@article{article_1303067, title={Fractal Analysis of S&P 500 Sector Indexes}, journal={Fiscaoeconomia}, volume={7}, pages={2128–2148}, year={2023}, DOI={10.25295/fsecon.1303067}, author={Ünal, Baki}, keywords={Multifractality, MF-DFA, Multifractal Detrended Fluctuation Analysis, Fractal Theory, S&P 500}, abstract={In this study multifractal properties of S&P 500 sector indexes are investigated with Multifractal Detrended Fluctuation Analysis (MF-DFA). The MF-DFA is a signal processing technique that is used to describe the multifractal properties of a time series data. It is an extension of Detrended Fluctuation Analysis (DFA), which is a widely utilized method for estimating the scaling behavior of a time series. Main idea behind MF-DFA is to decompose a time series into multiple scales using a coarse-graining procedure, and then to estimate the scaling behavior of each scale using DFA. This gives a set of scaling exponents that describe the multifractal features of the time series. Our MF-DFA results indicates the presence of multifractality in all S&P 500 sector indexes. Since these indexes are multifractal, we can conclude that they possess properties such as scaling variability, nonlinear dynamics, self-similarity, long-range dependence, multiscale correlations and nonstationary.}, number={3}, publisher={Ahmet Arif EREN}