Search

Financial markets have undergone a deep reorganization during the last 20 y. A mixture of technological innovation and regulatory constraints has promoted the diffusion of market fragmentation and high-frequency trading. The new stock market has changed the traditional ecology of market participants and market professionals, and financial markets have evolved into complex sociotechnical institutions characterized by a great heterogeneity in the time scales of market members’ interactions that cover more than eight orders of magnitude. We analyze three different datasets for two highly studied market venues recorded in 2004 to 2006, 2010 to 2011, and 2018. Using methods of complex network theory, we show that transactions between specific couples of market members are systematically and persistently overexpressed or underexpressed. Contemporary stock markets are therefore networked markets where liquidity provision of market members has statistically detectable preferences or avoidances with respect to some market members over time with a degree of persistence that can cover several months. We show a sizable increase in both the number and persistence of networked relationships between market members in most recent years and how technological and regulatory innovations affect the networked nature of the markets. Our study also shows that the portfolio of strategic trading decisions of high-frequency traders has evolved over the years, adding to the liquidity provision other market activities that consume market liquidity.

© 2020, Cambridge University PressThe book ‘The Rise and Fall of Business Firms: A Stochastic Framework on Innovation, Creative Destruction and Growth’ by S.V. Buldyrev, F. Pammolli, M. Riccaboni a...

We develop an algorithm that is fast and scalable in the detection of a nested partition extracted from a dendrogram that is obtained from hierarchical clustering of a multivariate series. Our algorithm provides a -value for each clade observed in the hierarchical tree. The -value is obtained by computing many bootstrap replicas of the dissimilarity matrix and by performing a statistical test on each difference between the dissimilarity associated with a given clade and the dissimilarity of the clade of its parent node. We prove the efficacy of our algorithm with a set of benchmarks generated by a hierarchically nested factor model. We compare results obtained by our algorithm with those of Pvclust. Pvclust is a widely-used algorithm pursuing a global approach originally developed in the context of phylogenetic studies. In our numerical experiments, we focus on the role of multiple hypothesis test correction and the robustness of the algorithms to inaccuracies and errors of datasets. We verify that our algorithm is much faster than Pvclust algorithm and has a better scalability both in the number of elements and in the number of records of the investigated multivariate set. We also apply our algorithm to two empirical datasets, one related to a biological complex system and the other related to financial time-series. We prove that the clusters detected by our methodology are meaningful with respect to some consensus partitioning of the two datasets.

We perform a large scale analysis of a list of fintech terms in (i) news and blogs in English language and (ii) professional descriptions of companies operating in many countries. The occurrence and co-occurrence of fintech terms and locutions shows a progressive evolution of the list of fintech terms in a compact and coherent set of terms used worldwide to describe fintech business activities. By using methods of complex networks that are specifically designed to deal with heterogeneous systems, our analysis of a large set of professional descriptions of companies shows that companies having fintech terms in their description present over-expressions of specific attributes of country, municipality, and economic sector. By using the approach of statistically validated networks, we detect geographical and economic over-expressions of a set of companies related to the multi-industry, geographically and economically distributed fintech movement., 11 pages, 4 figures, 4 tables

In this chapter we discuss Aoki’s work on the description of clusters of economic agents acting in a market. Specifically, we briefly discuss his work on the Ewens distribution and its application in a model of stock market with heterogeneous agents. We then review recent empirical analyses on the heterogeneity of financial market participants and make a working hypothesis for an empirical study on the distribution of the number of clusters of market participants in a real stock market monitored with a resolution down to the shadowed identity of market participants.

The role of networks in the emergence, diffusion and evolution of technological innovations has attracted much theoretical and empirical attention. Yet, much of the work has explored the role of undirected and homogeneous networks. In real cases, many networks are directed. The flow of information, benefits or observations is directed from one node towards another node. Real networks are also heterogeneous, for example, few nodes have a high degree while many others have a low degree. In this article, we report on the results of an evolutionary agent-based model in which a group of agents, in our case firms, collectively search a complex (rugged) technological landscape and observe each other's solutions with different frequencies through different observation networks. Two sets of networks are considered in the analysis. The first set comprises undirected networks that vary in terms of efficiency while the second set comprises directed networks that vary in terms of homogeneity of node degree. We find that collective innovation (described as an exploration of a technology landscape) improves average fitness over independent search because information about better innovations can diffuse faster through the network at an early stage. Moreover, we find that efficient networks outperform inefficient ones in the first stages of the search, but, given their slower rate of information diffusion, some of the inefficient networks require additional independent search which can guarantee a marginally better performance at later stages. We also find that degree homogeneous and undirected networks achieve better fitness on average than heterogeneous and directed ones. Finally, we apply our model to a simplified scenario of innovation diffusion in a financial system by using an empirical network obtained from the Thomson Reuters DealScan database. For this illustrative case study, our results are presented and discussed from the perspective of system-level technological innovation. We conclude by discussing implications for technological innovation and possible extensions.

We find a nonlinear dependence between an indicator of the degree of multiscaling of log-price time series of a stock and the average correlation of the stock with respect to the other stocks traded in the same market. This result is a robust stylized fact holding for different financial markets. We investigate this result conditional on the stocks' capitalization and on the kurtosis of stocks' log-returns in order to search for possible confounding effects. We show that a linear dependence with the logarithm of the capitalization and the logarithm of kurtosis does not explain the observed stylized fact, which we interpret as being originated from a deeper relationship., Comment: 19 pages, 8 figures, 9 tables

We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that is able to detect a hierarchical structure of the trading profiles of heterogeneous individual investors. We verify that the detected hierarchical structure is highly overlapping with the cluster structure obtained with the approach of statistically validated networks when an appropriate threshold of the hierarchical trees is used. We also show that the combination of the correlation based method and of the statistically validated method provides a way to expand the information about the clusters of investors with similar trading profiles in a robust and reliable way., 25 pages, 8 figures

This paper aims at analyzing the unexpected influence of Financial economics on Physics. The rise of Econophysics, a fundamentally new approach in finance, suggests that the influence between the two disciplines becomes less unilateral than in the past. Methodological debates emerging in Econophysics led physicists to acknowledge that dealing with financial complex systems contributed to a wider modelling of their field. The approach of econophysicists suggests that physicists might try to conceptualize physical phenomena by integrating elements they faced with in Financial economics, and more generally in Economics. Surprisingly, many of econophysicists’ argumentations have some methodological similarities with practices used in Financial economics. This paper analyzes the influence of Financial economics on Physics by discussing three examples: (i) out of equilibrium processes, (ii) signal detection and information filtering, and (iii) the role of information in complex systems. It investigates and illustrates what are the methodological changes generated by Econophysics that explain this new influence of finance on Physics. This paper sheds new light on the way finance and economics can improve physics modelling. With this purpose, this article is going one step further in the dialogue between econophysics and economics. Indeed, by investigating the reciprocal influence between the two fields, this methodological paper identifies some areas for a better cross-fertilisation between the fields.

We describe two different bootstrap methods applied to the detection of a minimum spanning tree obtained from a set of multivariate variables. We show that two different bootstrap procedures provide partly distinct information that can be highly informative about the investigated complex system. Our case study, based on the investigation of daily returns of a portfolio of stocks traded in the US equity markets, shows the degree of robustness and completeness of the information extracted with popular information filtering methods such as the minimum spanning tree and the planar maximally filtered graph. The first method performs a "row bootstrap" whereas the second method performs a "pair bootstrap". We show that the parallel use of the two methods is suggested especially for complex systems presenting both a nested hierarchical organization together with the presence of global feedback channels., 17 pages, 7 figures

According to the leading models in modern finance, the presence of intraday lead-lag relationships between financial assets is negligible in efficient markets. With the advance of technology, however, markets have become more sophisticated. To determine whether this has resulted in an improved market efficiency, we investigate whether statistically significant lagged correlation relationships exist in financial markets. We introduce a numerical method to statistically validate links in correlation-based networks, and employ our method to study lagged correlation networks of equity returns in financial markets. Crucially, our statistical validation of lead-lag relationships accounts for multiple hypothesis testing over all stock pairs. In an analysis of intraday transaction data from the periods 2002--2003 and 2011--2012, we find a striking growth in the networks as we increase the frequency with which we sample returns. We compute how the number of validated links and the magnitude of correlations change with increasing sampling frequency, and compare the results between the two data sets. Finally, we compare topological properties of the directed correlation-based networks from the two periods using the in-degree and out-degree distributions and an analysis of three-node motifs. Our analysis suggests a growth in both the efficiency and instability of financial markets over the past decade.

This paper aims at analyzing the unexpected influence of Financial economics on Physics. The rise of Econophysics, a fundamentally new approach in finance, suggests that the influence between the two disciplines becomes less unilateral than in the past. Methodological debates emerging in Econophysics led physicists to acknowledge that dealing with financial complex systems contributed to a wider modelling of their field. The approach of econophysicists suggests that physicists might try to conceptualize physical phenomena by integrating elements they faced with in Financial economics, and more generally in Economics. Surprisingly, many of econophysicists’ argumentations have some methodological similarities with practices used in Financial economics. This paper analyzes the influence of Financial economics on Physics by discussing three examples: (i) out of equilibrium processes, (ii) signal detection and information filtering, and (iii) the role of information in complex systems. It investigates and illustrates what are the methodological changes generated by Econophysics that explain this new influence of finance on Physics. This paper sheds new light on the way finance and economics can improve physics modelling. With this purpose, this article is going one step further in the dialogue between econophysics and economics. Indeed, by investigating the reciprocal influence between the two fields, this methodological paper identifies some areas for a better cross-fertilisation between the fields.

The cornerstone of modern finance is the efficient market hypothesis. Under this hypothesis all information available about a financial asset is immediately incorporated into its price dynamics by fully rational investors. In contrast to this hypothesis many studies have pointed out behavioral biases in investors. Recently it has become possible to access databases that track the trading decisions of investors. Studies of such databases have shown that investors acting in a financial market are highly heterogeneous among them, and that heterogeneity is a common characteristic of many financial markets. The article describes an empirical study of the daily trading decisions of all Finnish investors investing Nokia stock over a time period of 15 years. The investigation is performed by adapting and using methods and tools in network science. By investigating daily trading decisions, and by constructing the time-evolution of statistically validated networks of investors, clusters of investors—and their time evolution— which are characterized by similar trading profiles are detected. These clusters are performing distinct trading decisions on time scales ranging from several months to twelve years. These empirical observations show the presence of an ecology of groups of investors characterized by different attributes and by various investment styles over many years. Some of the detected clusters present a persistent over-expression of specific investor categories. The study shows that the logarithm of the ratio of pairs of statistically validated trading decisions is different for different values of the market volatility. These findings suggest that an ecology of investors is present in financial markets and that groups of traders are always competing, adopting, using and eventually discarding new investment strategies. This adaptation process is observed over a multiplicity of time scales, and is compatible with several conclusions of behavioral finance and with the assumptions of the so-called adaptive market hypothesis.

In this study, we assess the dynamic evolution of short-term correlation, long-term cointe-gration and Error Correction Model (hereafter referred to as ECM)-based long-term Granger causality between each pair of US, UK, and Eurozone stock markets from 1980 to 2015 using the rolling-window technique. A comparative analysis of pairwise dynamic integration and causality of stock markets, measured in common and domestic currency terms, is conducted to evaluate comprehensively how exchange rate fluctuations affect the time-varying integration among the S&P 500, FTSE 100 and EURO STOXX 50 indices. The results obtained show that the dynamic correlation, cointegration and ECM-based long-run Granger causality vary significantly over the whole sample period. The degree of dynamic correlation and cointegration between pairs of stock markets rises in periods of high volatility and uncertainty, especially under the influence of economic, financial and political shocks. Meanwhile, we observe the weaker and decreasing correlation and cointegration among the three developed stock markets during the recovery periods. Interestingly, the most persistent and significant cointegration among the three developed stock markets exists during the 200709 global financial crisis. Finally, the exchange rate fluctuations, also influence the dynamic integration and causality between all pairs of stock indices, with that influence increasing under the local currency terms. Our results suggest that the potential for diversifying risk by investing in the US, UK and Eurozone stock markets is limited during the periods of economic, financial and political shocks.

Understanding the relation between planned and realized flight trajectories and the determinants of flight deviations is of great importance in air traffic management. In this paper we perform an in depth investigation of the statistical properties of planned and realized air traffic on the German airspace during a 28 day periods, corresponding to an AIRAC cycle. We find that realized trajectories are on average shorter than planned ones and this effect is stronger during night-time than daytime. Flights are more frequently deviated close to the departure airport and at a relatively large angle to destination. Moreover, the probability of a deviation is higher in low traffic phases. All these evidences indicate that deviations are mostly used by controllers to give directs to flights when traffic conditions allow it. Finally we introduce a new metric, termed difork, which is able to characterize navigation points according to the likelihood that a deviation occurs there. Difork allows to identify in a statistically rigorous way navigation point pairs where deviations are more (less) frequent than expected under a null hypothesis of randomness that takes into account the heterogeneity of the navigation points. Such pairs can therefore be seen as sources of flexibility (stability) of controllers traffic management while conjugating safety and efficiency., Comment: 16 pages; 11 figures; 3 tables

We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are statistically reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power law; after the order is finished, it reverts to a level of about 0.5-0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.

The application of CNT to air traffic management has seen significant growth in recent years. This is partly because air traffic can be seen as the superposition of different networks, including the networks of airports, sectors and navigation points. Moreover each of these networks can be seen as a multiplex – for example, by associating each layer with a different airline. The study of the topology of these networks is important for several reasons related to understanding, monitoring, controlling, and optimising the air traffic system. The topological properties of air traffic networks are useful: (i) for studying how the air traffic has changed in recent years; (ii) for identifying the more central and vulnerable nodes of air traffic networks; and (iii) for identifying communities of nodes that can be naturally associated with units of ATC (e.g., sectors or airspaces). The topology affects the dynamics that can take place on a network, such as the mobility of passengers and goods, the propagation of disturbances (e.g., delays, congestion, and so on), and the propagation of epidemics. Despite this large range of applications of CNT to ATM, we believe that the interaction between these two fields is only beginning and new and interesting contributions will be published in forthcoming years.

We discuss the cultural background that was shared by some of the first econophysicists when they started to work on economic and financial problems with methods and tools of statistical physics. In particular we discuss about the role of stylized facts and statistical physical laws in economics and statistical physics respectively. As an example of the problems and potentials associated with the interaction of different communities of scholars dealing with problems observed in economic and financial systems we briefly discuss the development and the perspectives of the use of tools and concepts of networks in econophysics, economics and finance.

Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of network connectivity and link strength, while at the same time each frequency-specific network is characterized by a different signature pattern of sleep-stage stratification, reflecting a remarkable flexibility in response to change in physiologic state. These new aspects of neural plasticity demonstrate that in addition to dominant brain waves, the network of brain wave interactions is a previously unrecognized hallmark of physiologic state and function.

The degree of correlation among stock returns aects the possibility to diversify the risk of investment

This special issue of Quantitative Finance collects eight papers on the relation between interlinkages and systemic risk. The papers cover several types of interlinkages and follow different approaches, from agent-based modelling to empirical investigation of large and sometimes confidential data. The special issue collects some of the contributions presented at the international workshop‘Interlinkages and systemic risk ’ , which took place in Ancona (Italy) on 4 – 5 July 2013. The workshop, organized within the research project‘. New tools in the credit network modeling with agents ’ heterogeneity ’ funded by the Institute for New Economic Thinking, was attended by a balanced mix of scholars from academia and economists from central banks and regulatory authorities.

Versión aceptada obtenida del archivo digital en línea WestminsterResearch de la Universidad de Westminster. Complexity science is the multidisciplinary study of complex systems. Its marked network orientation lends itself well to transport contexts. Key features of complexity science are introduced and defined, with a specific focus on the application to air traffic management. An overview of complex network theory is presented, with examples of its corresponding metrics and multiple scales. Complexity science is starting to make important contributions to performance assessment and system design: selected, applied air traffic management case studies are explored. The important contexts of uncertainty, resilience and emergent behaviour are discussed, with future research priorities summarised.

We propose here two new recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three relevant data sets, and we compare their performance with several recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow to attain an improvement of performances of up to 20\% with respect to existing non-parametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we studied how an increasing presence of random links in the network affects the recommendation scores, and we found that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets., 9 pages, 6 figures, 2 tables

We present an agent based model of the Air Traffic Management socio-technical complex system that aims at modeling the interactions between aircrafts and air traffic controllers at a tactical level. The core of the model is given by the conflict detection and resolution module and by the directs module. Directs are flight shortcuts that are given by air controllers to speed up the passage of an aircraft within a certain airspace and therefore to facilitate airline operations. Conflicts resolution between flight trajectories can arise during the en-route phase of each flight due to both not detailed flight trajectory planning or unforeseen events that perturb the planned flight plan. Our model performs a local conflict detection and resolution procedure. Once a flight trajectory has been made conflict-free, the model searches for possible improvements of the system efficiency by issuing directs. We give an example of model calibration based on real data. We then provide an illustration of the capability of our model in generating scenario simulations able to give insights about the air traffic management system. We show that the calibrated model is able to reproduce the existence of a geographical localization of air traffic controllers' operations. Finally, we use the model to investigate the relationship between directs and conflict resolutions (i) in the presence of perfect forecast ability of controllers, and (ii) in the presence of some degree of uncertainty in flight trajectory forecast., Comment: 18 pages, 2 tables, 12 figures