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The aim of the paper is to find a method of using prediction rules in time series in such a way to maximize the profit considering the risk. To deal with this task, a regression approach to prediction was chosen. Hence, the paper refers to relation between autoregression of a chosen time series and investment strategies. The time series under consideration is the most important polish financial instrument, a future contract on WIG20. Usually, it is rather easy to prove statistically that the autoregression of a single time series cannot be considered as an effective method for forecasting WIG20 quotations for investment purpose. However, the authors find the relation between the autoregression (and also multi-regression) and real future values of WIG20 which can be the source of effective strategies. The paper presents both - the theoretical description of the proposed strategies and results of their application for monthly data of WIG20, unemployment rate and money supply in Poland (data from years 1995-2007).
Wydawca
Czasopismo
Rocznik
Tom
Strony
46--52
Opis fizyczny
Bibliogr. 16 poz., wykr.
Twórcy
autor
autor
- West Pomeranian University of Technology of Szczecin, Faculty of Computer Science, Żołnierska 52, 71-210 Szczecin, Poland, phone: +48 91 4495564, irejer@wi.zut.edu.pl
Bibliografia
- [1] Fama E., Efficient capital markets, Journal of Financial Economics, 11, 1575-1617, 1991.
- [2] Fama E., Market efficiency, long-term returns, and behavioral finance, Journal of Finacial Economics, 49, 283-306, 1998.
- [3] Kompa K., Matuszewska-Janica A., Examination of Warsaw Stock Exchange indexes gehaviour: Applications of rolling windows variance ratio test, Polish Journal of Environmental Studies, 17, 3b, 150-154, 2008.
- [4] Rejer I., Fuzzy expert model of index WIG20, Polish Journal of Environmental Studies, 17, 3b, 365-370, 2008.
- [5] Żebrowska-Suchodolska D., Witkowska D., Momentum and winner-loser strategies: Evidence for the Warsaw Stock Exchange, Polish Journal of Environmental Studies, 18, 5b, 220-227, 2009.
- [6] Dymowa L., Bartosiewicz P., Tkacz K., The stock trading decision support system based on the reoptimization (reapeted optimalization), Computer Science - Theory and Application, 14, 7-19, 2008.
- [7] Brock W., Lakonishok J., LeBaron B., Simple technical trading rules and stochastic properties of stock returns, Journal of Finance, 47, 1731-1764, 1992.
- [8] Hudson R., Dempsey M., Keasey K., A note on the weak form efficiency of capital markets: The application of simple technical trading rules to UK stock prices - 1935-1994, Journal of Banking Finance, 20, 1121-1132, 1996.
- [9] LeBaron B., Technical trading rules and regime shifts in foreign exchange intervention, Journal of International Economics, 49, 125-143, 1999.
- [10] Covel M.W., The complete turtle tracker, Harper Collins Publisher, New York, 2007.
- [11] Cai B.M., Cai C.X., Keasey K., Market efficiency and returns to simple technical trading rules: further evidence form US, UK, Asian and Chinese Stock Markets, Asia-Pacific Financial Markets, Springer, 45-60, 2005.
- [12] Gencay R., Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules, Journal of International Economics, 47, 91-107, 1999.
- [13] Gencay R., Stengos T., Moving average rules, volume and predictability of security returns with feedforward networks, Journal of Forecasting, 17, 401-414, 1998.
- [14] Krutsinger J., Transaction systems. Secrets of masters, Wig-Press, Warszawa, 1999.
- [15] Ball P., The critic mass. How the one influence the other, Insignis, Kraków, 2007.
- [16] Fong W.M., Yong L.H., Chasing trends: recursive moving average trading rules and internet stocks, 12, 43-76, 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR0-0065-0059