The earliest documented analytical approach to portfolio selection is Markowitz’s mean–variance analysis, which attempts to find the portfolio with optimal performance by considering the tradeoff between return and risk. The performance of mean–variance analysis has been the subject of many studies and compared to other portfolio construction approaches such as a naïve equally-weighted allocation scheme. In recent years, several approaches have been proposed to improve the mean–variance model by reducing the sensitivity of the portfolio selection process in order achieve robust performance. Although robust portfolio optimization has been one of the most researched methods for improving portfolio robustness, the performance of robust portfolios has not been the major focus of studies. In this paper, a comprehensive analysis on robust portfolio performance is presented for equity portfolios constructed in the U.S. market during the period 1980 and 2014, and results confirm the advantage of robust portfolio optimization for controlling uncertainty while efficiently allocating investments.
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The industry returns are available at http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
Portfolios with smaller annualized volatility are not considered since the GMV portfolio often shows annualized standard deviation above 10% for estimation periods of 1 year or longer.
The calculation involved in estimating the estimation error covariance matrix is derived in Stubbs and Vance (2005).
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This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2016R1C1B1014492).
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Kim, J.H., Kim, W.C., Kwon, D. et al. Robust equity portfolio performance. Ann Oper Res 266, 293–312 (2018). https://doi.org/10.1007/s10479-017-2739-1
- Portfolio optimization
- Robust optimization
- Portfolio performance
- U.S. equity market