Time Series Clustering for Robust Mean-Variance Portfolio Selection: Comparison of Several Dissimilarity Measures
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
7 2021
Kata Kunci/Keyword
Abstrak/Abstract
This paper shows how to create a robust portfolio selection with time series clustering
by using some dissimilarity measure. Based on such dissimilarity measures, stocks are initially
sorted into multiple clusters using the Partitioning Around Medoids (PAM) time series clustering
approach. Following clustering, a portfolio is constructed by selecting one stock from each
cluster. Stocks having the greatest Sharpe ratio are selected from each cluster. The optimum
portfolio is then constructed using the robust Fast Minimum Covariance Determinant (FMCD)
and robust S MV portfolio model. When there are a big number of stocks accessible for the
portfolio formation process, we can use this approach to quickly generate the optimum portfolio.
This approach is also resistant to the presence of any outliers in the data. The Sharpe ratio was
used to evaluate the performance of the portfolios that were created. 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 utilized as a case study. Empirical study revealed that portfolios
constructed using PAM time series clustering with autocorrelation dissimilarity and a robust
FMCD MV portfolio model outperformed portfolios created using other approaches.
