New robust portfolio selection models based on the principal components analysis: An application on the Turkish holding stocks
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Old City Publishing
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Robust optimization is a significant tool to deal with the uncertainty of parameters. However, the robust versions of the mean - variance (MV) model have serious shortcomings. Thus, we propose new robust versions of the MV model and its possibilistic counterpart, based on the Principal Component Analysis. We also derive their analytical solutions when the risk-free asset and short positioning are allowed. In addition, we suggest an eigenvalue approach to manage their conservativeness. After laying down the theoretical points, we illustrate them by using a real data set of six holding stocks trading on the Borsa Istanbul (BIST). We also compare the profitability and performance results of the existing models and the proposed robust models. © 2020 Old City Publishing, Inc. Published by license under the OCP Science imprint, a member of the Old City Publishing Group.
Açıklama
Anahtar Kelimeler
Fuzzy logic, Imprecise probability, Portfolio selection, Possibility theory, Principal components analysis, Robust optimization, Triangular fuzzy numbers, Worst-case analysis
Kaynak
Journal of Multiple-Valued Logic and Soft Computing
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
34
Sayı
2
