A New Approach for Robust Mean-Variance Portfolio Selection Using Trimmed k-Means Clustering
Penulis/Author
LA GUBU (1); Prof. Dr.rer.nat. Dedi Rosadi, S.Si.,, M.Sc. (2); Prof. Dr. Abdurakhman, S.Si., M.Si. (3)
Tanggal/Date
1 2021
Kata Kunci/Keyword
Abstrak/Abstract
In this study, we consider the data preprocessing using trimmed k-means clustering for robust mean-variance portfolio
selection. The proposed method trims the outliers in the data preprocessing stage. The optimum portfolio is formed by
selecting the stock representation for each cluster using the Sharpe ratio. The optimum portfolio formation is accom-
plished by robust fast minimum covariance determinant (FMCD) and robust S mean-variance (MV) portfolio model.
In the empirical experiment, we use fundamental trading data for the year 2017 (to form the clusters) and daily closing
price data of LQ45 index stocks from August 2017 to July 2018 taken from the Indonesian Stock Exchange to form
the optimum portfolio. As benchmark for portfolio performance formed in this study, we use the performance of the
Indonesia Composite Index (ICI). The results reveal that the proposed method can reliably obtain the optimum portfo-
lio and solve the outliers problem. Moreover, the comparison stage shows that the combination of trimmed k-means
clustering and robust portfolio model is better in forming the optimum portfolio than the combination of k-means clus-
tering and robust MV portfolio model. Finally, we also find that the combination of trimmed k-means clustering (with
α=10%) and robust FMCD MV portfolio model outperforms portfolios produced by other methods.
Rumpun Ilmu
Statistik
Bahasa Asli/Original Language
English
Level
Internasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
IEMS lagubu.pdf
[PAK] Full Dokumen
2
L1 lagubu iems 2021.pdf
Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
3
Similarity Jurnal La Gubu A New Approach for Robust Mean Variance.pdf
