scholarly journals ROBUST PORTFOLIO SELECTION WITH CLUSTERING BASED ON BUSINESS SECTOR OF STOCKS

2021 ◽  
Vol 14 (1) ◽  
pp. 33-43
Author(s):  
La Gubu ◽  
Dedi Rosadi ◽  
Abdurakhman Abdurakhman
Keyword(s):  
Cluster Analysis ◽  
Portfolio Selection ◽  
Stock Exchange ◽  
Sharpe Ratio ◽  
The Other ◽  
Portfolio Performance ◽  
Business Sector ◽  
Robust Portfolio ◽  
Mean Vectors ◽  
Robust Portfolio Selection

In recent years there have been numerous studies on portfolio selection using cluster analysis in conjunction with Markowitz model which used mean vectors and covariance matrix that are estimated from a highly volatile data. This study presents a more robust way of portfolio selection where stocks are grouped into clusters based on business sector of stocks. A representative from each cluster is selected from each cluster using Sharpe ratio to construct a portfolio and then optimized using robust FCMD and S-estimation. Calculation Sharpe ratio showed that this method works efficiently on large number of data while also robust against outlier in comparison to k-mean clustering. Implementation of this method on stocks listed on the Indonesia Stock Exchange, which included in the LQ-45 indexed for the period of August 2017 to July 2018 showed that portfolio performance obtained using clustering base on business sector of stocks combine with robust FMCD estimation is outperformed the other possible combination of the methods.

scholarly journals Time Series Clustering for Robust Mean-Variance Portfolio Selection: Comparison of Several Dissimilarity Measures

2021 ◽  
Vol 2123 (1) ◽  
pp. 012021
Author(s):  
La Gubu ◽  
Dedi Rosadi ◽  
Abdurakhman
Keyword(s):  
Time Series ◽  
Portfolio Selection ◽  
Stock Exchange ◽  
Sharpe Ratio ◽  
Minimum Covariance Determinant ◽  
Time Series Clustering ◽  
Dissimilarity Measures ◽  
Partitioning Around Medoids ◽  
Robust Portfolio ◽  
Mean Variance Portfolio

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.

scholarly journals A novel two-phase robust portfolio selection and optimization approach under uncertainty: A case study of Tehran stock exchange

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0239810
Author(s):  
Pejman Peykani ◽  
Emran Mohammadi ◽  
Armin Jabbarzadeh ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mir Saman Pishvaee
Keyword(s):  
Portfolio Selection ◽  
Stock Exchange ◽  
Optimization Approach ◽  
Two Phase ◽  
Robust Portfolio ◽  
Robust Portfolio Selection

Good deals and benchmarks in robust portfolio selection

2016 ◽  
Vol 250 (2) ◽  
pp. 666-678 ◽  
Author(s):  
Alejandro Balbás ◽  
Beatriz Balbás ◽  
Raquel Balbás
Keyword(s):  
Portfolio Selection ◽  
Robust Portfolio ◽  
Robust Portfolio Selection
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