Finanse, Rynki Finansowe, Ubezpieczenia

Previously: Zeszyty Naukowe Uniwersytetu Szczecińskiego. Finanse, Rynki Finansowe, Ubezpieczenia


ISSN: 2450-7741     eISSN: 2300-4460    OAI    DOI: 10.18276/frfu.2018.91-30
CC BY-SA   Open Access 

Issue archive / 1/2018 (91)
Związek pomiędzy ryzykiem a efektywnością na polskim rynku funduszy inwestycyjnych akcji według miar z grupy EMC
(The relationship between risk and efficiency on the polish investment fund market according to measures from the EMC group)

Authors: Monika Mościbrodzka
Uniwersytet Wrocławski
Keywords: investment funds classic measures of risk and effectiveness hierarchical agglomeration procedures
Data publikacji całości:2018
Page range:15 (365-379)
Klasyfikacja JEL: C38 D53 E44
Cited-by (Crossref) ?:

Abstract

Purpose – The aim of the article is to examine the relationship between classic measures of effectiveness and the risk of funds, determined by alternative uncertainty indicators such as beta and standard devia-tion of rates of return. Design/Methodology/approach – The study was based on the tools of multidimensional comparative analysis, comparing the results of risk classification (beta and standard deviation) and efficiency (measures from the EMC group) and examining the compatibility of both divisions. Findings – It was shown that groups of funds which are similar in terms of risk and efficiency are homo-geneous groups and funds with a similar risk level in different research periods reached similar levels of profitability Originality/value – Approach to the problem, binding hierarchical agglomeration procedures with the as-sessment of effectiveness and the study of the risk-effectiveness relationship is rarely used in this type of research
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Bibliography

1.Fama, E.F. (1972). Components of Investment Performance. Journal of Finance, 3, 551–567.
2.Friend, I., Blume, M. (1970). Measurement of Portfolio Performance under Uncertainty. American Economic Review, 4, 561–575.
3.Green, P.E., Rao, V.R. (1969). A Note of Proximity Measures and Cluster Analysis. Journal of Marketing Research, 6, 359–364.
4.Homa, M., Mościbrodzka, M. (2015) Application of multifactorial market-timing models to assess risk and effectiveness of equity-linked insurance funds in Poland. Statistics in Transition, 16, 279–292.
5.Homa, M., Mościbrodzka, M. (2016a). Application of diagram methods and hierarchical agglomerative procedures to assess the risk of investment funds on the Warsaw Stock Exchange. Nauki o Finansach, 4 (29), 21–34.
6.Homa, M., Mościbrodzka, M. (2016b). Dynamiczne wersje hybrydowych modeli market timing oraz weryfikacja ich przydatności w ocenie ryzyka i efektywności funduszy inwestycyjnych. Zeszyty Naukowe Uniwersytetu Szczecińskiego. Finanse, Rynki Finansowe, Ubezpieczenia, 1 (79), 73–85.
7.Israelsen, C.L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5, 423–427.
8.Jensen, M.C. (1968). The Performance of Mutual Funds in Period 1945–1964. Journal of Finance, 5, 389–416.
9.Karpio, A., Żebrowska-Suchodolska, D. (2015). Badanie stabilności wyników funduszy inwestycyjnych przy użyciu miar efektywności opartych na współczynniku
10.Sharpe’a. Zeszyty Naukowe Uniwersytetu Szczecińskiego, 862. Finanse, Rynki Finansowe, Ubezpieczenia, 75, 221–231.
11.Kaufman, L., Rousseeuw, P.J. (1990) Finding Groups in Data: an Introduction to Cluster Analysis. New York: Wiley.
12.Klemkosky, R.C. (1973). The Bias in Composite Performance Measures. Journal of Financial and Quantitative Analysis, 3, 505–514.
13.Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. Review of Economics and Statistics, 1 (47), 13–37.
14.Markowitz, H.M. (1952). Portfolio Selection. Journal of Finance, 1 (7), 77–91.
15.Milligan, G.W., Cooper, M.C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrica, 2, 159–179.
16.Mossin, J. (1969). Security Pricing and Investment Criteria in Competitive Markets. American Economic Review, 5 (59), 749–756.
17.Panek, T., Zwierzchowski, J. (2013). Statystyczne metody wielowymiarowej analizy porównawczej. Teoria i zastosowania. Warszawa: Oficyna Wydawnicza SGH.
18.Rand, W.M. (1971). Objective Criteria for the Evaluation of Clustering Methods. Journal of the American Statistical Association, 66, 846–850.
19.Rousseeuw, P.J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
20.Sharpe, W.F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19, 425–442.
21.Sharpe, W.F. (1966). Mutual Fund Performance. Journal of Business, 39, 119–138.
22.Treynor, J.L. (1965). How to Rate Management of Investment Funds. Harvard Business Review, 1.
23.Treynor, J.L., Black, F. (1973). How to Use Security Analysis to Improve Portfolio Selection. Journal of Business, 46, 66–85.
24.Walesiak, M., Gatnar, E. (red.) (2012). Statystyczna analiza danych z wykorzystaniem programu R. Warszawa: Wydawnictwo Naukowe PWN.
25.Ward, J.H. (1963). Hierarchical Grouping of Optimize an Objective Function. Journal of the American Statistical Association, 58, 236–244.
26.Wishart, D. (1969). An Algorithm for Hierarchical Classification. Biometrics, 1 (22), 165–170.