IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v27y2018icp201-207.html
   My bibliography  Save this article

On an adaptive Black–Litterman investment strategy using conditional fundamentalist information: A Brazilian case study

Author

Listed:
  • Fernandes, Betina
  • Street, Alexandre
  • Fernandes, Cristiano
  • Valladão, Davi

Abstract

We propose an investment strategy based on the Black–Litterman model with conditional information. We present how observed price-earnings ratio and past returns can be used to determine 1-step ahead returns, considering investors with different risk profiles. The provided approach updates the conditional probability distribution of asset returns and mitigates asset allocation instability due to estimation errors. Our case study using Brazilian data shows the resulting optimal portfolios outperform traditional mean-variance portfolios even in an emerging market with one of the highest nominal interest rates.

Suggested Citation

  • Fernandes, Betina & Street, Alexandre & Fernandes, Cristiano & Valladão, Davi, 2018. "On an adaptive Black–Litterman investment strategy using conditional fundamentalist information: A Brazilian case study," Finance Research Letters, Elsevier, vol. 27(C), pages 201-207.
    • Handle: RePEc:eee:finlet:v:27:y:2018:i:c:p:201-207
    DOI: 10.1016/j.frl.2018.03.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612317302969
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2018.03.006?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. Kim, Jang Ho & Kim, Woo Chang & Fabozzi, Frank J., 2013. "Composition of robust equity portfolios," Finance Research Letters, Elsevier, vol. 10(2), pages 72-81.
    2. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    3. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, Wiley Blackwell, vol. 38, pages 1-41.
    4. Kolm, Petter N. & Tütüncü, Reha & Fabozzi, Frank J., 2014. "60 Years of portfolio optimization: Practical challenges and current trends," European Journal of Operational Research, Elsevier, vol. 234(2), pages 356-371.
    5. Harris, Richard D.F. & Stoja, Evarist & Tan, Linzhi, 2017. "The dynamic Black–Litterman approach to asset allocation," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1085-1096.
    6. Wolfgang Bessler & Heiko Opfer & Dominik Wolff, 2017. "Multi-asset portfolio optimization and out-of-sample performance: an evaluation of Black–Litterman, mean-variance, and naïve diversification approaches," The European Journal of Finance, Taylor & Francis Journals, vol. 23(1), pages 1-30, 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. María Antonia Truyols-Pont & Amelia Bilbao-Terol & Mar Arenas-Parra, 2024. "Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market," Mathematics, MDPI, vol. 12(24), pages 1-17, December.
    2. Ko, Hyungjin & Byun, Junyoung & Lee, Jaewook, 2023. "A privacy-preserving robo-advisory system with the Black-Litterman portfolio model: A new framework and insights into investor behavior," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    3. Barua, Ronil & Sharma, Anil K., 2022. "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, vol. 49(C).

    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. Platanakis, Emmanouil & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Horses for courses: Mean-variance for asset allocation and 1/N for stock selection," European Journal of Operational Research, Elsevier, vol. 288(1), pages 302-317.
    2. Xidonas, Panos & Hassapis, Christis & Soulis, John & Samitas, Aristeidis, 2017. "Robust minimum variance portfolio optimization modelling under scenario uncertainty," Economic Modelling, Elsevier, vol. 64(C), pages 60-71.
    3. Erindi Allaj, 2020. "The Black–Litterman model and views from a reverse optimization procedure: an out-of-sample performance evaluation," Computational Management Science, Springer, vol. 17(3), pages 465-492, October.
    4. Xidonas, Panos & Mavrotas, George & Hassapis, Christis & Zopounidis, Constantin, 2017. "Robust multiobjective portfolio optimization: A minimax regret approach," European Journal of Operational Research, Elsevier, vol. 262(1), pages 299-305.
    5. Ghaemi Asl, Mahdi & Rashidi, Muhammad Mahdi & Tavakkoli, Hamid Raza & Rezgui, Hichem, 2024. "Does Islamic investing modify portfolio performance? Time-varying optimization strategies for conventional and Shariah energy-ESG-utilities portfolio," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 37-57.
    6. Newton, David & Platanakis, Emmanouil & Stafylas, Dimitrios & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Hedge fund strategies, performance &diversification: A portfolio theory & stochastic discount factor approach," The British Accounting Review, Elsevier, vol. 53(5).
    7. Jang Ho Kim & Woo Chang Kim & Do-Gyun Kwon & Frank J. Fabozzi, 2018. "Robust equity portfolio performance," Annals of Operations Research, Springer, vol. 266(1), pages 293-312, July.
    8. Adriana S. Cordis, 2014. "Accounting Ratios and the Cross-section of Expected Stock Returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 41(9-10), pages 1157-1192, November.
    9. Pagnoncelli, Bernardo K. & Cifuentes, Arturo & Denis, Gabriela, 2017. "A two-step hybrid investment strategy for pension funds," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 574-583.
    10. Samuel YM Ze‐To, 2022. "Fundamental index aligned and excess market return predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 592-614, April.
    11. Platanakis, Emmanouil & Sakkas, Athanasios & Sutcliffe, Charles, 2019. "Harmful diversification: Evidence from alternative investments," The British Accounting Review, Elsevier, vol. 51(1), pages 1-23.
    12. Sandra Caçador & Joana Matos Dias & Pedro Godinho, 2020. "Global minimum variance portfolios under uncertainty: a robust optimization approach," Journal of Global Optimization, Springer, vol. 76(2), pages 267-293, February.
    13. Panos Xidonas & Ralph Steuer & Christis Hassapis, 2020. "Robust portfolio optimization: a categorized bibliographic review," Annals of Operations Research, Springer, vol. 292(1), pages 533-552, September.
    14. Platanakis, Emmanouil & Urquhart, Andrew, 2020. "Should investors include Bitcoin in their portfolios? A portfolio theory approach," The British Accounting Review, Elsevier, vol. 52(4).
    15. Croce, M.M. & Nguyen, Thien T. & Raymond, S. & Schmid, L., 2019. "Government debt and the returns to innovation," Journal of Financial Economics, Elsevier, vol. 132(3), pages 205-225.
    16. Rhys Bidder & Ian Dew-Becker, 2016. "Long-Run Risk Is the Worst-Case Scenario," American Economic Review, American Economic Association, vol. 106(9), pages 2494-2527, September.
    17. Kevin J. Lansing, 2011. "Asset pricing with concentrated ownership of capital," Working Paper 2011/18, Norges Bank.
    18. Dichev, Ilia D. & Qian, Jingyi, 2022. "The benefits of transaction-level data: The case of NielsenIQ scanner data," Journal of Accounting and Economics, Elsevier, vol. 74(1).
    19. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    20. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.

    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:finlet:v:27:y:2018:i:c:p:201-207. 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/frl .

    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.