IDEAS home Printed from https://ideas.repec.org/a/trp/01jefa/jefa0021.html
   My bibliography  Save this article

Analysis of Equity Beta Components: New Results and Prospectives in a Low Beta Framework

Author

Listed:
  • Antonio Amendola
  • Dennis M. Montagna
  • Mario Maggi

    (University of Pavia
    University of Pavia
    University of Pavia)

Abstract

This work aims to exploit the so-called 'Beta anomaly' regarding the risk-reward relationship, and set up rules and methodologies in order to build new efficient portfolios. It is well known in literature, and among practitioners, that 'Low Beta strategies' generate good performances exploiting alpha opportunities. In this paper, we focus on Beta parameters: we analyze this one and its components (Correlation and Standard Deviation) in order to better understand the drivers and contributions behind the 'Low Beta strategies', and eventually exploit them. We perform an extensive empirical analysis on the S&P500 and the relative sectors, covering more than 10 years. In addition, we follow Long/Short strategies in building portfolios based on Beta and their components where we compare results against the benchmark. We also introduce 'Walking Beta' approach in order to give a deep and innovative view on the market risk/reward relationship, illustrating different time frames and the evolution of risk parameters.

Suggested Citation

  • Antonio Amendola & Dennis M. Montagna & Mario Maggi, 2019. "Analysis of Equity Beta Components: New Results and Prospectives in a Low Beta Framework," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 3(1), pages 1-26.
  • Handle: RePEc:trp:01jefa:jefa0021
    DOI: http://dx.doi.org/10.1991/jefa.v3i1.a21
    as

    Download full text from publisher

    File URL: https://ojs.tripaledu.com/index.php/jefa/article/download/41/45
    Download Restriction: no

    File URL: https://ojs.tripaledu.com/index.php/jefa/article/view/41/50
    Download Restriction: no

    File URL: https://libkey.io/http://dx.doi.org/10.1991/jefa.v3i1.a21?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
    ---><---

    References listed on IDEAS

    as
    1. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
    2. Ang, Andrew & Hodrick, Robert J. & Xing, Yuhang & Zhang, Xiaoyan, 2009. "High idiosyncratic volatility and low returns: International and further U.S. evidence," Journal of Financial Economics, Elsevier, vol. 91(1), pages 1-23, January.
    3. Frazzini, Andrea & Pedersen, Lasse Heje, 2014. "Betting against beta," Journal of Financial Economics, Elsevier, vol. 111(1), pages 1-25.
    4. Malcolm Baker & Mathias F. Hoeyer & Jeffrey Wurgler, 2016. "The Risk Anomaly Tradeoff of Leverage," NBER Working Papers 22116, National Bureau of Economic Research, Inc.
    5. Eugene F. Fama & Kenneth R. French, 2004. "The Capital Asset Pricing Model: Theory and Evidence," Journal of Economic Perspectives, American Economic Association, vol. 18(3), pages 25-46, Summer.
    6. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, August.
    7. Blitz, D.C. & van Vliet, P., 2007. "The Volatility Effect: Lower Risk without Lower Return," ERIM Report Series Research in Management ERS-2007-044-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    Full references (including those not matched with items on IDEAS)

    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. Maillet, Bertrand & Tokpavi, Sessi & Vaucher, Benoit, 2015. "Global minimum variance portfolio optimisation under some model risk: A robust regression-based approach," European Journal of Operational Research, Elsevier, vol. 244(1), pages 289-299.
    2. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, December.
    3. Pyo, Sujin & Lee, Jaewook, 2018. "Exploiting the low-risk anomaly using machine learning to enhance the Black–Litterman framework: Evidence from South Korea," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 1-12.
    4. Matthias M. M. Buehlmaier & Kit Pong Wong, 2020. "Should investors join the index revolution? Evidence from around the world," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 192-218, May.
    5. Joshua Traut, 2023. "What we know about the low-risk anomaly: a literature review," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(3), pages 297-324, September.
    6. Patrick Bielstein & Matthias X. Hanauer, 2019. "Mean-variance optimization using forward-looking return estimates," Review of Quantitative Finance and Accounting, Springer, vol. 52(3), pages 815-840, April.
    7. Suzanne G. M. Fifield & David G. McMillan & Fiona J. McMillan, 2020. "Is there a risk and return relation?," The European Journal of Finance, Taylor & Francis Journals, vol. 26(11), pages 1075-1101, July.
    8. Adam ZAREMBA, 2015. "Low Risk Anomaly In The Cee Stock Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 81-102, September.
    9. Andreas Oehler & Julian Schneider, 2022. "Gambling with lottery stocks?," Journal of Asset Management, Palgrave Macmillan, vol. 23(6), pages 477-503, October.
    10. 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.
    11. Jaehyung Choi & Hyangju Kim & Young Shin Kim, 2021. "Diversified reward-risk parity in portfolio construction," Papers 2106.09055, arXiv.org, revised Sep 2022.
    12. Flögel, Volker & Schlag, Christian & Zunft, Claudia, 2022. "Momentum-Managed Equity Factors," Journal of Banking & Finance, Elsevier, vol. 137(C).
    13. Auh, Jun Kyung & Cho, Wonho, 2023. "Factor-based portfolio optimization," Economics Letters, Elsevier, vol. 228(C).
    14. Grønborg, Niels S. & Lunde, Asger & Olesen, Kasper V. & Vander Elst, Harry, 2022. "Realizing correlations across asset classes," Journal of Financial Markets, Elsevier, vol. 59(PA).
    15. Thomas Trier Bjerring & Omri Ross & Alex Weissensteiner, 2017. "Feature selection for portfolio optimization," Annals of Operations Research, Springer, vol. 256(1), pages 21-40, September.
    16. Poon, Percy & Yao, Tong & Zhang, Andrew (Jianzhong), 2022. "The alphas of beta and idiosyncratic volatility," Journal of Financial Markets, Elsevier, vol. 61(C).
    17. Mabekebeke Segojane & Godfrey Ndlovu, 2022. "An Investigation of the Beta Anomaly in Emerging Markets: A South African Case," JRFM, MDPI, vol. 15(5), pages 1-18, May.
    18. Christoffersen, Peter & Langlois, Hugues, 2013. "The Joint Dynamics of Equity Market Factors," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(5), pages 1371-1404, October.
    19. S. Ciliberti & Y. Lemp'eri`ere & A. Beveratos & G. Simon & L. Laloux & M. Potters & J. P. Bouchaud, 2015. "Deconstructing the Low-Vol Anomaly," Papers 1510.01679, arXiv.org, revised Oct 2015.
    20. Olivier, Jacques & Dessaint, Olivier & Otto, Clemens A. & Thesmar, David, 2017. "CAPM-Based Company (Mis)valuations," HEC Research Papers Series 1235, HEC Paris, revised 20 Mar 2018.

    More about this item

    Keywords

    Asset Allocation; Quantitative Portfolio Management; CAPM; Hedge Funds; Correlation; Beta Anomaly.;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:trp:01jefa:jefa0021. 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: David Simon Hall (email available below). General contact details of provider: .

    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.