DocumentCode :
3089549
Title :
Robust mean absolute deviation portfolio model under Affine Data Perturbation uncertainty set
Author :
Zhifeng Dai ; Fenghua Wen
Author_Institution :
Coll. of Math. & Comput. Sci., Changsha Univ. of Sci. & Technol., Changsha, China
fYear :
2013
fDate :
17-19 July 2013
Firstpage :
472
Lastpage :
475
Abstract :
In this paper, based on the robust optimization techniques in Bertsimas and Sim[8], we propose a computationally tractable robust mean absolute deviation portfolio model. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. The remarkable characteristic of the new method is that the robust optimization model retains the complexity of original portfolio optimization problem, i.e., the robust counterpart problem is still a linear programming problem. Empirical analysis with real market data to illustrate the behavior of the robust optimization model is efficient.
Keywords :
investment; linear programming; set theory; statistical analysis; affine data perturbation uncertainty set; linear programming; parameter uncertainty; portfolio strategy performance; robust mean absolute deviation portfolio model; robust optimization technique; Computational modeling; Data models; Linear programming; Optimization; Portfolios; Robustness; Uncertainty; Linear Programming(LP); Robust Optimization; mean absolute deviation portfolio model; small cap stocks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Service Systems and Service Management (ICSSSM), 2013 10th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4673-4434-0
Type :
conf
DOI :
10.1109/ICSSSM.2013.6602489
Filename :
6602489
Link To Document :
بازگشت