Robust ranking and portfolio optimization
Robust ranking and portfolio optimization
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.
uncertainty modelling, network flows, portfolio optimization, ranking, mixed integer programming
407-416
Nguyen, Tri-Dung
a6aa7081-6bf7-488a-b72f-510328958a8e
Lo, Andrew
c095cab7-259a-430b-98b6-2a6fc0e2afaa
1 September 2012
Nguyen, Tri-Dung
a6aa7081-6bf7-488a-b72f-510328958a8e
Lo, Andrew
c095cab7-259a-430b-98b6-2a6fc0e2afaa
Nguyen, Tri-Dung and Lo, Andrew
(2012)
Robust ranking and portfolio optimization.
European Journal of Operational Research, 221 (2), .
(doi:10.1016/j.ejor.2012.03.023).
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.
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More information
e-pub ahead of print date: 28 March 2012
Published date: 1 September 2012
Keywords:
uncertainty modelling, network flows, portfolio optimization, ranking, mixed integer programming
Organisations:
Centre of Excellence for International Banking, Finance & Accounting, Operational Research
Identifiers
Local EPrints ID: 334988
URI: http://eprints.soton.ac.uk/id/eprint/334988
ISSN: 0377-2217
PURE UUID: f63eaf6c-561e-43d6-90da-6ad56ea0df03
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Date deposited: 08 Mar 2012 15:18
Last modified: 15 Mar 2024 03:37
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Contributors
Author:
Tri-Dung Nguyen
Author:
Andrew Lo
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