A0441
Title: Portfolio optimization using hybrid robust time series clustering and robust mean-variance portfolio selection
Authors: Dedi Rosadi - Universitas Gadjah Mada, Indonesia (Indonesia) [presenting]
Peter Filzmoser - Vienna University of Technology (Austria)
La Gubu - Universitas Haluuleo (Indonesia)
Lestari Vemmie Nastiti - Universitas Gadjah Mada (Indonesia)
Abstract: A novel portfolio optimization approach is presented by applying several hybrid approaches between robust clustering and robust portfolio selection approaches. When there are many stocks that can be selected during the portfolio optimization process, this approach can be used to quickly obtain the optimum portfolio, where, at the same time, the method is also robust to the presence of outliers in the data. The daily closing price of stocks listed on the Indonesia Stock Exchange, which are included in the LQ-45 indexed from August 2017 to July 2018, was used as a case study. The empirical study showed that portfolios constructed using PAM time series clustering with autocorrelation dissimilarity and a robust FMCD-MV portfolio model outperformed portfolios created using other considered approaches.
