The understanding of complex systems has become a central issue because
complex systems exist in a wide range of scientific disciplines. Time series
are typical experimental results we have about complex systems. In the analysis
of such time series, stationary situations have been extensively studied and
correlations have been found to be a very powerful tool. Yet most natural
processes are non-stationary. In particular, in times of crisis, accident or
trouble, stationarity is lost. As examples we may think of financial markets,
biological systems, reactors or the weather. In non-stationary situations
analysis becomes very difficult and noise is a severe problem. Following a
natural urge to search for order in the system, we endeavor to define states
through which systems pass and in which they remain for short times. Success in
this respect would allow to get a better understanding of the system and might
even lead to methods for controlling the system in more efficient ways.
We here concentrate on financial markets because of the easy access we have
to good data and because of the strong non-stationary effects recently seen. We
analyze the S&P 500 stocks in the 19-year period 1992-2010. Here, we propose
such an above mentioned definition of state for a financial market and use it
to identify points of drastic change in the correlation structure. These points
are mapped to occurrences of financial crises. We find that a wide variety of
characteristic correlation structure patterns exist in the observation time
window, and that these characteristic correlation structure patterns can be
classified into several typical "market states". Using this classification we
recognize transitions between different market states. A similarity measure we
develop thus affords means of understanding changes in states and of
recognizing developments not previously seen.
%0 Generic
%1 Munnix2012Identifying
%A Münnix, Michael C.
%A Shimada, Takashi
%A Schäfer, Rudi
%A Leyvraz, Francois
%A Seligman, Thomas H.
%A Guhr, Thomas
%A Stanley, H. Eugene
%D 2012
%J Scientific Reports
%K financial-markets econophysics
%R 10.1038/srep00644
%T Identifying States of a Financial Market
%U http://dx.doi.org/10.1038/srep00644
%V 2
%X The understanding of complex systems has become a central issue because
complex systems exist in a wide range of scientific disciplines. Time series
are typical experimental results we have about complex systems. In the analysis
of such time series, stationary situations have been extensively studied and
correlations have been found to be a very powerful tool. Yet most natural
processes are non-stationary. In particular, in times of crisis, accident or
trouble, stationarity is lost. As examples we may think of financial markets,
biological systems, reactors or the weather. In non-stationary situations
analysis becomes very difficult and noise is a severe problem. Following a
natural urge to search for order in the system, we endeavor to define states
through which systems pass and in which they remain for short times. Success in
this respect would allow to get a better understanding of the system and might
even lead to methods for controlling the system in more efficient ways.
We here concentrate on financial markets because of the easy access we have
to good data and because of the strong non-stationary effects recently seen. We
analyze the S&P 500 stocks in the 19-year period 1992-2010. Here, we propose
such an above mentioned definition of state for a financial market and use it
to identify points of drastic change in the correlation structure. These points
are mapped to occurrences of financial crises. We find that a wide variety of
characteristic correlation structure patterns exist in the observation time
window, and that these characteristic correlation structure patterns can be
classified into several typical "market states". Using this classification we
recognize transitions between different market states. A similarity measure we
develop thus affords means of understanding changes in states and of
recognizing developments not previously seen.
@misc{Munnix2012Identifying,
abstract = {{The understanding of complex systems has become a central issue because
complex systems exist in a wide range of scientific disciplines. Time series
are typical experimental results we have about complex systems. In the analysis
of such time series, stationary situations have been extensively studied and
correlations have been found to be a very powerful tool. Yet most natural
processes are non-stationary. In particular, in times of crisis, accident or
trouble, stationarity is lost. As examples we may think of financial markets,
biological systems, reactors or the weather. In non-stationary situations
analysis becomes very difficult and noise is a severe problem. Following a
natural urge to search for order in the system, we endeavor to define states
through which systems pass and in which they remain for short times. Success in
this respect would allow to get a better understanding of the system and might
even lead to methods for controlling the system in more efficient ways.
We here concentrate on financial markets because of the easy access we have
to good data and because of the strong non-stationary effects recently seen. We
analyze the S\&P 500 stocks in the 19-year period 1992-2010. Here, we propose
such an above mentioned definition of state for a financial market and use it
to identify points of drastic change in the correlation structure. These points
are mapped to occurrences of financial crises. We find that a wide variety of
characteristic correlation structure patterns exist in the observation time
window, and that these characteristic correlation structure patterns can be
classified into several typical "market states". Using this classification we
recognize transitions between different market states. A similarity measure we
develop thus affords means of understanding changes in states and of
recognizing developments not previously seen.}},
added-at = {2019-06-10T14:53:09.000+0200},
archiveprefix = {arXiv},
author = {M\"{u}nnix, Michael C. and Shimada, Takashi and Sch\"{a}fer, Rudi and Leyvraz, Francois and Seligman, Thomas H. and Guhr, Thomas and Stanley, H. Eugene},
biburl = {https://www.bibsonomy.org/bibtex/23344071959f4777e90d4c6d403bb55ab/nonancourt},
citeulike-article-id = {10329660},
citeulike-linkout-0 = {http://dx.doi.org/10.1038/srep00644},
citeulike-linkout-1 = {http://arxiv.org/abs/1202.1623},
citeulike-linkout-2 = {http://arxiv.org/pdf/1202.1623},
day = 10,
doi = {10.1038/srep00644},
eprint = {1202.1623},
interhash = {c023cce9076a2f3e8de2f3963ac772d5},
intrahash = {3344071959f4777e90d4c6d403bb55ab},
issn = {2045-2322},
journal = {Scientific Reports},
keywords = {financial-markets econophysics},
month = sep,
posted-at = {2012-02-10 10:54:35},
priority = {2},
timestamp = {2019-08-01T15:39:33.000+0200},
title = {{Identifying States of a Financial Market}},
url = {http://dx.doi.org/10.1038/srep00644},
volume = 2,
year = 2012
}