171,816 research outputs found
Dynamic structural and topological phase transitions on the Warsaw Stock Exchange: A phenomenological approach
We study the crash dynamics of the Warsaw Stock Exchange (WSE) by using the
Minimal Spanning Tree (MST) networks. We find the transition of the complex
network during its evolution from a (hierarchical) power law MST network,
representing the stable state of WSE before the recent worldwide financial
crash, to a superstar-like (or superhub) MST network of the market decorated by
a hierarchy of trees (being, perhaps, an unstable, intermediate market state).
Subsequently, we observed a transition from this complex tree to the topology
of the (hierarchical) power law MST network decorated by several star-like
trees or hubs. This structure and topology represent, perhaps, the WSE after
the worldwide financial crash, and could be considered to be an aftershock. Our
results can serve as an empirical foundation for a future theory of dynamic
structural and topological phase transitions on financial markets
Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis
In this work, we develop a novel framework to measure the similarity between
dynamic financial networks, i.e., time-varying financial networks.
Particularly, we explore whether the proposed similarity measure can be
employed to understand the structural evolution of the financial networks with
time. For a set of time-varying financial networks with each vertex
representing the individual time series of a different stock and each edge
between a pair of time series representing the absolute value of their Pearson
correlation, our start point is to compute the commute time matrix associated
with the weighted adjacency matrix of the network structures, where each
element of the matrix can be seen as the enhanced correlation value between
pairwise stocks. For each network, we show how the commute time matrix allows
us to identify a reliable set of dominant correlated time series as well as an
associated dominant probability distribution of the stock belonging to this
set. Furthermore, we represent each original network as a discrete dominant
Shannon entropy time series computed from the dominant probability
distribution. With the dominant entropy time series for each pair of financial
networks to hand, we develop a similarity measure based on the classical
dynamic time warping framework, for analyzing the financial time-varying
networks. We show that the proposed similarity measure is positive definite and
thus corresponds to a kernel measure on graphs. The proposed kernel bridges the
gap between graph kernels and the classical dynamic time warping framework for
multiple financial time series analysis. Experiments on time-varying networks
extracted through New York Stock Exchange (NYSE) database demonstrate the
effectiveness of the proposed approach.Comment: Previously, the original version of this manuscript appeared as
arXiv:1902.09947v2, that was submitted as a replacement by a mistake. Now,
that article has been replaced to correct the error, and this manuscript is
distinct from that articl
Structural and topological phase transitions on the German Stock Exchange
We find numerical and empirical evidence for dynamical, structural and
topological phase transitions on the (German) Frankfurt Stock Exchange (FSE) in
the temporal vicinity of the worldwide financial crash. Using the Minimal
Spanning Tree (MST) technique, a particularly useful canonical tool of the
graph theory, two transitions of the topology of a complex network representing
FSE were found. First transition is from a hierarchical scale-free MST
representing the stock market before the recent worldwide financial crash, to a
superstar-like MST decorated by a scale-free hierarchy of trees representing
the market's state for the period containing the crash. Subsequently, a
transition is observed from this transient, (meta)stable state of the crash, to
a hierarchical scale-free MST decorated by several star-like trees after the
worldwide financial crash. The phase transitions observed are analogous to the
ones we obtained earlier for the Warsaw Stock Exchange and more pronounced than
those found by Onnela-Chakraborti-Kaski-Kert\'esz for S&P 500 index in the
vicinity of Black Monday (October 19, 1987) and also in the vicinity of January
1, 1998. Our results provide an empirical foundation for the future theory of
dynamical, structural and topological phase transitions on financial markets
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Structural balance emerges and explains performance in risky decision-making.
Polarization affects many forms of social organization. A key issue focuses on which affective relationships are prone to change and how their change relates to performance. In this study, we analyze a financial institutional over a two-year period that employed 66 day traders, focusing on links between changes in affective relations and trading performance. Traders' affective relations were inferred from their IMs (>2 million messages) and trading performance was measured from profit and loss statements (>1 million trades). Here, we find that triads of relationships, the building blocks of larger social structures, have a propensity towards affective balance, but one unbalanced configuration resists change. Further, balance is positively related to performance. Traders with balanced networks have the "hot hand", showing streaks of high performance. Research implications focus on how changes in polarization relate to performance and polarized states can depolarize
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Weighted network analysis of high frequency cross-correlation measures
In this paper we implement a Fourier method to estimate high-frequency correlation matrices from small data sets. The Fourier estimates are shown to be considerably less noisy than the standard Pearson correlation measures and thus capable of detecting subtle changes in correlation matrices with just a month of data. The evolution of correlation at different time scales is analyzed from the full correlation matrix and its minimum spanning tree representation. The analysis is performed by implementing measures from the theory of random weighted networks. © 2007 The American Physical Society
Interlinkages and structural changes in cross-border liabilities: a network approach
We study the international interbank market through a geometrical and a
topological analysis of empirical data. The geometrical analysis of the time
series of cross-country liabilities shows that the systematic information of
the interbank international market is contained in a space of small dimension,
from which a topological characterization could be conveniently carried out.
Weighted and complete networks of financial linkages across countries are
developed, for which continuous clustering, degree centrality and closeness
centrality are computed. The behavior of these topological coefficients reveals
an important modification acting in the financial linkages in the period
1997-2011. Here we show that, besides the generalized clustering increase,
there is a persistent increment in the degree of connectivity and in the
closeness centrality of some countries. These countries seem to correspond to
critical locations where tax policies might provide opportunities to shift
debts. Such critical locations highlight the role that specific countries play
in the network structure and helps to situates the turbulent period that has
been characterizing the global financial system since the Summer 2007 as the
counterpart of a larger structural change going on for a more than one decade.Comment: 24 pages, 11 figure
On the Von Neumann Entropy of Graphs
The von Neumann entropy of a graph is a spectral complexity measure that has
recently found applications in complex networks analysis and pattern
recognition. Two variants of the von Neumann entropy exist based on the graph
Laplacian and normalized graph Laplacian, respectively. Due to its
computational complexity, previous works have proposed to approximate the von
Neumann entropy, effectively reducing it to the computation of simple node
degree statistics. Unfortunately, a number of issues surrounding the von
Neumann entropy remain unsolved to date, including the interpretation of this
spectral measure in terms of structural patterns, understanding the relation
between its two variants, and evaluating the quality of the corresponding
approximations.
In this paper we aim to answer these questions by first analysing and
comparing the quadratic approximations of the two variants and then performing
an extensive set of experiments on both synthetic and real-world graphs. We
find that 1) the two entropies lead to the emergence of similar structures, but
with some significant differences; 2) the correlation between them ranges from
weakly positive to strongly negative, depending on the topology of the
underlying graph; 3) the quadratic approximations fail to capture the presence
of non-trivial structural patterns that seem to influence the value of the
exact entropies; 4) the quality of the approximations, as well as which variant
of the von Neumann entropy is better approximated, depends on the topology of
the underlying graph
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