Camelia Catalina Joldes
Bucharest University of Economic Studies, 6 Piata Romana, 1st district, Bucharest, 010374 Romania
2nd International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2018 – Graz, Austria, November 8, 2018, CONFERENCE PROCEEDINGS published by the Association of Economists and Managers of the Balkans, Belgrade, Serbia; ISBN 978-86-80194-13-4
Abstract
The purpose of this paper is to show how principal component analysis (PCA) can be used to construct the most efficient portfolios. Due to the large number of existing variables, it is difficult to perform statistical analysis of data, but PCA will help us in choosing the best performing shares. Through the PCA we will show the utility of this statistical method in the selection phase of the data. For this study we decided to select a number of representative companies for four central European countries: Czech Republic, Poland, Germany and Austria. The main objective of this analysis is to identify the most important factors indicating the most profitable shares, so we have considered a number of five financial indicators, representative for the selected firms.
Key words
Stock selection, principal component analysis, diversification, data reduction, variance
References
[1] Fulga, C., Dedu, S., Şerban, F. (2013) Portfolio Optimization with Prior Stock Selection, Economic Computation and Economic Cybernetics Studies and Research. [2] Pasini, G.(2017) Principal Component Analysis For Stock Portfolio Management, International Journal of Pure and Applied Mathematics, Volume 115 No. 1 2017, pp.153-167. [3] Ngai, E.W.T., Cheng, T.C.E. (1997) Identifying potential barriers to total quality management using principal component analysis and correspondence analysis, International Journal of Quality & Reliability Management, Vol. 14 Issue: 4, pp.391-408. [4] Meriç, İ., Ding,J., Meriç, G. (2016) Global Portfolio Diversification with Emerging Stock Markets, Volume 6 No 1. [5] Lia, Y., Zhangb,Q.(2011) The Application of Principal Component Analysis on Financial Analysis in Real Estate Listed Company, Procedia Engineering, Volume 15, pp. 4499-4503. [6] Yap, B.(2013) The Application of Principal Component Analysis in the Selection of Industry Specific Financial Ratios, British Journal of Economics, Management & Trade. 3. pp.242-252. [7] Yang,L., Rea, W., Rea, A.(2015) Identifying Highly Correlated Stocks Using the Last Few Principal Components. arXiv:1512.03537v1 [8] Armeanu, D., Istudor, N., Florinel, S. M., Burca,A.-M. (2014) Analysis of the Romanian Insurance Market Based on Ensuring and Exercising Consumers’ Right to Claim, Amfiteatru Economic, 16(36), pp.550–562. [9] Timm, N. H.(2002) Applied Multivariate Analysis, Springer-Verlag, New York.
