A robust mean absolute deviation model for portfolio optimization

Yongma Moon, Tao Yao

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


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
Issue number9
StatePublished - Sep 1 2011

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research


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