoi Ying Wong, 2022. "Duality in optimal consumption--investment problems with alternative data," Papers 2210.08422, arXiv.org, revised Jul 2023.
    18. Brennan, Michael J & Xia, Yihong, 2001. "Assessing Asset Pricing Anomalies," The Review of Financial Studies, Society for Financial Studies, vol. 14(4), pages 905-942.
    19. Lleo, Sébastien & Runggaldier, Wolfgang J., 2024. "On the separation of estimation and control in risk-sensitive investment problems under incomplete observation," European Journal of Operational Research, Elsevier, vol. 316(1), pages 200-214.
    20. Michaela Szolgyenyi, 2016. "Dividend maximization in a hidden Markov switching model," Papers 1602.04656, arXiv.org.

    More about this item

    Keywords

    Behavioral finance; Black-Litterman; Expert opinions; Kalman filter; Portfolio selection; Stochastic control;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    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:eee:jbfina:v:113:y:2020:i:c:s0378426620300261. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbf .

    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.