| Issue |
SHS Web Conf.
Volume 65, 2019
The 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019)
|
|
|---|---|---|
| Article Number | 06009 | |
| Number of page(s) | 6 | |
| Section | Monitoring, Modeling, Forecasting and Preemption of Crisis in Socio-Economic Systems | |
| DOI | https://doi.org/10.1051/shsconf/20196506009 | |
| Published online | 29 May 2019 | |
Diagnostics of a persistence property for quotations dynamics on high-tech stock markets
Zaporizhzhia National University, Department of Economic Cybernetics, 66, Zhukovskogo str., Zaporizhzhia, 69600, Ukraine
* Corresponding author: maxishko@ukr.net
The research purpose is diagnosis of the persistence property for the stock quotes time series of leading companies belonging to the high-tech sector: Apple Inc., Microsoft Corporation and Samsung Electronics Co. The persistence property or the trend-stability of the time series is crucial meaning for the investor. As a result of the application of the R\S-analysis, it is proved that the stock quotations dynamics of these companies have the persistence property. Also, the method of sequential R\S analysis is applied: the leading characteristics of the long-term memory are discovered, which makes it possible to carry out a comparative analysis of their predictability. It is found that the time series of profitability do not have the properties of persistence. However, the tests for diagnostic of a deterministic chaos reveal the appearance of the persistence property in the time series of “delayed” profitability. The obtained results allows to state the fractal nature for the time series of quotations, while the characteristics of the persistence (depth of memory) determined by the research can be useful to the investor in terms of the investment instrument choice and the investment horizon as well as can be used in selecting the parameters for a forecasting model.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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