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Optimal portfolio selection with maximal risk adjusted return

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  • Yue Wang
  • Zhijian Qiu
  • Xiaomei Qu

Abstract

We investigate the portfolio diversification problem by maximizing the risk adjusted return (RAR) of the underlying portfolio. The model in this article has two primary advantages over the original portfolio selection model with maximal RAR: (1) it considers the set of available assets containing any number of assets instead of only two assets, which is more reasonable in practical applications and (2) it incorporates the general linear constraint other than the simple budget constraint, which can deal with additional constraints for rational investors. An application including in-sample and out-of-sample tests is provided where the results illustrate that the portfolios selected by our method lead to considerable increases of RAR in comparison with those by the minimization of variance approach, and the outperformance persists using different sample frequencies.

Suggested Citation

  • Yue Wang & Zhijian Qiu & Xiaomei Qu, 2017. "Optimal portfolio selection with maximal risk adjusted return," Applied Economics Letters, Taylor & Francis Journals, vol. 24(14), pages 1035-1040, August.
    • Handle: RePEc:taf:apeclt:v:24:y:2017:i:14:p:1035-1040
    DOI: 10.1080/13504851.2016.1248351
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    1. Joseph Simonian & Josh Davis, 2011. "Incorporating uncertainty into the Black-Litterman portfolio selection model," Applied Economics Letters, Taylor & Francis Journals, vol. 18(17), pages 1719-1722.
    2. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    3. Hatemi-J, Abdulnasser & El-Khatib, Youssef, 2015. "Portfolio selection: An alternative approach," Economics Letters, Elsevier, vol. 135(C), pages 141-143.
    4. Nielsen, Lars Tyge, 1987. "Portfolio Selection in the Mean-Variance Model: A Note," Journal of Finance, American Finance Association, vol. 42(5), pages 1371-1376, December.
    5. Campbell Harvey & John Liechty & Merrill Liechty & Peter Muller, 2010. "Portfolio selection with higher moments," Quantitative Finance, Taylor & Francis Journals, vol. 10(5), pages 469-485.
    6. Alexander, Gordon J. & Baptista, Alexandre M., 2006. "Portfolio selection with a drawdown constraint," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 3171-3189, November.
    7. Sharpe, William F., 1971. "A Linear Programming Approximation for the General Portfolio Analysis Problem," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 6(5), pages 1263-1275, December.
    8. Massimo Guidolin & Allan Timmermann, 2008. "International asset allocation under regime switching, skew, and kurtosis preferences," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 889-935, April.
    9. Kane, Alex, 1982. "Skewness Preference and Portfolio Choice," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 17(1), pages 15-25, March.
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