IDEAS home Printed from https://ideas.repec.org/p/ags/huaewp/232632.html
   My bibliography  Save this paper

Evaluating the Mean-Gini Approach to Portfolio Selection

Author

Listed:
  • Shalit, Haim
  • Yitzhaki, Shlomo

Abstract

This paper evaluates the empirical properties of the mean-Gini (MG) and the mean-extended Gini (MEG) efficient sets by comparing their performance to the mean-variance (MV) portfolio selection. The analysis focuses on the similarities and differences existing between the MV, the MG, and the various MEG efficient sets. In addition, the risk parameter for which the MEG efficient set is best supported by the market data is estimated. The analysis is carried out with respect to the Tel-Aviv Stock Exchange to present empirically a new approach to portfolio selection.

Suggested Citation

  • Shalit, Haim & Yitzhaki, Shlomo, 1985. "Evaluating the Mean-Gini Approach to Portfolio Selection," Working Papers 232632, Hebrew University of Jerusalem, Center for Agricultural Economic Research.
    • Handle: RePEc:ags:huaewp:232632
    DOI: 10.22004/ag.econ.232632
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/232632/files/hebrewuniv-workingpapers-8507.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.232632?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. Shalit, Haim & Yitzhaki, Shlomo, 1984. "Mean-Gini, Portfolio Theory, and the Pricing of Risky Assets," Journal of Finance, American Finance Association, vol. 39(5), pages 1449-1468, December.
    2. Frankfurter, George M. & Phillips, Herbert E., 1975. "Efficient Algorithms for Conducting Stochastic Dominance Tests on Large Numbers of Portfolios: A Comment," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 10(1), pages 177-179, March.
    3. Bey, Roger P. & Howe, Keith M., 1984. "Gini's Mean Difference and Portfolio Selection: An Empirical Evaluation," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 19(3), pages 329-338, September.
    4. Dybvig, Philip H & Ross, Stephen A, 1982. "Portfolio Efficient Sets," Econometrica, Econometric Society, vol. 50(6), pages 1525-1546, November.
    5. Bey, Roger P., 1979. "Estimating the Optimal Stochastic Dominance Efficient Set with a Mean-Semivariance Algorithm," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 14(5), pages 1059-1070, December.
    6. Porter, R. Burr, 1973. "An Empirical Comparison of Stochastic Dominance and Mean-Variance Portfolio Choice Criteria," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 8(4), pages 587-608, September.
    7. Porter, R. Burr & Pfaffenberger, Roger C., 1975. "Efficient Algorithms for Conducting Stochastic Dominance Tests on Large Numbers of Portfolios: Reply," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 10(1), pages 181-185, March.
    8. Yitzhaki, Shlomo, 1982. "Stochastic Dominance, Mean Variance, and Gini's Mean Difference," American Economic Review, American Economic Association, vol. 72(1), pages 178-185, March.
    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. Cochran, Mark J., 1986. "Stochastic Dominance: The State Of The Art In Agricultural Economics," Regional Research Projects > 1986: S-180 Annual Meeting, March 23-26, 1986, Tampa, Florida 271995, Regional Research Projects > S-180: An Economic Analysis of Risk Management Strategies for Agricultural Production Firms.

    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. Wetzstein, Michael E. & Szmedra, Philip I. & McClendon, Ronald W. & Edwards, David M., 1988. "Efficiency Criteria And Risk Aversion: An Empirical Evaluation," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 20(1), pages 1-8, July.
    2. Cochran, Mark J., 1986. "Stochastic Dominance: The State Of The Art In Agricultural Economics," Regional Research Projects > 1986: S-180 Annual Meeting, March 23-26, 1986, Tampa, Florida 271995, Regional Research Projects > S-180: An Economic Analysis of Risk Management Strategies for Agricultural Production Firms.
    3. Timo Kuosmanen, 2004. "Efficient Diversification According to Stochastic Dominance Criteria," Management Science, INFORMS, vol. 50(10), pages 1390-1406, October.
    4. Ran Ji & Miguel A. Lejeune & Srinivas Y. Prasad, 2017. "Properties, formulations, and algorithms for portfolio optimization using Mean-Gini criteria," Annals of Operations Research, Springer, vol. 248(1), pages 305-343, January.
    5. Darren Butterworth & Phil Holmes, 2005. "The Hedging Effectiveness of U.K. Stock Index Futures Contracts Using an Extended Mean Gini Approach: Evidence for the FTSE 100 and FTSE Mid250 Contracts," Multinational Finance Journal, Multinational Finance Journal, vol. 9(3-4), pages 131-160, September.
    6. Haim Shalit & Shlomo Yitzhaki, 2009. "Capital market equilibrium with heterogeneous investors," Quantitative Finance, Taylor & Francis Journals, vol. 9(6), pages 757-766.
    7. Haim Shalit, 1995. "Mean-Gini analysis of stochastic externalities: The case of groundwater contamination," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 6(1), pages 37-52, July.
    8. Trajtenberg, Manuel & Yitzhaki, Shlomo, 1989. "The Diffusion of Innovations: A Methodological Reappraisal," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 35-47, January.
    9. Miguel A. Lejeune & John Turner, 2019. "Planning Online Advertising Using Gini Indices," Operations Research, INFORMS, vol. 67(5), pages 1222-1245, September.
    10. Blavatskyy, Pavlo, 2016. "Probability weighting and L-moments," European Journal of Operational Research, Elsevier, vol. 255(1), pages 103-109.
    11. Zhenlong Jiang & Ran Ji & Kuo-Chu Chang, 2020. "A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment," JRFM, MDPI, vol. 13(7), pages 1-20, July.
    12. Maria-Teresa Bosch-Badia & Joan Montllor-Serrats & Maria-Antonia Tarrazon-Rodon, 2017. "Analysing assets’ performance inside a portfolio: From crossed beta to the net risk premium ratio," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1270251-127, January.
    13. Kaplanski, Guy & Kroll, Yoram, 2002. "VaR Risk Measures versus Traditional Risk Measures: an Analysis and Survey," MPRA Paper 80070, University Library of Munich, Germany.
    14. Shlomo Yitzhaki, 2003. "Gini’s Mean difference: a superior measure of variability for non-normal distributions," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 285-316.
    15. Sadefo Kamdem, Jules, 2012. "A nice estimation of Gini index and power Pen's parade," Economic Modelling, Elsevier, vol. 29(4), pages 1299-1304.
    16. Rachida Hennani & Michel Terraza, 2015. "Contributions of a noisy chaotic model to the stressed Value-at-Risk," Economics Bulletin, AccessEcon, vol. 35(2), pages 1262-1273.
    17. Haim Shalit, 2021. "The Shapley value decomposition of optimal portfolios," Annals of Finance, Springer, vol. 17(1), pages 1-25, March.
    18. Haim Shalit, 2020. "The Shapley value of regression portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 21(6), pages 506-512, October.
    19. David Shaffer & Andrea DeMaskey, 2005. "Currency Hedging Using the Mean-Gini Framework," Review of Quantitative Finance and Accounting, Springer, vol. 25(2), pages 125-137, September.
    20. Frank Hespeler & Haim Shalit, 2018. "Mean-Extended Gini Portfolios: A 3D Efficient Frontier," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 731-740, March.

    More about this item

    Keywords

    Research Methods/ Statistical Methods;

    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:ags:huaewp:232632. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/caehuil.html .

    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.