IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/dypw6.html
   My bibliography  Save this paper

Mutual Fund Allocations that Maximize Safe Portfolio Returns

Author

Listed:
  • Prendergast, Michael

Abstract

This paper describes an empirical analysis of optimized portfolios and safe return rates across multiple investment time horizons using Telser’s Safety-First method. The analysis uses thirty years of historical monthly data for 81 different Fidelity® mutual funds and a blended money market fund rate. The Fidelity® funds represent a wide variety of investment factors, strategies and asset types, including bonds, stocks, commodities and convertible securities. A large synthetic return dataset was generated from this data by a Monte-Carlo random walk using cointegrated bootstrapping of investment returns and yields. Portfolio optimization was then performed on this synthetic dataset for safety factors varying from 60% to 99% and time horizons varying from one month to ten years. Results from portfolio analyses include the following: 1) there are no risk-free investments available to Fidelity® mutual fund investors, as even money market funds have risk due to yield fluctuations, 2) optimized portfolios are sensitive to both investment time horizons and safety factor confidence levels, 3) conservative, short-term investors are better off leaving their money in a money market fund than investing in securities, 4) optimized portfolios for longer term, more aggressive investors consist of a blend of both value and growth equities, and 5) the funds most often represented in optimized portfolios are those that have the best risk/reward ratios, although this rule is not universal. Two practical applications of this optimization approach are also presented.

Suggested Citation

  • Prendergast, Michael, 2022. "Mutual Fund Allocations that Maximize Safe Portfolio Returns," OSF Preprints dypw6, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:dypw6
    DOI: 10.31219/osf.io/dypw6
    as

    Download full text from publisher

    File URL: https://osf.io/download/63365fedd5b01003dd1f88a1/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/dypw6?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. M. Ryan Haley & Harry J. Paarsch & Charles H. Whiteman, 2013. "Smoothed safety first and the holding of assets," Quantitative Finance, Taylor & Francis Journals, vol. 13(2), pages 167-176, January.
    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. Haley, M. Ryan, 2008. "A simple nonparametric approach to low-dimension, shortfall-based portfolio selection," Finance Research Letters, Elsevier, vol. 5(3), pages 183-190, September.
    2. Steven E. Pav, 2015. "Safety Third: Roy's Criterion and Higher Order Moments," Papers 1506.04227, arXiv.org.
    3. Minghu Ha & Yang Yang & Chao Wang, 2017. "A portfolio optimization model for minimizing soft margin-based generalization bound," Journal of Intelligent Manufacturing, Springer, vol. 28(3), pages 759-766, March.
    4. M. Ryan Haley, 2016. "Shortfall minimization and the Naive (1/N) portfolio: an out-of-sample comparison," Applied Economics Letters, Taylor & Francis Journals, vol. 23(13), pages 926-929, September.
    5. M. Ryan Haley, 2017. "K-fold cross validation performance comparisons of six naive portfolio selection rules: how naive can you be and still have successful out-of-sample portfolio performance?," Annals of Finance, Springer, vol. 13(3), pages 341-353, August.
    6. Jules Sadefo Kamdem, 2023. "Risk-Adjusted Performance And Semi-Moments Of Non-Gaussian Portfolio Returns Distributions," Working Papers hal-04134833, HAL.
    7. M. Ryan Haley, 2018. "A nonparametric quantity-of-quality approach to assessing financial asset return performance," Annals of Finance, Springer, vol. 14(3), pages 343-351, August.
    8. M. Haley, 2014. "Gaussian and logistic adaptations of smoothed safety first," Annals of Finance, Springer, vol. 10(2), pages 333-345, May.
    9. Alina Lucia Trifan, 2009. "Testing Capital Asset Pricing Model For Romanian Capital Market," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 1(11), pages 1-43.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:osf:osfxxx:dypw6. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.