Analysis of cyclical behavior and quantification of the level of development from time series of stock market returns
Само за регистроване кориснике
2016
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
We have analyzed cyclical behavior of the three types of stock market indexes (SMI) time series: data belonging to stock markets of developed economies, emerging economies, and of the underdeveloped or transitional economies. We have used two techniques of data analysis to obtain and verify our findings: the wavelet transformation (WT) spectral analysis to study SMI returns data, and the Hurst exponent formalism to study local behavior around market cycles and trends. We calculated WT power spectra for all our SMI series, and have searched for characteristic peaks (local maxima) that point to existence of cycles in our data. We found cyclical behavior in all SMI data sets that we have analyzed. Moreover, the positions and the boundaries of cyclical intervals that we found seem to be common for all markets in our dataset. We differentiated nine such periods in our SMI data. Our results show that measures like the relative WT energy content and the relative WT amplitude for the peaks in ...the small scales region could be used to partially differentiate levels of growth and/or maturity of market economies in our dataset. Finally, we propose a way to quantify and compare the level of development of a stock market, based on the Hurst scaling exponent approach. From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index (HDI), which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups. Further verification of this method remains open for future research.
Извор:
Presentations and Abstracts Data Science Challenges, July 8–11, 2016, Matera, Italy, 2016Финансирање / пројекти:
- Фазни прелази и карактеризација неорганских и органских система (RS-MESTD-Basic Research (BR or ON)-171015)
- Напредне аналитичке, нумеричке и методе анализе примењене механике флуида и комплексних система (RS-MESTD-Basic Research (BR or ON)-174014)
Колекције
Институција/група
Fakultet veterinarske medicineTY - CONF AU - Stratimirović, Đorđe AU - Blesić, Suzana AU - Sarvan, Darko AU - Miljković, Vladimir PY - 2016 UR - https://vet-erinar.vet.bg.ac.rs/handle/123456789/2751 AB - We have analyzed cyclical behavior of the three types of stock market indexes (SMI) time series: data belonging to stock markets of developed economies, emerging economies, and of the underdeveloped or transitional economies. We have used two techniques of data analysis to obtain and verify our findings: the wavelet transformation (WT) spectral analysis to study SMI returns data, and the Hurst exponent formalism to study local behavior around market cycles and trends. We calculated WT power spectra for all our SMI series, and have searched for characteristic peaks (local maxima) that point to existence of cycles in our data. We found cyclical behavior in all SMI data sets that we have analyzed. Moreover, the positions and the boundaries of cyclical intervals that we found seem to be common for all markets in our dataset. We differentiated nine such periods in our SMI data. Our results show that measures like the relative WT energy content and the relative WT amplitude for the peaks in the small scales region could be used to partially differentiate levels of growth and/or maturity of market economies in our dataset. Finally, we propose a way to quantify and compare the level of development of a stock market, based on the Hurst scaling exponent approach. From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index (HDI), which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups. Further verification of this method remains open for future research. C3 - Presentations and Abstracts Data Science Challenges, July 8–11, 2016, Matera, Italy T1 - Analysis of cyclical behavior and quantification of the level of development from time series of stock market returns UR - https://hdl.handle.net/21.15107/rcub_veterinar_2751 ER -
@conference{ author = "Stratimirović, Đorđe and Blesić, Suzana and Sarvan, Darko and Miljković, Vladimir", year = "2016", abstract = "We have analyzed cyclical behavior of the three types of stock market indexes (SMI) time series: data belonging to stock markets of developed economies, emerging economies, and of the underdeveloped or transitional economies. We have used two techniques of data analysis to obtain and verify our findings: the wavelet transformation (WT) spectral analysis to study SMI returns data, and the Hurst exponent formalism to study local behavior around market cycles and trends. We calculated WT power spectra for all our SMI series, and have searched for characteristic peaks (local maxima) that point to existence of cycles in our data. We found cyclical behavior in all SMI data sets that we have analyzed. Moreover, the positions and the boundaries of cyclical intervals that we found seem to be common for all markets in our dataset. We differentiated nine such periods in our SMI data. Our results show that measures like the relative WT energy content and the relative WT amplitude for the peaks in the small scales region could be used to partially differentiate levels of growth and/or maturity of market economies in our dataset. Finally, we propose a way to quantify and compare the level of development of a stock market, based on the Hurst scaling exponent approach. From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index (HDI), which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups. Further verification of this method remains open for future research.", journal = "Presentations and Abstracts Data Science Challenges, July 8–11, 2016, Matera, Italy", title = "Analysis of cyclical behavior and quantification of the level of development from time series of stock market returns", url = "https://hdl.handle.net/21.15107/rcub_veterinar_2751" }
Stratimirović, Đ., Blesić, S., Sarvan, D.,& Miljković, V.. (2016). Analysis of cyclical behavior and quantification of the level of development from time series of stock market returns. in Presentations and Abstracts Data Science Challenges, July 8–11, 2016, Matera, Italy. https://hdl.handle.net/21.15107/rcub_veterinar_2751
Stratimirović Đ, Blesić S, Sarvan D, Miljković V. Analysis of cyclical behavior and quantification of the level of development from time series of stock market returns. in Presentations and Abstracts Data Science Challenges, July 8–11, 2016, Matera, Italy. 2016;. https://hdl.handle.net/21.15107/rcub_veterinar_2751 .
Stratimirović, Đorđe, Blesić, Suzana, Sarvan, Darko, Miljković, Vladimir, "Analysis of cyclical behavior and quantification of the level of development from time series of stock market returns" in Presentations and Abstracts Data Science Challenges, July 8–11, 2016, Matera, Italy (2016), https://hdl.handle.net/21.15107/rcub_veterinar_2751 .