A robust mean absolute deviation model for portfolio optimization

Yongma Moon, Tao Yao

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

In this paper we develop a robust model for portfolio optimization. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. We construct a simple robust mean absolute deviation (RMAD) model which leads to a linear program and reduces computational complexity of existing robust portfolio optimization methods. This paper tests the robust strategies on real market data and discusses performance of the robust optimization model empirically based on financial elasticity, standard deviation, and market condition such as growth, steady state, and decline in trend. Our study shows that the proposed robust optimization generally outperforms a nominal mean absolute deviation model. We also suggest precautions against use of robust optimization under certain circumstances.

Original languageEnglish (US)
Pages (from-to)1251-1258
Number of pages8
JournalComputers and Operations Research
Volume38
Issue number9
DOIs
StatePublished - Sep 1 2011

Fingerprint

Portfolio Optimization
Robust Optimization
Deviation
Parameter Uncertainty
Estimation Error
Linear Program
Optimization Model
Model
Standard deviation
Categorical or nominal
Optimization Methods
Elasticity
Computational Complexity
Error analysis
Computational complexity
Portfolio optimization
Robust optimization
Strategy
Market

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research

Cite this

Moon, Yongma ; Yao, Tao. / A robust mean absolute deviation model for portfolio optimization. In: Computers and Operations Research. 2011 ; Vol. 38, No. 9. pp. 1251-1258.
@article{e9907050d69a415fa6d6619e63900dd6,
title = "A robust mean absolute deviation model for portfolio optimization",
abstract = "In this paper we develop a robust model for portfolio optimization. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. We construct a simple robust mean absolute deviation (RMAD) model which leads to a linear program and reduces computational complexity of existing robust portfolio optimization methods. This paper tests the robust strategies on real market data and discusses performance of the robust optimization model empirically based on financial elasticity, standard deviation, and market condition such as growth, steady state, and decline in trend. Our study shows that the proposed robust optimization generally outperforms a nominal mean absolute deviation model. We also suggest precautions against use of robust optimization under certain circumstances.",
author = "Yongma Moon and Tao Yao",
year = "2011",
month = "9",
day = "1",
doi = "10.1016/j.cor.2010.10.020",
language = "English (US)",
volume = "38",
pages = "1251--1258",
journal = "Surveys in Operations Research and Management Science",
issn = "0305-0548",
publisher = "Elsevier Limited",
number = "9",

}

A robust mean absolute deviation model for portfolio optimization. / Moon, Yongma; Yao, Tao.

In: Computers and Operations Research, Vol. 38, No. 9, 01.09.2011, p. 1251-1258.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A robust mean absolute deviation model for portfolio optimization

AU - Moon, Yongma

AU - Yao, Tao

PY - 2011/9/1

Y1 - 2011/9/1

N2 - In this paper we develop a robust model for portfolio optimization. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. We construct a simple robust mean absolute deviation (RMAD) model which leads to a linear program and reduces computational complexity of existing robust portfolio optimization methods. This paper tests the robust strategies on real market data and discusses performance of the robust optimization model empirically based on financial elasticity, standard deviation, and market condition such as growth, steady state, and decline in trend. Our study shows that the proposed robust optimization generally outperforms a nominal mean absolute deviation model. We also suggest precautions against use of robust optimization under certain circumstances.

AB - In this paper we develop a robust model for portfolio optimization. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. We construct a simple robust mean absolute deviation (RMAD) model which leads to a linear program and reduces computational complexity of existing robust portfolio optimization methods. This paper tests the robust strategies on real market data and discusses performance of the robust optimization model empirically based on financial elasticity, standard deviation, and market condition such as growth, steady state, and decline in trend. Our study shows that the proposed robust optimization generally outperforms a nominal mean absolute deviation model. We also suggest precautions against use of robust optimization under certain circumstances.

UR - http://www.scopus.com/inward/record.url?scp=79251618950&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79251618950&partnerID=8YFLogxK

U2 - 10.1016/j.cor.2010.10.020

DO - 10.1016/j.cor.2010.10.020

M3 - Article

AN - SCOPUS:79251618950

VL - 38

SP - 1251

EP - 1258

JO - Surveys in Operations Research and Management Science

JF - Surveys in Operations Research and Management Science

SN - 0305-0548

IS - 9

ER -