Robust ranking and portfolio optimization

نویسندگان

  • Tri-Dung Nguyen
  • Andrew W. Lo
چکیده

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 popular method is to use assets' ordering information, expressed in the form of preferences over the stocks, instead of the exact expected returns for portfolio construction. Motivating from 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 R is in a discrete uncertainty set. We want to find a weight vector w that maximizes some generic objective function f(w, R) 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. We apply the column generation method, where constraints are efficiently generated by solving a network flow problem, to solve this robust ranking problem. For empirical tests, we use post announcement earning drifts to obtain ranking uncertainty set for stocks in the DJIA index. We show our robust portfolios produce smaller risk and higher Sharpe ratios compared to their nonrobust counterparts. (This is a joint work with Andrew Lo at MIT.) Biography Dr. Nguyen is a visiting research assistant professor at the Department of Civil and Environmental Engineering at UIUC. He holds a B.Eng. in Software Systems Engineering from RMIT in Australia in 2002, an MS in High Performance Computation for Engineered Systems from Singapore-MIT Alliance in 2004 and a Ph.D. in Operations Research from MIT in June 2009. Dr. Nguyen is interested in applying Operations Research to a wide range of applications that include complex system modeling, robust statistical analysis, transportation, and quantitative finance (portfolio optimization, hedge fund strategies). He is currently working on a biofuel development project to study the complex engineered systems and their interplay with the natural environment and social-economic factors. Dr. Nguyen is a finalist in the INFORMS Nicholson Student Paper Competition in 2008. He has worked for some financial firms (Credit Suisse, Fidelity) and IT and logistics firms (FujitsuSiemens, PSA, ATCRC). Location: 101 Transportation Building Date: Thursday, December 3, 2009 Time: 4-5 p.m.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Robustness-based portfolio optimization under epistemic uncertainty

In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two approaches: (1) moment bounding approach and (2) likelihood-based approach. This paper first proposes a ...

متن کامل

A Robust Knapsack Based Constrained Portfolio Optimization

Many portfolio optimization problems deal with allocation of assets which carry a relatively high market price. Therefore, it is necessary to determine the integer value of assets when we deal with portfolio optimization. In addition, one of the main concerns with most portfolio optimization is associated with the type of constraints considered in different models. In many cases, the resulted p...

متن کامل

Robust Portfolio Optimization with risk measure CVAR under MGH distribution in DEA models

Financial returns exhibit stylized facts such as leptokurtosis, skewness and heavy-tailness. Regarding this behavior, in this paper, we apply multivariate generalized hyperbolic (mGH) distribution for portfolio modeling and performance evaluation, using conditional value at risk (CVaR) as a risk measure and allocating best weights for portfolio selection. Moreover, a robust portfolio optimizati...

متن کامل

Robust portfolio selection with polyhedral ambiguous inputs

 Ambiguity in the inputs of the models is typical especially in portfolio selection problem where the true distribution of random variables is usually unknown. Here we use robust optimization approach to address the ambiguity in conditional-value-at-risk minimization model. We obtain explicit models of the robust conditional-value-at-risk minimization for polyhedral and correlated polyhedral am...

متن کامل

Primal and dual robust counterparts of uncertain linear programs: an application to portfolio selection

This paper proposes a family of robust counterpart for uncertain linear programs (LP) which is obtained for a general definition of the uncertainty region. The relationship between uncertainty sets using norm bod-ies and their corresponding robust counterparts defined by dual norms is presented. Those properties lead us to characterize primal and dual robust counterparts. The researchers show t...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • European Journal of Operational Research

دوره 221  شماره 

صفحات  -

تاریخ انتشار 2012