Nonlinearity in Stock Networks

0494143 - ÚI 2019 RIV US eng J - Článek v odborném periodiku
Hartman, David - Hlinka, Jaroslav
Nonlinearity in Stock Networks.
Chaos. Roč. 28, č. 8 (2018), č. článku 083127. ISSN 1054-1500. E-ISSN 1089-7682
Institucionální podpora: RVO:67985807
Klíčová slova: small-world networks * financial networks * time-series * functional connectivity * mutual information * climate networks * complex networks * systems * dynamics * organization
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 2.643, rok: 2018

Stock networks, constructed from stock price time series, are a well-established tool for the characterization of complex behavior in stock markets. Following Mantegna’s seminal paper, the linear Pearson’s correlation coefficient between pairs of stocks has been the usual way to determine network edges. Recently, possible effects of nonlinearity on the graph-theoretical properties of such networks have been demonstrated when using nonlinear measures such as mutual information instead of linear correlation. In this paper, we quantitatively characterize the nonlinearity in stock time series and the effect it has on stock network properties. This is achieved by a systematic multi-step approach that allows us to quantify the nonlinearity of coupling, correct its effects wherever it is caused by simple univariate non-Gaussianity, potentially localize in space and time any remaining strong sources of this nonlinearity, and, finally, study the effect nonlinearity has on global network properties. By applying this multi-step approach to stocks included in three prominent indices (New York Stock Exchange 100, Financial Times Stock Exchange 100, and Standard & Poor 500), we establish that the apparent nonlinearity that has been observed is largely due to univariate non-Gaussianity. Furthermore, strong nonstationarity in a few specific stocks may play a role. In particular, the sharp decrease in some stocks during the global financial crisis of 2008 gives rise to apparent nonlinear dependencies among stocks.
Trvalý link: http://hdl.handle.net/11104/0287402