Yazar "Duran, A." seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    A new possibilistic mean – Variance model based on the principal components analysis: An application on the Turkish holding stocks
    (Old City Publishing, 2019) Goktas, F.; Duran, A.
    Possibility Theory is a great tool to deal with the imprecise probability. However, the possibilistic counterpart of the mean – variance (MV) model has serious shortcomings. Thus, we propose a new possibilistic MV model, which depends on the Principal Components Analysis. The proposed model enables to incorporate subjective judgments into the portfolio selection. In addition, it captures the asymmetry in the return data unlike the MV model. The proposed model is also tractable as the MV model since it can be expressed as a concave quadratic maximization problem. After laying down the theoretical points, we illustrate it 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 proposed model and the MV model. © 2019 Old City Publishing, Inc.
  • Küçük Resim Yok
    Öğe
    New robust portfolio selection models based on the principal components analysis: An application on the Turkish holding stocks
    (Old City Publishing, 2020) Goktas, F.; Duran, A.
    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.