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Warianty tytułu
Języki publikacji
Abstrakty
From the macroeconomic point of view, the stock index is the best indicator of the behavior of the stock market. Stock indices fulfill different functions. One of their most important functions is to observe developments of the stock market situation. Therefore, it is crucial to describe the long-term development of indices and also to find moments of abrupt changes. Another interesting aspect is to find those indices that have evolved in a similar way over time. In this article, using trend analysis, we will uncover the global evolution of selected indices. After evaluating the global trend in the series we compare the results with local trend analysis. Other goal is to detect the moments in which this development suddenly changed using the change-point analysis. By means of cluster analysis, we find those indices that are most similar in long-term development. In each analysis, we select the most appropriate methods and compare their results.
Słowa kluczowe
Rocznik
Tom
Strony
56--63
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
autor
- Department of Mathematics and Descriptive Geometry, Faculty of Civil Engineering, Slovak University of Technology, Bratislava, 810 05, Slovakia
Bibliografia
- [1] E. Lehtinen, U. Pulkkinen, and K. Pörn, “Statistical Trend Analysis Methods for Temporal Phenomena”, SKi, 1997.
- [2] M. G. Kendall, Rank correlation methods, Griffin: London, 1975.
- [3] H. B. Mann, “Nonparametric Tests Against Trend”,Econometrica, vol. 13, no. 3, 1945, 245–259DOI: 10.2307/1907187.
- [4] R. Sneyers, On the statistical analysis of series of observations, Secretariat of the World Meteorological Organization: Geneva, 1990, Technical Note no. 143.
- [5] A. Wald and J. Wolfowitz, “On a Test Whether Two Samples are from the Same Population”, The Annals of Mathematical Statistics, vol. 11, no. 2, 1940, 147–162.
- [6] A. N. Pettitt, “A Non-Parametric Approach to the Change-Point Problem”, Journal of the RoyalStatistical Society. Series C (Applied Statistics), vol. 28, no. 2, 1979, 126–135 DOI: 10.2307/2346729.
- [7] D. S. Matteson and N. A. James, “A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data”, Journal of the American Statistical Association, vol. 109, no. 505, 2014, 334– 345 DOI: 10.1080/01621459.2013.849605.
- [8] Gilmore, Dynamic Time and Price Analysis of Market Trends, Bryce Gilmore & associates, 1999.
- [9] R. D. Edwards, J. Magee, and W. H. C. Bassetti, Technical Analysis of Stock Trends, CRC Press, 2001.
- [10] E. Brodsky, Change-Point Analysis in Nonstationary Stochastic Models, CRC Press, 2017.
- [11] B. S. Everitt, S. Landau, M. Leese, and D. Stahl, Cluster Analysis, John Wiley & Sons, Ltd: Chichester, UK, 2011 DOI: 10.1002/9780470977811.
- [12] A. Kassambara, Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning, STHDA, 2017.
- [13] J. Chen and A. K. Gupta, Parametric Statistical Change Point Analysis with Applications to Genetics, Medicine, and Finance, Birkhäuser Basel, 2012 DOI: 10.1007/978-0-8176-4801-5.
- [14] J. S. Racine, “Nonparametric Econometrics: A Primer”, Foundations and T DOI: 10.1561/0800000009.
- [15] T. Mills, Modelling Trends and Cycles in Economic Time Series, Palgrave Macmillan, 2003 DOI: 10.1057/9780230595521.
- [16] W. Palma, Time Series Analysis, John Wiley & Sons, 2016.
- [17] “Cophenetic correlation coefficient – MATLAB cophenet”. https://www.mathworks.com/help/stats/cophenet.html. Accessed on: 2019-11-07.
- [18] M. Charrad, N. Ghazzali, V. Boiteau, and A. Niknafs, “NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set”, Journal of Statistical Software, vol. 61, no. 1, 2014, 1– 36 DOI: 10.18637/jss.v061.i06. 59
- [19] T. Pohlert, “trend: Non-Parametric Trend Tests and Change-Point Detection”. c2018, https://CRAN.Rproject.org/package=trend. Accessed on: 2019-11-07.
- [20] P. Grosjean, F. Ibanez, and M. Etienne, “pastecs: Package for Analysis of Space-Time Ecological Series”. c2018, https://CRAN.R-project.org/package=pastecs, Accessed on: 2019-11-07.
- [21] Devreker and A. Lefebvre, “TTAinterface – TrendAnalysis: Temporal Trend Analysis Graphical Interface”. c2018, https://CRAN.R-project.org/package=TTAinterfaceTrendAnalysis. Accessed on: 2019-11-07.
- [22] N. A. James and D. S. Matteson, “ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data”, Journal of Statistical Software, vol. 62, no. 1, 2015, 1–25, 10.18637/jss.v062.i07.
- [23] “R: The R Stats Package”. https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html. Accessed on: 2019-11-07.
- [24] T. Galili et al, “dendextend: Extending ’dendrogram’ Functionality in R”. c2019, https://CRAN. Rproject. org/package=dendextend. Accessed on: 2019-11-07.
- [25] “R: Documentation”. https://www.r-project.org/other-docs.html. Accessed on: 2019-11-07.
- [26] T. Wei, V. Simko, M. Levy, Y. Xie, Y. Jin, and J. Zemla, “corrplot: Visualization of a Correlation Matrix”. c2017, https://CRAN.R-project.org/package=corrplot. Accessed on: 2019-11-07.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6b7ca19e-b42f-4dd5-91af-5f533cc91322