Imprecise Return Rates on the Warsaw Stock Exchange

• Autorzy: Krzysztof Piasecki
• Języki publikacji: EN

Abstrakty

EN

The return rate in imprecision risk may be described as a fuzzy probabilistic set [Piasecki, 2011a]. On the other side, in [Piasecki, Tomasik 2013] is shown that the Normal Inverse Gaussiandistribution is the best matching probability distribution of logarithmic returns on Warsaw Stock Exchange. There will be presented the basic properties if imprecise return with the Normal Inverse Gaussian distribution of future value logarithm. The existence of distribution of expected return rate is discussed. All obtained results may be immediately applied for effectiveness analysis at risk of uncertainty and imprecision [Piasecki, 2011c]. (original abstract)

Słowa kluczowe

PL

Stopa zwrotu; Ryzyko; Zbiory rozmyte; Giełda papierów wartościowych

EN

Rate of return; Risk; Fuzzy sets; Stock market

• Czasopismo: Metody Ilościowe w Badaniach Ekonomicznych / Szkoła Główna Gospodarstwa Wiejskiego
• Rocznik: 2014
• Tom: 15(XV)
• Numer: nr 1
• Strony: 153-158

Twórcy

autor

Krzysztof Piasecki

• Poznań University of Economics, Poland

Bibliografia

• Barndorff-Nielsen O.E. (1977) Exponentially decreasing distributions for the logarithm of particle size, Proceedings of the Royal Society of London, Series A, Mathematical and Physical Sciences 353.
• Bølviken E., Benth F.E. (2000) Quantification of risk in Norwegian stocks via the normal inverse Gaussian distribution, Proceedings of the AFIR 2000 Colloquium, Tromsø.
• Buckley J.J. (1987) Fuzzy mathematics of finance, Fuzzy Sets and Systems, 21, 257-273.
• Buckley J.J. (1992) Solving fuzzy equations in economics and finance, Fuzzy Sets and Systems, 48, 289-296.
• Czogała E., Gottwald S., Pedrycz W. (1982) Contribution to application of energy measure of fuzzy sets, Fuzzy Sets and System 8, 205-214.
• Dubois J., Prade H.(1979) Fuzzy real algebra: some results, Fuzzy Sets and Systems 2, 327-348.
• Gottwald S., Czogała E., Pedrycz W.(1982), Measures of fuzziness and operations with fuzzy sets, Stochastica 6, 187-205.
• Greenhut J.G., Norman G., Temponi C.(1995) Towards a fuzzy theory of oligopolistic competetion, IEEE Proceedings of ISUMA-NAFIPS 1995, 286-291.
• Gutierrez I., (1989), Fuzzy numbers and Net Present Value, Scand. J. Mgmt. 5(2), 149-159.
• Hiroto K.(1981) Concepts of probabilistic sets, Fuzzy Sets and Systems 5, 31-46.
• Kahneman D., Tversky A.(1979) Prospect theory: an analysis of decision under risk, Econometrica 47, 263-292.
• Kuchta D. (2000)Fuzzy capital budgeting, Fuzzy Sets and Systems, 111, 367-385.
• Lesage C. (2001) Discounted cash-flows analysis. An interactive fuzzy arithmetic approach, European Journal of Economic and Social Systems, 15(2), 49-68.
• Piasecki K. (2011a) Behavioural Present Value, Behavioral & Experimental Finance eJournal 2011/4. Available at SSRN: http://dx.doi.org/10.2139/ssrn.1729351.
• Piasecki K. (2011b) Rozmyte zbiory probabilistyczne jako narzędzie finansów behawiorralnych, Poznań.
• Piasecki K. (2011c) Effectiveness of securities with fuzzy probabilistic return, Operations Research and Decisions 2/2011, 65-78.
• Piasecki K. (2014) On imprecise investment recommendations, Studies in Logic Grammar and Rhetoric, 37(50), 25-37.
• Piasecki K., Tomasik E. (2013) Rozkłady stóp zwrotu z instrumentów polskiego rynku kapitałowego, edu-libri, Kraków.
• Sheen J.N. (2005) Fuzzy financial profitability analyses of demand side management alternatives from participant perspective, Information Sciences, 169, 329-364.
• Ward T.L. (1985) Discounted fuzzy cash flow analysis, 1985 Fall Industrial Engineering Conference Proceedings, 476-481.
• Weron A., Weron R. (1999) Inżynieria finansowa, WNT, Warszawa.