Lorentz Center - Econophysics and Networks Across Scales from 27 May 2013 through 31 May 2013
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    Econophysics and Networks Across Scales
    from 27 May 2013 through 31 May 2013

 

Abstracts

 

Organizers

 

Diego Garlaschelli, Leiden University, Netherlands

H. Eugene Stanley, Boston University, USA

 

 

Technical Committee

 

Maria I. Loffredo, University of Siena, Italy

Peter Denteneer, Leiden University, Netherlands

Iman van Lelyveld, De Nederlandsche Bank, Netherlands

Reindert Stoffer, Duyfken Trading Knowledge BV, Netherlands

Ferry Vos, Anthos, Netherlands

 

 

 

Assaf Almog (Leiden, Netherlands)

 

“Maximum Entropy Matrices: Binary vs Weighted properties of financial time series”

 

We introduce a novel information-theoretic approach to the analysis of single and multiple time series, with empirical applications to real financial time series. Our formalism allows us to connect stochastic processes with ensembles of time series inferred from partial information, and to extract and quantify information from single or multiple time series. The method allows us to assign a level of uncertainty to a time series given measured properties (of the time series), and also to compare which property of a specific time series is more informative.

 

 

 

Alexander Becker (Duisburg, Germany)

 

"The dependence between credit default risk and recovery rates."

 

While many studies in Econophysics have been devoted to equity, we must not forget debt markets. In 2010, for example, the value of outstanding bonds was twice the global equity-market capitalization. Debt analysis is mainly driven by the key-figure of credit risk, the default probability. We will show that it is important to also include the recovery rate of the bonds. Investigating historic default events, we examine its behavior. Furthermore, we will illustrate that the two measures empirically follow a functional dependence, which can be derived from the original Merton model of 1974.

 

 

 

Sonia Bentes (Lisbon, Portugal)

 

“Views of Econophysics from the perspective of Finance”

 

The most commonly used measure of stock market volatility has been the standard deviation. However, since it exhibits some drawbacks we purpose an alternative approach based on the concept of entropy, whose main advantage relies on the fact that it makes possible a more comprehensive description of such volatility. In view of the fact that Shannon entropy is only suitable for describing equilibrium systems we consider Tsallis entropy, more appropriate to describe anomalous systems, into which category financial markets appear to fall. More specifically, a comparison is made in this research between the results of the traditional approach based on the standard deviation and those provided by the entropy, on the other. A sample is used consisting of the returns of the main stock market indexes of the G7 countries during the period from January, 1999 to January, 2009. The results evidence the limitations of the standard deviation-based approach in fully characterizing volatility.

 

 

Pasquale Cirillo (Delft, Netherlands)

 

“Are your data really power law distributed?”

 

Power laws have shown to be very useful models to describe many different phenomena, from physics to finance. In recent years, the econophysical literature has proposed a large amount of papers and models justifying the presence of power laws in economic data. But is it really that good?

In this talk I review three heuristic methods that may be used to search for power laws in empirical data: log-log plots, mean excess plots and self-similarity plots. The first tool has been extensively used in the last years, while the other two methods are less popular. However, I will show that all these methods can generate serious misunderstanding, and that one needs to use them carefully and to combine them, in order to have a good idea about the nature of data.

Two "new" graphical tools that can be helpful to verify the presence of Paretianity are also presented.

 

 

 

Matthieu Cristelli (Rome, Italy)

 

“New Metrics for Economic Complexity: Measuring the Intangible Growth Potential of Countries”

 

Economic Complexity is a new line of research which portrays economic growth as an evolutive process of ecosystems of technologies and industrial capabilities. Complex systems analysis, simulation, systems science methods, and big data capabilities offer new opportunities to empirically map technology and capability ecosystems of countries and industrial sectors, analyse their structure, understand their dynamics and measure economic complexity. This approach provides a new perspective for data-driven fundamental economics in a strongly connected, globalised world. 

In particular here we discuss how it is possible to assess the competitiveness of country and complexity of products starting from the archival data on export flows that is the COMTRADE dataset which provides the matrix of countries and their exported products. According to the standard economic theory the specialization of countries towards certain specific products should be optimal. The observed data show that this is not the case and that diversification is actually more important. Specialization may be the leading effect in a static situation but the strongly dynamic and globalized world market suggests instead that flexibility and adaptability are essential elements of competitiveness as in bio-systems.

The crucial challenge is therefore how these qualitative observations can be turned into quantitative variables. We have introduced a new metrics for the Fitness of countries and the Complexity of products which corresponds to the fixed point of the iteration of two nonlinear coupled equations. The nonlinearity is a key feature because it translates in mathematical terms the fact that the upper bound on the Complexity of a product must be mainly given by the less developed country able to produce it. The information provided by the new metrics can be used in several ways. As an example, the direct comparison of the Fitness with the country GDP per capita (Fitness-Income Plane) gives an assessment of the non-expressed potential of growth of a country. This can be used as a predictor of GDP evolution or stock index and sectors performances.

The global dynamic in the Fitness-Income Plane reveals, however, a large degree of heterogeneity which implies that countries can evolve with different level of predictability according to the specific zone of the Fitness-Income plane they belong to. This heterogeneous dynamics is often disregarded in usual economic analysis. When dealing with heterogeneous systems, in fact, the usual tools of linear regressions become inappropriate. Making reliable predictions of growth in the context of economic complexity will then require a paradigm shift in order to catch the information contained in the complex dynamic patterns observed.

These methods and concepts can give concrete contributions, as other possible applications, to risk analysis, investment opportunities analysis, policy-modelling of country growth and industrial planning.

 

 

 

Tiziana di Matteo (London, UK)

 

“Spread of risk across financial markets: better to invest in the peripheries”

 

In this talk I will introduce a methodology and a set of tools to filter complex dependency structures in financial datasets by using networks [1-2]. The topology of these networks efficiently encodes the complex dependency structure reducing data complexity while preserving the fundamental characteristics of the dataset. This methodology has the added advantage of visualizing directly the complex organization of the dependency structure over the graphic layout of the network.

I will discuss how this approach can be used to build a well-diversified portfolio that effectively reduces investment risk. Specifically I will show that investments in stocks that occupy peripheral, poorly connected regions in the financial filtered networks are most successful in diversifying investments even for small baskets of stocks. On the contrary, investments in subsets of central, highly connected stocks are characterized by greater risk and worse performance [3].

I will also introduce a general graph-theoretic approach that use these filtered networks to simultaneously extract clusters and hierarchies in an unsupervised and deterministic manner, without the use of any prior information and without need to specify any threshold [4-5]. I will show that applications to financial data-sets can meaningfully identify industrial activities and structural market changes.

 

[1] T. Aste, T. Di Matteo, S. T. Hyde,  Physica A 346 (2005) 20-26.

[2] M. Tumminello, T. Aste, T. Di Matteo, R. N. Mantegna, PNAS 102, n. 30 (2005) 10421.

[3] F. Pozzi, T. Di Matteo and T. Aste, Scientific Reports 3 (2013) 1665.

[4] Won-Min Song, T. Di Matteo, T. Aste, Discrete Applied Mathematics 159 (2011) 2135.

[5] Won-Min Song, T. Di Matteo, T. Aste, PLoS One 7(3) (2012) e31929.



Yoshi Fujiwara (Hyogo, Japan)

 

 “Chained Financial Failures at Nation-wide Scale in Japan”

 

I will talk about recent studies based on real data of propagation of financial failures in the past financial crises and the present one due to the earthquake at nation-wide scales in Japan. Leading credit research agencies in Tokyo and Nikkei have accumulated a huge amount of data on banks-firms and supplier-customer links with financial information and failures of nodes. By using these large-scale data, we measure the actually occurred propagation of financial distress on the real data of large-scale economic networks comprising of firms, banks, and their relationships at the order of millions and even more. Exogenous shocks due to global financial crisis and mass destruction by disasters such as earthquakes cause propagation resulting in a sluggish relaxation, typically observed as an Omori-law.

 

 

 

Diego Garlaschelli (Leiden, Netherlands)

 

“Jan Tinbergen's legacy for economic networks: from the gravity model to quantum statistics”

 

Jan Tinbergen, the first recipient of the Nobel Memorial Prize in Economics in 1969, obtained his PhD in physics at the University of Leiden under the supervision of Paul Ehrenfest in 1929. Among many achievements as an economist after his training as a physicist, Tinbergen proposed the so-called Gravity Model of international trade. The model predicts that the intensity of trade between two countries is described by a formula similar to Newton's law of gravitation, where mass is replaced by Gross Domestic Product. Since Tinbergen's proposal, the Gravity Model has become the standard model of non-zero trade flows in macroeconomics. However, its intrinsic limitation is the prediction of a completely connected network, which fails to explain the observed intricate topology of international trade. Recent network models overcome this limitation by describing the real network as a member of a maximum-entropy statistical ensemble. The resulting expressions are formally analogous to quantum statistics: the international trade network is found to closely follow the Fermi-Dirac statistics in its purely binary topology, and the recently proposed mixed Bose-Fermi statistics in its full (binary plus weighted) structure. This seemingly esoteric result is actually a simple effect of the heterogeneity of world countries, which imposes strong structural constraints on the network. Our discussion highlights similarities and differences between macroeconomics and statistical-physics approaches to economic networks.

 

 

 


 

Changgui Gu (Leiden, Netherlands)

 

“Onset of cooperation between layered networks”

 

Functionalities of a variety of complex systems involve cooperation among multiple components; for example, a transportation system provides convenient transfers among airlines, railways, roads, and shipping lines. A layered model with interacting networks can facilitate the description and analysis of such systems. In this paper we propose a model of traffic dynamics and reveal a transition at the onset of cooperation between layered networks. The cooperation strength, treated as an order parameter, changes from zero to positive at the transition point. Numerical results on artificial networks as well as two real networks, Chinese and European railway-airline transportation networks, agree well with our analysis.

 

 

 

Cars Hommes (Amsterdam, Netherlands)

 

“Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments”

 

We discuss `learning to forecast' laboratory experiments with human subjects to study formation of individual expectations, their interactions and the aggregate market structure they co-create. Three different patterns in aggregate price behavior have been observed: slow monotonic convergence, permanent oscillations and dampened fluctuations. We show that a simple model of individual learning and evolutionary selection of heterogeneous expectation rules can explain these different aggregate outcomes within the same experimental setting. In markets with positive feedback trend-following strategies are likely to survive evolutionary selection causing persistent deviations and market fluctuations around the rational fundamental benchmark.

 

 

 

Andreas Joseph (Kowloon, Hong Kong)

 

“Making Sense of Big Data, Network Science and Economics”

 

First Thought: A Uniform Framework for Network Data Analysis.

Many real-world networks can be looked at considering several levels of complexity, such as the binary, weighted and directed level. A prominent example is the World Trade Web. On each level, different network measures quantify different flow processes, which, in turn, evaluate certain expectations about underlying network processes. Often, those processes are not well understood by themselves, creating the desire for a uniform framework of evaluating and comparing multiple perspectives. Motivated by this, we present the composite centrality framework which is based on proper measure standardisation. In additional, the exploration of collective scaling behaviours observed among different metrics leads to the concept of exceptionality, defined as a deviation thereof, which allows for the discovery of peculiar graph configurations.

Second Thought: Early-Warning Indicators for Financial Crisis.

Since the global financial crisis 2008, there is a growing awareness of the inter-connectedness and inter-dependency of financial markets, and the resulting systemic risk. However, much of the relevant information/data is deemed confidential on the institutional level. We present results from the analysis of financial networks serving as proxies for the global financial/economic architecture, namely cross-border portfolio investment networks. A set of indicators for stability and inter-connectedness of financial markets can be identified. These show signs of potential distress already well ahead of the actual crisis.

 

 

 

Drona Kandhai (Amsterdam, Netherlands)

 

“An effective early-warning signal for the Lehman Brothers collapse based on information theory”

 

The largest financial crash in the past decades is the bankruptcy of Lehman Brothers which was followed by a trust-based crisis between banks. In this talk we introduce information dissipation length (IDL) as a leading indicator of global instability of dynamical systems based on the transmission of Shannon information, and apply it to the time series of USD and EUR interest rate swaps (IRS). In both markets, we find that the IDL steadily increases toward the bankruptcy, then peaks at the time of bankruptcy, and decreases afterwards. These results suggest that the IDL may be used as an early-warning signal for critical transitions in financial systems. We strongly believe that the methodology can be applied a wide range of complex systems.

 

 

 

János Kertész (Budapest, Hungary)

 

“Innovation spreading using communication data”

 

Theories of innovation spreading (or innovation diffusion) rely on social interaction and media influence. Until recently only sparse data were available about the detailed mechanism of spreading. We use data from Skype, the largest Internet-based free phone provider to follow in detail the process. As Skype has free and pay services, the investigated structure is a three-layered network: social network - free service network - pay service network. We study the innovation spreading in a large number of different countries. We assume an epidemic spreading model with peer pressure plus media effect and solve the mean field equations with simple assumptions, which can be checked with the data. The theory works surprisingly well in characterising different scenarios and it even enables to make predictions.


 

Jan Korbel (Prague, Czech Republic)

 

“Methods and Techniques for Multifractal Spectrum Estimation in Financial Time Series”

 

Scaling properties belong to the most important signatures describing complexity involved in dynamics of many real systems, financial markets are no exception. Presence of scaling usually points to the presence of some underlying non-trivial fractal structure in temporal correlations. Techniques of fractal geometry can be then applied to reveal the potential scaling behavior. Often systems exhibit a multiple scaling, then the scaling exponents can be found via methods of multifractal analysis. The presence of an array of scaling exponent usually points to the presence of such phenomena, ass economic cycles, crises, etc. In this talk are compared some of the existing techniques of estimation of multifractal spectrum. Our particular focus will be on Multifractal detrended analysis and Multifractal entropy analysis. We outline their respective interpretations, and compare the methods from both theoretical and practical points of view. Finally, we apply the methods in question to analyze financial time series of S&P500 gathered over period of 50 years.

 

 

 

Ladislav Kristoufek (Prague, Czech Republic)

 

“Leverage effect between financial returns and volatility: A long-range cross-correlations perspective”

 

A negative relationship between returns and changes in volatility is a well-documented phenomenon in the financial economics. Investigation of the leverage effect is frequently connected to the asymmetric volatility phenomenon, which is usually treated as a situation when volatility of a growing market tends to be lower than volatility of a falling market. The interconnection is thus very tight and it is mostly quite hard to distinguish between these two effects. Nonetheless, most of the authors agree on several regularities – correlation between returns and volatility is negative but rather weak, the effect comes from the returns to volatility and lasts for several periods while the effect remains negative and quite persistent. We look at the leverage effect from the long-range cross-correlations perspective. As the financial returns are usually treated as serially uncorrelated and volatility is taken as a long-range correlated process regardless of the volatility measure applied, the leverage effect makes the pair an ideal candidate for long-range cross-correlations inspection. We focus on 14 stock indices and analyze the leverage effect while focusing on the presence of long-range cross-correlations between returns and realized volatility. We then analyze whether the effect arises from the processes properties we find and whether it can be mimicked by a simple model that we propose. We first describe the dataset and its statistical properties. Then, we test whether the processes of returns and volatility are long-range cross-correlated and since the majority of the analyzed indices turns out to be long-range cross-correlated, we follow with the analysis of power law coherency. As the power law coherency is not observed, we close with the discussion of the leverage effect and we propose a simple model that can mimic the effect as well as other stylized facts of the financial returns.

 

 

 

Fabrizio Lillo (Pisa, Italy)

 

“The relation between the trading activity of financial agents and the stock price dynamics”

 

In recent years databases containing the trading activity of all the agents in a financial markets have become available to researchers. This opens the possibility of interesting empirical analyses of the ways in which agents are affected by other agents or by external inputs, and the relation between agents and price dynamics. I review the results of three recent papers where: (1) we study how the number of agents and the imbalance between the number of buyers and the number of sellers affect and is affected by price returns and volatility. (2) we study the relative role of endogenous (returns and volatility) and exogenous (news) factors in the trading decision of agents; (3) we identify clusters of investors trading in a financial market characterized by a very high degree of synchronization in time when they decide to trade and in the trading action taken.

 

 

 

Eliza-Olivia Lungu (Bucharest, Romania)

 

“Is occupational mobility predictable?”

 

We explore the early career mobility of the Romanian higher education graduates using the network analysis approach. The nodes are represented by occupations (3 digits groups according to ISCO 88), while the links represent movements of individuals switching from one job to another. A job change is defined as an experience of inter-organisational mobility. The network is constructed as a weighted and directed one with self-loops (graduates changing their job in the same occupational category). Having in mind the idea that occupations are related to each other via transferable skills, we visualize paths of mobility and calculate network indicators in order to understand models of connectivity between occupations. Exploiting a dataset on working histories of higher education graduates from Romania during their early career, we provide novel evidence on the fact that individuals move according to certain career pathways and that the entrance occupation influence their subsequent career.

 

 

 


 

Mel MacMahon (New York, USA)

 

“Extending Community Detection to Probe the Structure of Financial Time Series”

 

Community detection in complex networks has been a hot topic over the past decade, with research producing myriad models and algorithms to partition a vast number of structurally different complex networks. One such set of networks is that created from time series data, where the edges between each pair of nodes are derived in some way from the cross correlations of the node pairs. To date, community detection algorithms as they have been applied to such networks continue to use the original formulation, whose null model is derived from the degree of nodes in the network. In this presentation we discuss the use of more appropriate null models, based on covariance and show how to rework some of the more popular community detection algorithms to use these null models. We apply this technique to sets of financial time series’ from a variety of different, international equities markets to show how it can be used to accurately discern communities of stocks. In particular, we show these communities are internally, more correlated with each other than expected, while at the same time being anti correlated with the other communities, a property of considerable value particularly in fields such as portfolio optimization and risk management.  

 

 

 

Rosario N. Mantegna (Palermo, Italy)

 

“Statistically validated networks of market members trading at the LSE electronic and dealers' market”

 

We empirically detect and analyze trading networks, which are present among all market members of the London Stock Exchange (LSE) trading shares of a specific stock in a selected period of time. We analyze the anonymous electronic book and the networked dealers' market separately, and we statistically validate a link between two market members if the number of transactions of a selected stock that occur between the two market members is too large to be explained according to a null hypothesis of random trading between them. Specifically, we separately analyze the trading networks of market members trading five highly liquid stocks, in the two LSE venues, from daily to yearly time scale, during the calendar year 2005. For the selected stocks, we find that trading networks for the dealers' market are bigger and more stable over time than those observed for the electronic market. Our results confirm that anonymity in the electronic order book minimizes the probability of preferential pair interactions and implies that concerns about adverse selection in the dealers' market are somewhat compensated by other positive aspects, such as the possibility of exchanging large volumes in a single transaction or obtaining a transaction price within the current spread, which are specific to the dealers' market.

 

 

Rossana Mastrandrea (Pisa, Italy)

 

“Weighted networks with given strengths and degrees: a fast and unbiased method”

 

In the analysis of real networks, it is essential to filter out the effects of local topological properties in order to detect nontrivial higher-order patterns.   In binary graphs, this is done using a null model that controls for the degrees of all vertices. In weighted networks, the standard approach is to control for the strengths of all vertices. However, recent counter-intuitive results suggest that, even in weighted networks, degrees are as fundamental as strengths, and irreducible to the latter.  This conjecture implies that null models of weighted networks should control for both quantities, a computationally hard and bias-prone problem.  Here we solve this problem by introducing an analytical and unbiased method that works in shortest possible time and does not require the explicit generation of randomized networks. We apply our method to economic systems and rigorously confirm the conjecture by showing that, while the strengths alone are poorly informative, the additional knowledge of the degrees is extremely informative and at the same time does not overfit the network.

 

 

 

Helen Susannah Moat (London, UK)

 

“Can online data anticipate economic behaviour?”

 

Economic crises affect humans worldwide. Vast stock market datasets offer a window into the catastrophic combination of decisions that lead to such crises, but do not tell us how these decisions were reached. In the work I will describe in this talk, we ask whether Internet usage data might help us understand the early information gathering stages of traders' decision making processes. By analysing changes in the frequency with which Wikipedia [1] and Google [2] users look for information related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. These results suggest that big data capturing our everyday interactions with the Internet may allow us to gain insight into early information gathering stages of collective decision making, on a scale previously impossible to achieve [3].

 

[1] Moat, H. S., Curme, C., Avakian, A., Kenett, D.Y., Stanley, H. E., & Preis, T. (2013). Quantifying Wikipedia usage patterns before stock market moves. Scientific Reports, 3, 1801.

[2] Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3, 1684.

[3] Moat, H. S., Preis, T., Olivola, C.Y., Liu, C., & Chater, N. (in press). Using big data to predict collective behavior in the real world. Behavioral and Brain Sciences.

 

 

 


 

NicoloMusmeci (London, UK)

 

“Dynamical analysis of clustering on financial market data”

 

In this talk I will show the application of the DBHT method [1] to a set of 342 US stocks daily prices during the time period between 1997 and 2012. The DBHT method is a novel approach to extract cluster structure and to detect hierarchical organization in complex data-sets, it is based on the study of the properties of topologically embedded graphs [2], it is deterministic, requires no a-priori parameters and it does not need any expert supervision. In the case of financial data, the method yields a clustering set of stocks. I will discuss the dynamical evolution of these clusters and show results about their persistence over time, together with analyses about their varying similarity with the Industrial Sectors classification. To this aim I will introduce dynamical measures taken from the theory of temporal networks; these measures point out peculiar behaviours in coincidence with the 2007-08 financial crisis [3].

 

[1] Won-Min Song, T. Di Matteo, T. Aste, "Hierarchical information clustering by means of topologically embedded graphs", PLoS One 7(3) (2012) e31929.

[2] T. Aste, T. Di Matteo, S. T. Hyde, "Complex networks on hyperbolic surfaces”, Physica A 346 (2005) 20-26.

[3] N. Musmeci, T. Di Matteo, T. Aste, working paper 2013.

 

 

 

Alexander Petersen (Lucca, Italy)

 

“Multilevel networks in science: from individual careers to Europe”

 

Quantitative measures are becoming increasingly prevalent at all scales of scientific evaluation, from countries, to universities, departments, laboratories, and individuals. In this talk I will discuss the multi-level scientific networks that can be constructed from these output measures and the growth factors associated with the knowledge, human, and public capital spillovers which are facilitated by the network structure. Indeed, there is mounting evidence that both career growth and economic growth are intrinsically related to underlying features of co-evolving scientific networks. At the level of careers, I will discuss the role of strong ties in superstar careers, and the evolution of these ties longitudinally across the career. At the level of countries, I will discuss recent results obtained by analyzing 4 networks constructed from 2.4 million patent applications filed with the European Patent Office (EPO) over the 25-year period 1986-2010 [Science 339, 650-651 (2013)]. Combining econometric methods with network science we perform a comparative network analysis across time and between EU and non-EU countries to determine the “treatment effect” resulting from EU integration policies. Using non-EU countries as a control set, we provide quantitative evidence that, despite decades of efforts to build a European Research Area, there has been little integration above global trends in patenting and publication. This analysis provides concrete evidence that Europe remains a collection of national innovation systems.

 

 

Tobias Preis (Coventry, UK)

 

“Quantifying the complex world we inhabit using big data”

 

Society’s steadily increasing interactions with technology are creating volumes of digital traces documenting our collective human behaviour, fuelling the rapid development of the new field of computational social science.

In this talk, I will outline some recent highlights of our research, addressing two questions. Firstly, can we provide insight into international differences in economic wellbeing by comparing patterns of interaction with the Internet? To answer this question, we introduce a future-orientation index to quantify the degree to which Internet users seek more information about years in the future than years in the past. We analyse Google logs and find a striking correlation between the country's GDP and the predisposition of its inhabitants to look forward [1].

Secondly, can data from Flickr, a popular website for sharing personal photographs, provide insights into user attention to the Hurricane Sandy disaster in 2012? We find that the number of photos taken and subsequently uploaded to Flickr with titles, descriptions or tags related to Hurricane Sandy bears a striking correlation to the atmospheric pressure in the US state New Jersey during this period. Appropriate leverage of such information could be useful to policy makers and others charged with emergency crisis management. Our results illustrate the potential that combining extensive behavioural data sets offers for a better understanding of large scale human behaviour.

 

[1] T. Preis, H. S. Moat, H. E. Stanley, S. R. Bishop, Quantifying the Advantage of Looking Forward. Sci. Rep. 2, 350 (2012).

 

 

 

Andrea Scharnhorst (Den Haag, Netherlands)

 

KNOWeSCAPE - Dynamics of knowledge spaces”

 

This talk introduces into the goals of a COST Action in which physicists, computer scientists, sociologists, digital humanities scholars, and information professionals try to better understand the dynamics in large information spaces and to develop knowledge maps for better navigation through them.

 

 

 

Tiziano Squartini (Leiden, Netherlands)

 

“Stationary and non-stationary behavior of meso-scale and macro-scale networks”

 

Networks belonging to different scale regimes can show very different kinds of temporal evolution. In the present talk we consider two different economic networks, belonging to different scales: the Dutch Interbank Network (DIN) over the period 1998-2008 (meso-scale) and the World Trade Web (WTW) over the period 1950-2000 (macro-scale). By employing a recently proposed analytical pattern-detection method, we study the role that local properties have in shaping higher-order patterns of both the WTW, in all its possible representations (binary or weighted, directed or undirected, aggregated or disaggregated by commodity and across several years) and the DIN as a binary, directed network. In particular, we focus on the occurrence of dyadic motifs (two-vertices subgraphs) and triadic motifs (three-vertices subgraphs). The two systems have a completely different behavior: whereas the triadic z-scores of the WTW show the same profile across the considered temporal period, pointing out the substantial stationarity of this network, the triadic z-scores of the DIN give origin to four different profiles, subdividing the analysed decade in four subperiods, directly related to the evolution of the system towards the critical configuration of 2008. Moreover, whereas the higher-order properties of the WTW binary representations are well reproduced by constraining the nodes' degrees, the higher-order structure of the DIN is not reproduced by the same kind of topological constraints. What is most interesting in the second case is the detection of a slow and continuous transition of the (otherwise unexplainable) topological properties from the crisis period to a much earlier stationary phase, providing a clear early-warning signal of the upcoming “big” event.

Our results highlight the importance of understanding the (non) stationary character of the considered network, since this can dramatically affect the possibility of forecasting the specific network's behavior, as evident when thinking about the risk of a systemic contagion in a financial network.

 

 

 

H. Eugene Stanley (Boston, USA)

 

[TBA]

 

 

 

Yuriy Stepanov (Duisbrug, Germany)

 

“Market States and Planar Maximally Filtered Graphs”

 

Münnix et al. purposed a method to identify states of a financial market through a clustering of the similarities in the correlation structure of (daily) stock returns  [1]. Assigning a correlation matrix C(t) to every time point t one can measure similarity (resp. dissimilarity) of the time points. These can be then clustered into the market states. The market states we obtained by using different clustering methods are all consistent with those in [1]. In every case the number of market states is not given by the system itself and requires additional prior information (fixed number of states, clustering threshold etc.).

In [2] the authors present a clustering method by means of topologically embedded graphs - the DBHT technique (the authors don't give the explanation of the abbreviation) applied to the Planar Maximally Filtered Graphs (PMFG), which works without any use of prior information. A PMFG is constructed out of a similarity (resp. dissimilarity) matrix, which is given in our case.

As a future project we want to construct a PMFG, the nodes of which are not companies or countries, but time points, and to apply then the DBHT technique to this time point network, since for this clustering procedure no prior information is needed.

 

[1] M.C. Münnix, T. Shimada, R. Schäfer, F. Leyvraz, T.H. Seligman, T. Guhr and H.E. Stanley. 'Identifying States of a Financial Market' , Scientific Reports 2 : 644 (2012)

[2] Song W-M, Di Matteo T, Aste T. 'Hierarchical Information Clustering by Means of Topologically Embedded Graphs'. PLoS ONE 7(3), (2012)

 

 

 

Stefan Thurner (Vienna, Austria)

 

“Systemic risk as a multiplex: three lessons from three of its layers”

 

[Abstract TBA]

 

 

 

Huijuan Wang (Delft, Netherlands)

 

“Spreading of economic crisis”

 

Does economic crisis spread like an epidemic?  on which types of economic networks? Can we model the spreading of economic crisis by the susceptable-infected-susceptable (SIS) or susceptable-infected-recovered (SIR) epidemic model?

In this talk, I would like to introduce a set of research questions and possible approaches about the epidemics on economic networks and collect inputs and comments from our audience.

 

 

 

Marco van der Leij (Amsterdam, Netherlands)

 

“The formation of a core-periphery network in over-the-counter markets”

 

Recent evidence suggests that financial networks exhibit a core-periphery network structure. This paper aims at giving an explanation for the existence of such a structure by using the tools of network formation theory. Focusing on intermediation benefits, we find that a core-periphery network cannot be unilaterally stable when agents are homogenous. A core-periphery network structure can be explained if we allow for heterogeneity among agents.

 

 

Iman van Lelyveld (Basel, Switzerland)

 

“A Network Perspective on Regulatory Data”

 

Financial sector regulators and supervisors base their supervisory approach on the concept of the legal entity in their jurisdiction. Firms, however, are generally not bound by location or precise legal structure. Moreover, firms can only report their own exposures – not those of the market as a whole. Our understanding of the risks firms pose, both individually and as a group, is therefore limited.

Fortunately the collection of data – a necessary first step – has gained momentum since the 2007-2009 crisis. This opens up the possibility to improve our understanding of the risk profile of individual institutions but also of the system as a whole.

In this talk I will discuss current gaps, how these gaps are being tackled and what we could expect network methods to contribute.

 

 

 

Fernando Vega-Redondo (Florence, Italy)

 

Globalization in social networks”

 

I propose a stylised dynamic model of "globalization," understood as the process by which even agents who are geographically far apart come to interact, thus being able to overcome what would otherwise be a fast saturation of local opportunities. One of the main insights of the model is that, in order for the social network to turn global, the economy needs to display a degree of "cohesion" that is neither too high (for then global opportunities simply do not arise) nor too low (then the meeting mechanism displays too little structure for the process to take off). Our model of the phenomenon admits an interpretation at different scales, from the micro level (say, at the level of an organization) to a macro perspective (e.g. at the level of countries). Focusing on the latter, I will provide systematic empirical evidence that, at the world level, countries that are more globalized indeed perform better, i.e. they grow faster. This adds a novel network perspective to economic growth that enriches the received approach to the phenomenon.

 

 

 

Paolo Zeppini (Eindhoven, Netherlands)

 

“Innovation diffusion in networks: the microeconomics of percolation”

 

We implement a diffusion model for an innovative product in a market with a structure of social relationships. Diffusion is described with a percolation approach in the price space. Percolation shows a phase transition from a diffusion to a no-diffusion regime. This has strong implications for market demand and pricing. Small-worlds are often mentioned as being efficient in spreading innovation due to shorts cuts leading to short average path length. We show that diffusion, if defined as a percolation process in line with microeconomic theory, actually benefits from low clustering rather than low average path length. Network connectivity ``spreading'' is the most important factor for diffusion size. Hence, social structures with low clustering ("individualistic society") are most beneficial for innovation to spread.



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