TY  - JOUR
AU  - Stratimirović, Đorđe
AU  - Sarvan, Darko
AU  - Miljković, Vladimir
AU  - Blesić, Suzana
PY  - 2018
UR  - https://vet-erinar.vet.bg.ac.rs/handle/123456789/1596
AB  - In this paper we have analyzed scaling properties and 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 transform (WT) spectral analysis to identify cycles in the SMI returns data, and the time- dependent detrended moving average (tdDMA) analysis to investigate local behavior around market cycles and trends. 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 seam to be common for all markets in our dataset. We list and illustrate the presence of nine such periods in our SMI data. We report on the possibilities to differ-entiate between the level of growth of the analyzed markets by way of statistical analysis of the properties of wavelet spectra that characterize particular peak behaviors. Our results show that measures like the relative WT energy content and the relative WT amplitude of the peaks in the small scales region could be used to partially differentiate between market economies. Finally, we propose a way to quantify the level of development of a stock market based on estimation of local complexity of markets SMI series. From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index, 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.
PB  - Elsevier Science Bv, Amsterdam
T2  - Communications in Nonlinear Science and Numerical Simulation
T1  - Analysis of cyclical behavior in time series of stock market returns
VL  - 54
SP  - 21
EP  - 33
DO  - 10.1016/j.cnsns.2017.05.009
ER  - 

@article{
author = "Stratimirović, Đorđe and Sarvan, Darko and Miljković, Vladimir and Blesić, Suzana",
year = "2018",
abstract = "In this paper we have analyzed scaling properties and 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 transform (WT) spectral analysis to identify cycles in the SMI returns data, and the time- dependent detrended moving average (tdDMA) analysis to investigate local behavior around market cycles and trends. 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 seam to be common for all markets in our dataset. We list and illustrate the presence of nine such periods in our SMI data. We report on the possibilities to differ-entiate between the level of growth of the analyzed markets by way of statistical analysis of the properties of wavelet spectra that characterize particular peak behaviors. Our results show that measures like the relative WT energy content and the relative WT amplitude of the peaks in the small scales region could be used to partially differentiate between market economies. Finally, we propose a way to quantify the level of development of a stock market based on estimation of local complexity of markets SMI series. From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index, 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.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Communications in Nonlinear Science and Numerical Simulation",
title = "Analysis of cyclical behavior in time series of stock market returns",
volume = "54",
pages = "21-33",
doi = "10.1016/j.cnsns.2017.05.009"
}

Stratimirović, Đ., Sarvan, D., Miljković, V.,& Blesić, S.. (2018). Analysis of cyclical behavior in time series of stock market returns. in Communications in Nonlinear Science and Numerical Simulation
Elsevier Science Bv, Amsterdam., 54, 21-33.
https://doi.org/10.1016/j.cnsns.2017.05.009

Stratimirović Đ, Sarvan D, Miljković V, Blesić S. Analysis of cyclical behavior in time series of stock market returns. in Communications in Nonlinear Science and Numerical Simulation. 2018;54:21-33.
doi:10.1016/j.cnsns.2017.05.009 .

Stratimirović, Đorđe, Sarvan, Darko, Miljković, Vladimir, Blesić, Suzana, "Analysis of cyclical behavior in time series of stock market returns" in Communications in Nonlinear Science and Numerical Simulation, 54 (2018):21-33,
https://doi.org/10.1016/j.cnsns.2017.05.009 . .

TY  - 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 .

TY  - CONF
AU  - Sarvan, Darko
AU  - Ajtić, Jelena
AU  - Miljković, Vladimir
PY  - 2015
UR  - https://vet-erinar.vet.bg.ac.rs/handle/123456789/2367
AB  - To describe complex behavior of solar radiation in terms of multifractional Brownian motion,
two methods are used: central Detrended Moving Average (cDMA) and its version, the-so-called
time dependent Detrended Moving Average (tdDMA).
The methods are applied to solar radiation time series (SRTS) consisting of 696 daily solar
irradiation measurements for Belgrade (44.810 °N, 20.460 °E), Serbia (data obtained
from www.soda-is.com on 15 November 2014). The measurements are taken with a 15-minute
temporal resolution, and they cover approximately two full years of measurements, 2004 and
2005.
The time dependent Detrended Moving Average method gives a distribution of the local Hurst
exponents for the whole data series. Long range correlations are characterized by the Hurst
exponent H. In particular, the exponents 0.0 < H < 0.5 and 0.5 < H < 1.0 correspond to negative
(anti-persistence) and positive (persistence) correlation, respectively. The Hurst exponent equal
to 0.5 corresponds to an uncorrelated Brownian process. In accordance with an estimated
magnitude of Hurst exponents, the results with persistent characteristics are obtained. This
finding implies some underlying trends.
Further investigation focuses on the effects of potential periodic-like influences on the
analyzed SRTS data. For example, daily and three-month periodicities that correspond to a
diurnal and seasonal variability of solar radiation, respectively, are found. We propose that an
existence of a number of periodic-like influences on SRTS data may partially explain the observed
difference in types of correlated behavior of corresponding scaling functions.
PB  - Niš : RAD Association
C3  - Book of Abstracts of the Third International Conference on Radiation and Applications in Various Fields of Research, June 08–12, 2015, Budva, Montenegro
T1  - Fractality of observed solar radiation data
SP  - 116
EP  - 116
UR  - https://hdl.handle.net/21.15107/rcub_veterinar_2367
ER  - 

@conference{
author = "Sarvan, Darko and Ajtić, Jelena and Miljković, Vladimir",
year = "2015",
abstract = "To describe complex behavior of solar radiation in terms of multifractional Brownian motion,
two methods are used: central Detrended Moving Average (cDMA) and its version, the-so-called
time dependent Detrended Moving Average (tdDMA).
The methods are applied to solar radiation time series (SRTS) consisting of 696 daily solar
irradiation measurements for Belgrade (44.810 °N, 20.460 °E), Serbia (data obtained
from www.soda-is.com on 15 November 2014). The measurements are taken with a 15-minute
temporal resolution, and they cover approximately two full years of measurements, 2004 and
2005.
The time dependent Detrended Moving Average method gives a distribution of the local Hurst
exponents for the whole data series. Long range correlations are characterized by the Hurst
exponent H. In particular, the exponents 0.0 < H < 0.5 and 0.5 < H < 1.0 correspond to negative
(anti-persistence) and positive (persistence) correlation, respectively. The Hurst exponent equal
to 0.5 corresponds to an uncorrelated Brownian process. In accordance with an estimated
magnitude of Hurst exponents, the results with persistent characteristics are obtained. This
finding implies some underlying trends.
Further investigation focuses on the effects of potential periodic-like influences on the
analyzed SRTS data. For example, daily and three-month periodicities that correspond to a
diurnal and seasonal variability of solar radiation, respectively, are found. We propose that an
existence of a number of periodic-like influences on SRTS data may partially explain the observed
difference in types of correlated behavior of corresponding scaling functions.",
publisher = "Niš : RAD Association",
journal = "Book of Abstracts of the Third International Conference on Radiation and Applications in Various Fields of Research, June 08–12, 2015, Budva, Montenegro",
title = "Fractality of observed solar radiation data",
pages = "116-116",
url = "https://hdl.handle.net/21.15107/rcub_veterinar_2367"
}

Sarvan, D., Ajtić, J.,& Miljković, V.. (2015). Fractality of observed solar radiation data. in Book of Abstracts of the Third International Conference on Radiation and Applications in Various Fields of Research, June 08–12, 2015, Budva, Montenegro
Niš : RAD Association., 116-116.
https://hdl.handle.net/21.15107/rcub_veterinar_2367

Sarvan D, Ajtić J, Miljković V. Fractality of observed solar radiation data. in Book of Abstracts of the Third International Conference on Radiation and Applications in Various Fields of Research, June 08–12, 2015, Budva, Montenegro. 2015;:116-116.
https://hdl.handle.net/21.15107/rcub_veterinar_2367 .

Sarvan, Darko, Ajtić, Jelena, Miljković, Vladimir, "Fractality of observed solar radiation data" in Book of Abstracts of the Third International Conference on Radiation and Applications in Various Fields of Research, June 08–12, 2015, Budva, Montenegro (2015):116-116,
https://hdl.handle.net/21.15107/rcub_veterinar_2367 .

TY  - CONF
AU  - Stratimirović, Đorđe
AU  - Blesić, Suzana
AU  - Miljković, Vladimir
AU  - Sarvan, Darko
PY  - 2014
UR  - https://vet-erinar.vet.bg.ac.rs/handle/123456789/2704
AB  - In this paper we have analyzed scaling properties of time
series of stock market indices (SMIs) of developing economies
of Western Balkans, and have compared the results we have
obtained with the results from more developed economies. We
have used three different techniques of data analysis to obtain
and verify our findings: Detrended Fluctuation Analysis
(DFA) method, Detrended Moving Average (DMA) method,
and Wavelet Transformation (WT) analysis.
Following extensive research in the area of econophysics of
national and international stock markets, we were interested
to contribute to this body of knowledge by analyzing the
dynamics of market behavior of transitional economies in the
Western Balkans, and to compare data from these emerging
economies with data from more economically developed
countries. Analyzes of stock market behavior of the emerging
economies of South America, or the developing Asian or
African markets have shown that the values of scaling exponents,
calculated from the time series of stock market indices,
could be used to estimate the efficiency of markets in question.
With that in mind, by applying the theoretical approach of
statistical physics, we aim to offer a new perspective on stock
market dynamics in the Western Balkans and contribute
to better understanding of the development process in the
region's economies.
We have found scaling behavior in all SMI data sets that
we have analyzed. Scaling of SMI series changes from
long-range correlated to slightly anti-correlated behavior,
i.e. the appropriate scaling exponents decrease in value with
the increase in growth and/or maturity of the economy the
stock market is embedded in. Scaling exponents α, H, and
β, corresponding to the DFA, DMA, and WT technique, all
cross the 0.5 (and zero) line, marking this alteration.
We also report the presence of effects of potential periodic-like
influences on the SMI data that we have analyzed. One
such influence is visible in all our SMI series, and appears
at a period Tp ≈ 90 days. We propose that the existence
of various periodic-like influences on SMI data may partially
explain the observed difference in types of correlated
behavior of corresponding scaling functions. The application
of time-dependent scaling analysis (tdDMA) proved
that these influences are of a complex type, that is, they
can not be easily distinguished from a local correlations profile.
C3  - International Conference on Statistical Physics (SigmaPhi2014), Rhodes, 7-11 July 2014
T1  - Scaling analysis of time series of stock market indices of transitional economies in the Western Balkans
UR  - https://hdl.handle.net/21.15107/rcub_veterinar_2704
ER  - 

@conference{
author = "Stratimirović, Đorđe and Blesić, Suzana and Miljković, Vladimir and Sarvan, Darko",
year = "2014",
abstract = "In this paper we have analyzed scaling properties of time
series of stock market indices (SMIs) of developing economies
of Western Balkans, and have compared the results we have
obtained with the results from more developed economies. We
have used three different techniques of data analysis to obtain
and verify our findings: Detrended Fluctuation Analysis
(DFA) method, Detrended Moving Average (DMA) method,
and Wavelet Transformation (WT) analysis.
Following extensive research in the area of econophysics of
national and international stock markets, we were interested
to contribute to this body of knowledge by analyzing the
dynamics of market behavior of transitional economies in the
Western Balkans, and to compare data from these emerging
economies with data from more economically developed
countries. Analyzes of stock market behavior of the emerging
economies of South America, or the developing Asian or
African markets have shown that the values of scaling exponents,
calculated from the time series of stock market indices,
could be used to estimate the efficiency of markets in question.
With that in mind, by applying the theoretical approach of
statistical physics, we aim to offer a new perspective on stock
market dynamics in the Western Balkans and contribute
to better understanding of the development process in the
region's economies.
We have found scaling behavior in all SMI data sets that
we have analyzed. Scaling of SMI series changes from
long-range correlated to slightly anti-correlated behavior,
i.e. the appropriate scaling exponents decrease in value with
the increase in growth and/or maturity of the economy the
stock market is embedded in. Scaling exponents α, H, and
β, corresponding to the DFA, DMA, and WT technique, all
cross the 0.5 (and zero) line, marking this alteration.
We also report the presence of effects of potential periodic-like
influences on the SMI data that we have analyzed. One
such influence is visible in all our SMI series, and appears
at a period Tp ≈ 90 days. We propose that the existence
of various periodic-like influences on SMI data may partially
explain the observed difference in types of correlated
behavior of corresponding scaling functions. The application
of time-dependent scaling analysis (tdDMA) proved
that these influences are of a complex type, that is, they
can not be easily distinguished from a local correlations profile.",
journal = "International Conference on Statistical Physics (SigmaPhi2014), Rhodes, 7-11 July 2014",
title = "Scaling analysis of time series of stock market indices of transitional economies in the Western Balkans",
url = "https://hdl.handle.net/21.15107/rcub_veterinar_2704"
}

Stratimirović, Đ., Blesić, S., Miljković, V.,& Sarvan, D.. (2014). Scaling analysis of time series of stock market indices of transitional economies in the Western Balkans. in International Conference on Statistical Physics (SigmaPhi2014), Rhodes, 7-11 July 2014.
https://hdl.handle.net/21.15107/rcub_veterinar_2704

Stratimirović Đ, Blesić S, Miljković V, Sarvan D. Scaling analysis of time series of stock market indices of transitional economies in the Western Balkans. in International Conference on Statistical Physics (SigmaPhi2014), Rhodes, 7-11 July 2014. 2014;.
https://hdl.handle.net/21.15107/rcub_veterinar_2704 .

Stratimirović, Đorđe, Blesić, Suzana, Miljković, Vladimir, Sarvan, Darko, "Scaling analysis of time series of stock market indices of transitional economies in the Western Balkans" in International Conference on Statistical Physics (SigmaPhi2014), Rhodes, 7-11 July 2014 (2014),
https://hdl.handle.net/21.15107/rcub_veterinar_2704 .

TY  - JOUR
AU  - Sarvan, Darko
AU  - Stratimirović, Đorđe
AU  - Blesić, Suzana
AU  - Miljković, Vladimir
PY  - 2014
UR  - https://vet-erinar.vet.bg.ac.rs/handle/123456789/2684
AB  - In this paper we have analyzed scaling properties of time series of stock market indices (SMIs) of developing economies of Western Balkans, and have compared the results we have obtained with the results from more developed economies. We have used three different techniques of data analysis to obtain and verify our findings: detrended fluctuation analysis (DFA) method, detrended moving average (DMA) method, and wavelet transformation (WT) analysis. We have found scaling behavior in all SMI data sets that we have analyzed. The scaling of our SMI series changes from long-range correlated to slightly anti-correlated behavior with the change in growth or maturity of the economy the stock market is embedded in. We also report the presence of effects of potential periodic-like influences on the SMI data that we have analyzed. One such influence is visible in all our SMI series, and appears at a period T p ≈ 90 days. We propose that the existence of various periodic-like influences on SMI data may partially explain the observed difference in types of correlated behavior of corresponding scaling functions.
PB  - SpringerOpen
PB  - Società Italiana di Fisica
PB  - EDP Sciences
T2  - The European Physical Journal B (EPJ B)
T1  - Scaling analysis of time series of daily prices form stock markets of transitional economies in the Western Balkans
VL  - 87
IS  - 297
DO  - 10.1140/epjb/e2014-50655-5
ER  - 

@article{
author = "Sarvan, Darko and Stratimirović, Đorđe and Blesić, Suzana and Miljković, Vladimir",
year = "2014",
abstract = "In this paper we have analyzed scaling properties of time series of stock market indices (SMIs) of developing economies of Western Balkans, and have compared the results we have obtained with the results from more developed economies. We have used three different techniques of data analysis to obtain and verify our findings: detrended fluctuation analysis (DFA) method, detrended moving average (DMA) method, and wavelet transformation (WT) analysis. We have found scaling behavior in all SMI data sets that we have analyzed. The scaling of our SMI series changes from long-range correlated to slightly anti-correlated behavior with the change in growth or maturity of the economy the stock market is embedded in. We also report the presence of effects of potential periodic-like influences on the SMI data that we have analyzed. One such influence is visible in all our SMI series, and appears at a period T p ≈ 90 days. We propose that the existence of various periodic-like influences on SMI data may partially explain the observed difference in types of correlated behavior of corresponding scaling functions.",
publisher = "SpringerOpen, Società Italiana di Fisica, EDP Sciences",
journal = "The European Physical Journal B (EPJ B)",
title = "Scaling analysis of time series of daily prices form stock markets of transitional economies in the Western Balkans",
volume = "87",
number = "297",
doi = "10.1140/epjb/e2014-50655-5"
}

Sarvan, D., Stratimirović, Đ., Blesić, S.,& Miljković, V.. (2014). Scaling analysis of time series of daily prices form stock markets of transitional economies in the Western Balkans. in The European Physical Journal B (EPJ B)
SpringerOpen., 87(297).
https://doi.org/10.1140/epjb/e2014-50655-5

Sarvan D, Stratimirović Đ, Blesić S, Miljković V. Scaling analysis of time series of daily prices form stock markets of transitional economies in the Western Balkans. in The European Physical Journal B (EPJ B). 2014;87(297).
doi:10.1140/epjb/e2014-50655-5 .

Sarvan, Darko, Stratimirović, Đorđe, Blesić, Suzana, Miljković, Vladimir, "Scaling analysis of time series of daily prices form stock markets of transitional economies in the Western Balkans" in The European Physical Journal B (EPJ B), 87, no. 297 (2014),
https://doi.org/10.1140/epjb/e2014-50655-5 . .