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

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.

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Robust mean-variance portfolio selection with time series clustering

10.1063/5.0042172 ◽

2021 ◽

Author(s):

La Gubu ◽

Dedi Rosadi ◽

Abdurakhman

Keyword(s):

Time Series ◽

Portfolio Selection ◽

Time Series Clustering ◽

Mean Variance Portfolio ◽

Mean Variance

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ROBUST PORTFOLIO SELECTION WITH CLUSTERING BASED ON BUSINESS SECTOR OF STOCKS

MEDIA STATISTIKA ◽

10.14710/medstat.14.1.33-43 ◽

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.

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Common Dissimilarity Measures are Inappropriate for Time Series Clustering

Revista de Informática Teórica e Aplicada ◽

10.22456/2175-2745.25070 ◽

2013 ◽

Vol 20 (1) ◽

pp. 25 ◽

Cited By ~ 1

Author(s):

Cássio Martini Martins Pereira ◽

Rodrigo F. de Mello

Keyword(s):

Time Series ◽

Time Series Clustering ◽

Dissimilarity Measures

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Mean-Variance Portfolio Selection with Tracking Error Penalization

Mathematics ◽

10.3390/math8111915 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1915

Author(s):

William Lefebvre ◽

Grégoire Loeper ◽

Huyên Pham

Keyword(s):

Portfolio Selection ◽

Tracking Error ◽

Efficient Frontier ◽

Minimum Variance ◽

Sharpe Ratio ◽

Portfolio Strategy ◽

The Mean ◽

Mean Variance Portfolio ◽

Mean Variance ◽

Variance Criterion

This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a reference portfolio with same wealth and fixed weights. Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in the case of misspecified parameters, by “fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean–Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.

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Optimisation of wind farm location using mean-variance portfolio theory and time series clustering

2016 IEEE International Conference on Power and Energy (PECon) ◽

10.1109/pecon.2016.7951638 ◽

2016 ◽

Cited By ~ 5

Author(s):

Christiaan J. Joubert ◽

Hendrik J. Vermeulen

Keyword(s):

Time Series ◽

Wind Farm ◽

Portfolio Theory ◽

Time Series Clustering ◽

Wind Farm Location ◽

Mean Variance Portfolio ◽

Mean Variance

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Time Series Clustering on Lower Tail Dependence for Portfolio Selection

Mathematical and Statistical Methods for Actuarial Sciences and Finance ◽

10.1007/978-3-319-02499-8_12 ◽

2014 ◽

pp. 131-140 ◽

Cited By ~ 1

Author(s):

Giovanni De Luca ◽

Paola Zuccolotto

Keyword(s):

Time Series ◽

Portfolio Selection ◽

Tail Dependence ◽

Lower Tail ◽

Time Series Clustering

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Examining of the countries in terms of water resources along with the effect of climate change by time series clustering

10.21203/rs.3.rs-176059/v1 ◽

2021 ◽

Author(s):

özlem akay

Keyword(s):

Climate Change ◽

Time Series ◽

Water Resources ◽

Water Cycle ◽

Clustering Methods ◽

Time Series Clustering ◽

Risk Levels ◽

Partitioning Around Medoids ◽

Living Things ◽

Life Itself

Abstract Today, the problem of climate change is addressed in many ways. The most important effect of climate change is the disruption of the water cycle. Therefore, the location and timing of the water resources in the world are changing. Water is an indispensable resource that connects all living things together and directly affects their lives. Water is not only a biological requirement for man, but also for economic, social, and cultural life itself. However, this resource, which is of vital importance, exists unfortunately in a limited amount on earth. This study aims to identify similar countries in terms of water resources by taking into account the changes in precipitation amounts together with climate change. For this purpose, a time series clustering analysis was conducted to precipitation amounts of 21 countries between 2009-2017 with the Hierarchical clustering and Partitioning Around Medoids (PAM) clustering methods. Countries are divided into two clusters and they were evaluated according to their water risk levels and populations. It should be noted that countries with low precipitation, high risk of water, and a high population should take urgent measures for climate change.

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A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection

Risks ◽

10.3390/risks6040115 ◽

2018 ◽

Vol 6 (4) ◽

pp. 115 ◽

Cited By ~ 1

Author(s):

Xin Liu ◽

Jiang Wu ◽

Chen Yang ◽

Wenjun Jiang

Keyword(s):

Time Series ◽

Portfolio Selection ◽

Financial Time Series ◽

Tail Dependence ◽

Simulation Studies ◽

Lower Tail ◽

Financial Time ◽

The Matrix ◽

Mean Variance Portfolio ◽

Mean Variance

In this paper, we propose a clustering procedure of financial time series according to the coefficient of weak lower-tail maximal dependence (WLTMD). Due to the potential asymmetry of the matrix of WLTMD coefficients, the clustering procedure is based on a generalized weighted cuts method instead of the dissimilarity-based methods. The performance of the new clustering procedure is evaluated by simulation studies. Finally, we illustrate that the optimal mean-variance portfolio constructed based on the resulting clusters manages to reduce the risk of simultaneous large losses effectively.

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