IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v12y2024i3p69-d1434304.html
   My bibliography  Save this article

Normal Asset Allocations and Their Statistical Properties

Author

Listed:
  • Luca Ghezzi

    (Department of Integrated Business Management, LIUC Università Carlo Cattaneo, Corso Matteotti 22, 21053 Castellanza, Italy)

Abstract

This study focuses on efficient asset allocations that properly include T-bills, T-bonds, and the S&P 500 stock index. It checks that their annual real rates of linear return are both normal and almost lognormal. It reexamines how efficient portfolios based on the rates of linear return may turn into efficient portfolios based on the rates of logarithmic return. It finds that each efficient asset allocation has the lowest possible standard deviation as well as the highest possible arithmetic and geometric means. It eventually reconsiders the relationship between the confidence interval of a geometric mean and an expected long-run capital accumulation. As a consequence, it bridges a gap in the scientific literature by enabling financial advisors to trade off the mean rate of return on a portfolio more rigorously against the value at risk.

Suggested Citation

  • Luca Ghezzi, 2024. "Normal Asset Allocations and Their Statistical Properties," IJFS, MDPI, vol. 12(3), pages 1-14, July.
    • Handle: RePEc:gam:jijfss:v:12:y:2024:i:3:p:69-:d:1434304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/12/3/69/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/12/3/69/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," The Journal of Business, University of Chicago Press, vol. 54(4), pages 513-533, October.
    2. James Nguyen & Wei-Xuan Li & Clara Chia-Sheng Chen, 2022. "Mean Reversions in Major Developed Stock Markets: Recent Evidence from Unit Root, Spectral and Abnormal Return Studies," JRFM, MDPI, vol. 15(4), pages 1-20, April.
    3. Luigi Buzzacchi & Luca Ghezzi, 2023. "Mean Reversion Lessens Mean Blur: Evidence from the S&P Composite Index," IJFS, MDPI, vol. 11(1), pages 1-13, January.
    4. Merton, Robert C., 1972. "An Analytic Derivation of the Efficient Portfolio Frontier," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(4), pages 1851-1872, September.
    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. Gordon J. Alexander & Alexandre M. Baptista, 2004. "A Comparison of VaR and CVaR Constraints on Portfolio Selection with the Mean-Variance Model," Management Science, INFORMS, vol. 50(9), pages 1261-1273, September.
    2. Butt, Prof. Khursheed A & Pandow, Bilal Ahmad, 2013. "An analysis into the Stock Selectivity skill of Indian Fund Managers," MPRA Paper 83500, University Library of Munich, Germany, revised 2013.
    3. Jung‐Soon Shin & Minki Kim & Dongjun Oh & Tong Suk Kim, 2019. "Do hedge funds time market tail risk? Evidence from option‐implied tail risk," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(2), pages 205-237, February.
    4. Ben-Rephael, Azi & Kandel, Shmuel & Wohl, Avi, 2012. "Measuring investor sentiment with mutual fund flows," Journal of Financial Economics, Elsevier, vol. 104(2), pages 363-382.
    5. Muhinyuza, Stanislas & Bodnar, Taras & Lindholm, Mathias, 2020. "A test on the location of the tangency portfolio on the set of feasible portfolios," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    6. Pirgaip, Burak & Arslan-Ayaydin, Özgür & Karan, Mehmet Baha, 2021. "Do Sukuk provide diversification benefits to conventional bond investors? Evidence from Turkey," Global Finance Journal, Elsevier, vol. 50(C).
    7. Ding, Jing & Jiang, Lei & Liu, Xiaohui & Peng, Liang, 2023. "Nonparametric tests for market timing ability using daily mutual fund returns," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    8. Joanna Olbryś, 2010. "Three-factor market-timing models with Fama and French’s spread variables," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 20(2), pages 91-106.
    9. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    10. Shuzhen Yang, 2019. "A varying terminal time mean-variance model," Papers 1909.13102, arXiv.org, revised Jan 2020.
    11. Zheng, Yao & Osmer, Eric & Zhang, Ruiyi, 2018. "Sentiment hedging: How hedge funds adjust their exposure to market sentiment," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 147-160.
    12. Felix Fie{ss}inger & Mitja Stadje, 2024. "Mean-Variance Optimization for Participating Life Insurance Contracts," Papers 2407.11761, arXiv.org.
    13. Zied Ftiti & Aviral Tiwari & Amél Belanès & Khaled Guesmi, 2015. "Tests of Financial Market Contagion: Evolutionary Cospectral Analysis Versus Wavelet Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 575-611, December.
    14. Blake, David & Caulfield, Tristan & Ioannidis, Christos & Tonks, Ian, 2014. "Improved inference in the evaluation of mutual fund performance using panel bootstrap methods," Journal of Econometrics, Elsevier, vol. 183(2), pages 202-210.
    15. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa O., 2019. "Upside potential of hedge funds as a predictor of future performance," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 212-229.
    16. Fritsche, Ulrich & Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Forecasting the Brazilian real and the Mexican peso: Asymmetric loss, forecast rationality, and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 130-139.
    17. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    18. Andrew J. Patton & Tarun Ramadorai, 2013. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," Journal of Finance, American Finance Association, vol. 68(2), pages 597-635, April.
    19. Chiu-Ming Hsiao, 2022. "Economic Growth, CO 2 Emissions Quota and Optimal Allocation under Uncertainty," Sustainability, MDPI, vol. 14(14), pages 1-26, July.
    20. Kee-Hong Bae & Junesuh Yi, 2008. "The Impact of the Short-Short Rule Repeal on the Timing Ability of Mutual Funds," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 35(7-8), pages 969-997.

    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:gam:jijfss:v:12:y:2024:i:3:p:69-:d:1434304. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.