Efficient Stock Portfolio Construction by Means of Clustering


  • Jerzy Korzeniewski University of Łódź, Faculty of Economics and Sociology, Department of Statistical Methods




investment portfolio construction, clustering, number of clusters, Sharpe index


When investors start to use statistical methods to optimise their stock market investment decisions, one of fundamental problems is constructing a well‑diversified portfolio consisting of a moderate number of positions. Among a multitude of methods applied to the task, there is a group based on dividing all companies into a couple of homogeneous groups followed by picking out a representative from each group to create the final portfolio. The division stage does not have to coincide with the sector affiliation of companies. When the division is performed by means of clustering of companies, a vital part of the process is to establish a good number of clusters. The aim of this article is to present a novel technique of portfolio construction based on establishing a numer of portfolio positions as well as choosing cluster representatives. The grouping methods used in the clustering process are the classical k‑means and the PAM (Partitioning Around Medoids) algorithm. The technique is tested on data concerning the 85 biggest companies from the Warsaw Stock Exchange for the years 2011–2016. The results are satisfactory with respect to the overall possibility of creating a clustering‑based algorithm requiring almost no intervention on the part of the investor.


Download data is not yet available.


Bensmail H., DeGennaro R. (2004), Analyzing Imputed Financial Data: A New Approach to Cluster Analysis, FRB of Atlanta Working Paper no. 2004–20, Atlanta, https://www.econstor.eu/bitstream/10419/100973/1/wp2004–20.pdf [accesed: 1.08.2015].
Google Scholar

Craighead S., Klemesrud B. (2002), Stock Selection Based on Cluster and Outlier Analysis, Fifteenth International Symposium on Mathematical Theory of Networks and Systems, University of Notre Dame, Notre Dame, Indiana, https://www.researchgate.net/publication/272175812_Stock_Selection_Based_on_Cluster_and_Outlier_Analysis [accesed: 1.08.2015].
Google Scholar

Gatnar E., Walesiak M. (2004), Metody statystycznej analizy wielowymiarowej w badaniach marketingowych, Wydawnictwo Akademii Ekonomicznej we Wrocławiu, Wrocław.
Google Scholar

Korzeniewski J. (2014), Indeks wyboru liczby skupień w zbiorze danych, “Przegląd Statystyczny”, vol. 61, no. 2, pp. 169–180.
Google Scholar

Marvin K. (2015), Creating Diversified Portfolios Using Cluster Analysis, unpublished research, pp. 1–15, https://www.cs.princeton.edu/sites/default/files/uploads/karina_marvin.pdf [accesed: 1.08.2015].
Google Scholar

Pasha S., Leong P. (2013), Cluster Analysis of High‑Dimensional High‑Frequency Financial Time Series, IEEE Conference on Computational Intelligence for Financial Engineering & Economics, Piscataway, http://ieeexplore.ieee.org/document/6611700/ [accesed: 1.08.2015].
Google Scholar

Ren Z. (2005), Portfolio Construction Using Clustering Methods, Thesis at the Worcester Polytechnic Institute, Worcester, https://web.wpi.edu/Pubs/ETD/Available/etd–042605–092010/unrestricted/ZhiweiRen.pdf [accesed: 1.08.2015].
Google Scholar

Rosén F. (2006), Correlation Based Clustering of the Stockholm Stock Exchange, Master’s Thesis, School of Business, Stockholm University, Stockholm, http://www.diva‑portal.org/smash/get/diva2:196577/FULLTEXT01.pdf [accesed: 1.08.2015].
Google Scholar




How to Cite

Korzeniewski, J. (2018). Efficient Stock Portfolio Construction by Means of Clustering. Acta Universitatis Lodziensis. Folia Oeconomica, 1(333), [85]–92. https://doi.org/10.18778/0208-6018.333.06