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Robust ranking and portfolio optimization

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  • Nguyen, Tri-Dung
  • Lo, Andrew W.

Abstract

The portfolio optimization problem has attracted researchers from many disciplines to resolve the issue of poor out-of-sample performance due to estimation errors in the expected returns. A practical method for portfolio construction is to use assets’ ordering information, expressed in the form of preferences over the stocks, instead of the exact expected returns. Due to the fact that the ranking itself is often described with uncertainty, we introduce a generic robust ranking model and apply it to portfolio optimization. In this problem, there are n objects whose ranking is in a discrete uncertainty set. We want to find a weight vector that maximizes some generic objective function for the worst realization of the ranking. This robust ranking problem is a mixed integer minimax problem and is very difficult to solve in general. To solve this robust ranking problem, we apply the constraint generation method, where constraints are efficiently generated by solving a network flow problem. For empirical tests, we use post-earnings-announcement drifts to obtain ranking uncertainty sets for the stocks in the DJIA index. We demonstrate that our robust portfolios produce smaller risk compared to their non-robust counterparts.

Suggested Citation

  • Nguyen, Tri-Dung & Lo, Andrew W., 2012. "Robust ranking and portfolio optimization," European Journal of Operational Research, Elsevier, vol. 221(2), pages 407-416.
  • Handle: RePEc:eee:ejores:v:221:y:2012:i:2:p:407-416 DOI: 10.1016/j.ejor.2012.03.023
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    References listed on IDEAS

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    1. Lin, Chang-Chun & Liu, Yi-Ting, 2008. "Genetic algorithms for portfolio selection problems with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 185(1), pages 393-404, February.
    2. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    3. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    4. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    5. Gondzio, Jacek & Grothey, Andreas, 2007. "Solving non-linear portfolio optimization problems with the primal-dual interior point method," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1019-1029, September.
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    1. repec:ebl:ecbull:eb-17-00061 is not listed on IDEAS
    2. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    3. Black, Geoffrey & Holley, Donald & Solan, David & Bergloff, Michael, 2014. "Fiscal and economic impacts of state incentives for wind energy development in the Western United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 136-144.
    4. Lioui, Abraham & Poncet, Patrice, 2013. "Optimal benchmarking for active portfolio managers," European Journal of Operational Research, Elsevier, vol. 226(2), pages 268-276.
    5. Chang, Zhiqi & Song, Shiji & Zhang, Yuli & Ding, Jian-Ya & Zhang, Rui & Chiong, Raymond, 2017. "Distributionally robust single machine scheduling with risk aversion," European Journal of Operational Research, Elsevier, vol. 256(1), pages 261-274.

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