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