Fundamental Portfolio Construction Based on Semi-Variance
Anna Rutkowska-Ziarko
Department of Quantitative Methods, University of Warmia and Mazury in OlsztynRésumé
In models for creating a fundamental portfolio, based on the classical Markowitz model, the variance is usually used as a risk measure. However, equal treatment of negative and positive deviations from the expected rate of return is a slight shortcoming of variance as the risk measure. Markowitz defined semi-variance to measure the negative deviations only. However, finding the fundamental portfolio with minimum semi-variance is not possible with the existing methods.The aim of the article is to propose and verify a method which allows to find a fundamental portfolio with the minimum semi-variance. A synthetic indicator is constructed for each company, describing its economic and financial situation. The method of constructing fundamental portfolios using semi-variance as the risk measure is presented. The differences between the semi-variance fundamental portfolios and variance fundamental portfolios are analysed on example of companies listed on the Warsaw Stock Exchange.
Mots-clés :
Markowitz model, fundamental portfolio, semi-variance, Mahalanobis distanceRéférences
Balicki A. 2009. Statystyczna analiza wielowymiarowa i jej zastosowania społeczno- ekonomiczne. Wydawnictwo Uniwersytetu Gdańskiego, Gdańsk.
Basu S. 1977. Investment performance of common stocks In relation to their price-earnings ratios: a test of the efficient market hypothesis. Journal of Finance, 3: 663- 682
Galagedera U.A., Brooks R.D. 2007. Is co-skewness a better measure of risk in the downside than downside beta? Evidence in emerging market data. Journal of Multinational Financial Management, 17: 214-230.
Harlow W.V., Rao R.K.S. 1989. Asset pricing in a generalized mean-lower partial moment framework: theory and evidence. Journal of Financial and Quantitative Analysis, 24: 285-311.
Hellwig Z. 1968. Zastosowanie metody taksonomicznej do typologicznego podziału krajów ze względu na poziom ich rozwoju oraz zasoby i strukturę wykwalifikowanych kadr. Przegląd Statystyczny, 4: 323-326.
Elton E.J., Gruber M.J. 1998. Nowoczesna teoria portfelowa i analiza papierów wartościowych. WIG PRESS.
Łuniewska M. 2005. Evaluation of Selected Methods of Classification for the Warsaw Stock Exchange. International Advances in Economic Research, 11: 469-481.
Mandelbrot B., Fisher A., Calvet L. 1997. A Multifractal Model of Asset Returns. Working Papers-Yale School of Management's Economics Research Network, 1997: 1-33.
Mahalanobis P.C. 1936. On the Generalized Distance in Statistics. Proceedings of the National Institute of Science. India, 12: 49-55.
Markowitz H. 1952. Portfolio selection. Journal of Finance, 7: 77-91.
Markowitz H. 1959. Portfolio selection: efficient diversification of investments. John Wiley and Sons, New York.
Markowitz H. 1991. Portfolio selection: efficient diversification of investments. Blackwell, Malden, Massachusetts.
Post T., van Vliet P. 2006. Downside risk and asset pricing. Journal of Banking and Finance, 30: 823-849.
Rutkowska-Ziarko A, 2005. Metody znajdowania portfela efektywnego dla semiwariancji. Badania operacyjne i decyzyjne, 3-4: 63- 83.
Rutkowska-Ziarko A. 2007. Wykorzystanie wariancji i semiwariancji do budowy portfela akcji przy normalności rozkładów stóp zwrotu. Przegląd Statystyczny, 4: 105-116.
Rutkowska-Ziarko A. 2011. Alternatywna metoda budowy fundamentalnego portfela papierów wartościowych. Taksonomia 18 - Klasyfikacja i analiza danych - teoria i zastosowania: p. 551-559.
Rutkowska-Ziarko A. 2013. Fundamental portfolio construction based on Mahalanobis distance. Studies in Classification. Data Analysis and Knowledge Organization: in press.
Tarczyński W. 2002. Fundamentalny portfel papierów wartościowych. PWE, Warszawa.
Tarczyński W. 1995. O pewnym sposobie wyznaczania składu portfela papierów wartościowych. Przegląd Statystyczny, 1: 91-106.
Walesiak M., Dudek A. 2010. Finding Groups in Ordinal Data: An Examination of Some Clustering Procedures. Studies in Classification. Data Analysis. and Knowledge Organization. Part 2: p. 185-192.
Department of Quantitative Methods, University of Warmia and Mazury in Olsztyn
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