665 research outputs found
An Outlook on Correlations in Stock Prices
We present an outlook of the studies on correlations in the price timeseries
of stocks, discussing the construction and applications of "asset tree". The
topic discussed here should illustrate how the complex economic system
(financial market) enrichens the list of existing dynamical systems that
physicists have been studying for long.Comment: 6 pages, RevTeX format. To appear in the Conference Proceedings of
ECONOPHYS-KOLKATA II: International Workshop on Econophysics of Stock Markets
and Minority Games", February 14-17, 2006, SINP, Kolkata, as a book chapter
in Eds. A. Chatterjee and B.K. Chakrabarti, Econophysics of Stock and other
Markets, (Springer-Verlag (Italia), Milan, 2006
Clustering and information in correlation based financial networks
Networks of companies can be constructed by using return correlations. A
crucial issue in this approach is to select the relevant correlations from the
correlation matrix. In order to study this problem, we start from an empty
graph with no edges where the vertices correspond to stocks. Then, one by one,
we insert edges between the vertices according to the rank of their correlation
strength, resulting in a network called asset graph. We study its properties,
such as topologically different growth types, number and size of clusters and
clustering coefficient. These properties, calculated from empirical data, are
compared against those of a random graph. The growth of the graph can be
classified according to the topological role of the newly inserted edge. We
find that the type of growth which is responsible for creating cycles in the
graph sets in much earlier for the empirical asset graph than for the random
graph, and thus reflects the high degree of networking present in the market.
We also find the number of clusters in the random graph to be one order of
magnitude higher than for the asset graph. At a critical threshold, the random
graph undergoes a radical change in topology related to percolation transition
and forms a single giant cluster, a phenomenon which is not observed for the
asset graph. Differences in mean clustering coefficient lead us to conclude
that most information is contained roughly within 10% of the edges.Comment: 11 pages including 14 figures. Uses REVTeX4. To be published in a
special volume of EPJ on network
The Spontaneous Emergence of Social Influence in Online Systems
Social influence drives both offline and online human behaviour. It pervades
cultural markets, and manifests itself in the adoption of scientific and
technical innovations as well as the spread of social practices. Prior
empirical work on the diffusion of innovations in spatial regions or social
networks has largely focused on the spread of one particular technology among a
subset of all potential adopters. It has also been difficult to determine
whether the observed collective behaviour is driven by natural influence
processes, or whether it follows external signals such as media or marketing
campaigns. Here, we choose an online context that allows us to study social
influence processes by tracking the popularity of a complete set of
applications installed by the user population of a social networking site, thus
capturing the behaviour of all individuals who can influence each other in this
context. By extending standard fluctuation scaling methods, we analyse the
collective behaviour induced by 100 million application installations, and show
that two distinct regimes of behaviour emerge in the system. Once applications
cross a particular threshold of popularity, social influence processes induce
highly correlated adoption behaviour among the users, which propels some of the
applications to extraordinary levels of popularity. Below this threshold, the
collective effect of social influence appears to vanish almost entirely in a
manner that has not been observed in the offline world. Our results demonstrate
that even when external signals are absent, social influence can spontaneously
assume an on-off nature in a digital environment. It remains to be seen whether
a similar outcome could be observed in the offline world if equivalent
experimental conditions could be replicated
Dynamic asset trees and Black Monday
The minimum spanning tree, based on the concept of ultrametricity, is
constructed from the correlation matrix of stock returns. The dynamics of this
asset tree can be characterised by its normalised length and the mean
occupation layer, as measured from an appropriately chosen centre called the
`central node'. We show how the tree length shrinks during a stock market
crisis, Black Monday in this case, and how a strong reconfiguration takes
place, resulting in topological shrinking of the tree.Comment: 6 pages, 3 eps figues. Elsevier style. Will appear in Physica A as
part of the Bali conference proceedings, in pres
Close relationships: A study of mobile communication records
Mobile phone communication as digital service generates ever-increasing
datasets of human communication actions, which in turn allow us to investigate
the structure and evolution of social interactions and their networks. These
datasets can be used to study the structuring of such egocentric networks with
respect to the strength of the relationships by assuming direct dependence of
the communication intensity on the strength of the social tie. Recently we have
discovered that there are significant differences between the first and further
"best friends" from the point of view of age and gender preferences. Here we
introduce a control parameter based on the statistics of
communication with the first and second "best friend" and use it to filter the
data. We find that when is decreased the identification of the
"best friend" becomes less ambiguous and the earlier observed effects get
stronger, thus corroborating them.Comment: 11 pages, 7 figure
The evolution of interdisciplinarity in physics research
Science, being a social enterprise, is subject to fragmentation into groups
that focus on specialized areas or topics. Often new advances occur through
cross-fertilization of ideas between sub-fields that otherwise have little
overlap as they study dissimilar phenomena using different techniques. Thus to
explore the nature and dynamics of scientific progress one needs to consider
the large-scale organization and interactions between different subject areas.
Here, we study the relationships between the sub-fields of Physics using the
Physics and Astronomy Classification Scheme (PACS) codes employed for
self-categorization of articles published over the past 25 years (1985-2009).
We observe a clear trend towards increasing interactions between the different
sub-fields. The network of sub-fields also exhibits core-periphery
organization, the nucleus being dominated by Condensed Matter and General
Physics. However, over time Interdisciplinary Physics is steadily increasing
its share in the network core, reflecting a shift in the overall trend of
Physics research.Comment: Published version, 10 pages, 8 figures + Supplementary Informatio
Dynamic asset trees and portfolio analysis
The minimum spanning tree, based on the concept of ultrametricity, is
constructed from the correlation matrix of stock returns and provides a
meaningful economic taxonomy of the stock market. In order to study the
dynamics of this asset tree we characterize it by its normalized length and by
the mean occupation layer, as measured from an appropriately chosen center. We
show how the tree evolves over time, and how it shrinks particularly strongly
during a stock market crisis. We then demonstrate that the assets of the
optimal Markowitz portfolio lie practically at all times on the outskirts of
the tree. We also show that the normalized tree length and the investment
diversification potential are very strongly correlated.Comment: 9 pages, 3 figures (encapsulated postscript
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
Network science is an interdisciplinary endeavor, with methods and
applications drawn from across the natural, social, and information sciences. A
prominent problem in network science is the algorithmic detection of
tightly-connected groups of nodes known as communities. We developed a
generalized framework of network quality functions that allowed us to study the
community structure of arbitrary multislice networks, which are combinations of
individual networks coupled through links that connect each node in one network
slice to itself in other slices. This framework allows one to study community
structure in a very general setting encompassing networks that evolve over
time, have multiple types of links (multiplexity), and have multiple scales.Comment: 31 pages, 3 figures, 1 table. Includes main text and supporting
material. This is the accepted version of the manuscript (the definitive
version appeared in Science), with typographical corrections included her
Effects of time window size and placement on the structure of aggregated networks
Complex networks are often constructed by aggregating empirical data over
time, such that a link represents the existence of interactions between the
endpoint nodes and the link weight represents the intensity of such
interactions within the aggregation time window. The resulting networks are
then often considered static. More often than not, the aggregation time window
is dictated by the availability of data, and the effects of its length on the
resulting networks are rarely considered. Here, we address this question by
studying the structural features of networks emerging from aggregating
empirical data over different time intervals, focussing on networks derived
from time-stamped, anonymized mobile telephone call records. Our results show
that short aggregation intervals yield networks where strong links associated
with dense clusters dominate; the seeds of such clusters or communities become
already visible for intervals of around one week. The degree and weight
distributions are seen to become stationary around a few days and a few weeks,
respectively. An aggregation interval of around 30 days results in the stablest
similar networks when consecutive windows are compared. For longer intervals,
the effects of weak or random links become increasingly stronger, and the
average degree of the network keeps growing even for intervals up to 180 days.
The placement of the time window is also seen to affect the outcome: for short
windows, different behavioural patterns play a role during weekends and
weekdays, and for longer windows it is seen that networks aggregated during
holiday periods are significantly different.Comment: 19 pages, 11 figure
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