The competitiveness versus the wealth of a country

Politicians world-wide frequently promise a better life for their citizens. We find that the probability that a country will increase its {\it per capita} GDP ({\it gdp}) rank within a decade follows an exponential distribution with decay constant $\lambda = 0.12$. We use the Corruption Perceptions Index (CPI) and the Global Competitiveness Index (GCI) and find that the distribution of change in CPI (GCI) rank follows exponential functions with approximately the same exponent as $\lambda$, suggesting that the dynamics of {\it gdp}, CPI, and GCI may share the same origin. Using the GCI, we develop a new measure, which we call relative competitiveness, to evaluate an economy's competitiveness relative to its {\it gdp}. For all European and EU countries during the 2008-2011 economic downturn we find that the drop in {\it gdp} in more competitive countries relative to {\it gdp} was substantially smaller than in relatively less competitive countries, which is valuable information for policymakers.Comment: 11 pages, 7 figures, accepted for publication in Nature Scientific Report

How high frequency trading affects a market index

The relationship between a market index and its constituent stocks is complicated. While an index is a weighted average of its constituent stocks, when the investigated time scale is one day or longer the index has been found to have a stronger effect on the stocks than vice versa. We explore how this interaction changes in short time scales using high frequency data. Using a correlation-based analysis approach, we find that in short time scales stocks have a stronger influence on the index. These findings have implications for high frequency trading and suggest that the price of an index should be published on shorter time scales, as close as possible to those of the actual transaction time scale.We would like to thank Yoash Shapira, Idan Michaeli and Dustin Plotnick for all of their help. DYK and EBJ acknowledge support in part by the Tauber Family Foundation and the Maguy-Glass Chair in Physics of Complex Systems at Tel Aviv University. HES and DYK thank the support of the Office of Naval Research (ONR, Grant N00014-09-1-0380, Grant N00014-12-1-0548), Keck Foundation and the NSF (Grant CMMI 1125290) for support. This work was also supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC00285. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U.S. Government. (Tauber Family Foundation; Maguy-Glass Chair in Physics of Complex Systems at Tel Aviv University; N00014-09-1-0380 - Office of Naval Research (ONR); N00014-12-1-0548 - Office of Naval Research (ONR); Keck Foundation; CMMI 1125290 - NSF; D12PC00285 - Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC))Published versio

Community analysis of global financial markets

We analyze the daily returns of stock market indices and currencies of 56 countries over the period of 2002–2012. We build a network model consisting of two layers, one being the stock market indices and the other the foreign exchange markets. Synchronous and lagged correlations are used as measures of connectivity and causality among different parts of the global economic system for two different time intervals: non-crisis (2002–2006) and crisis (2007–2012) periods. We study community formations within the network to understand the influences and vulnerabilities of specific countries or groups of countries. We observe different behavior of the cross correlations and communities for crisis vs. non-crisis periods. For example, the overall correlation of stock markets increases during crisis while the overall correlation in the foreign exchange market and the correlation between stock and foreign exchange markets decrease, which leads to different community structures. We observe that the euro, while being central during the relatively calm period, loses its dominant role during crisis. Furthermore we discover that the troubled Eurozone countries, Portugal, Italy, Greece and Spain, form their own cluster during the crisis period.Published versio

Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics

10.1155/2019/2108423Complexity2019210842

Quantifying Wikipedia usage patterns before stock market moves

Financial crises result from a catastrophic combination of actions. Vast stock market datasets offer us a window into some of the actions that have led to these crises. Here, we investigate whether data generated through Internet usage contain traces of attempts to gather information before trading decisions were taken. We present evidence in line with the intriguing suggestion that data on changes in how often financially related Wikipedia pages were viewed may have contained early signs of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of decision making

Dynamics of Stock Market Correlations

We present a novel approach to the study the dynamics of stock market correlations. This is achieved through an innovative visualization tool that allows an investigation of the structure and dynamics of the market, through the study of correlations. This is based on the Stock Market Holography (SMH) method recently introduced. This qualitative measure is complemented by the use of the eigenvalue entropy measure, to quantify how the information in the market changes in time. Using this innovative approach, we analyzed data from the New York Stock Exchange (NYSE), and the Tel Aviv Stock Exchange (TASE), for daily trading data for the time period of 2000–2009. This paper covers these new concepts for the study of financial markets in terms of structure and information as reflected by the changes in correlations over time.Correlation, Stock Market Holography, eigenvalue entropy, sliding window

Evolvement of Uniformity and Volatility in the Stressed Global Financial Village

Background: In the current era of strong worldwide market couplings the global financial village became highly prone to systemic collapses, events that can rapidly sweep throughout the entire village. Methodology/Principal Findings: We present a new methodology to assess and quantify inter-market relations. The approach is based on the correlations between the market index, the index volatility, the market Index Cohesive Force and the meta-correlations (correlations between the intra-correlations.) We investigated the relations between six important world markets—U.S., U.K., Germany, Japan, China and India—from January 2000 until December 2010. We found that while the developed ‘‘western’’ markets (U.S., U.K., Germany) are highly correlated, the interdependencies between these markets and the developing ‘‘eastern’’ markets (India and China) are volatile and with noticeable maxima at times of global world events. The Japanese market switches ‘‘identity’’—it switches between periods of high meta-correlations with the ‘‘western’’ markets and periods when it behaves more similarly to the ‘‘eastern’’ markets. Conclusions/Significance: The methodological framework presented here provides a way to quantify the evolvement of interdependencies in the global market, evaluate a world financial network and quantify changes in the world inter market relations. Such changes can be used as precursors to the agitation of the global financial village. Hence, the new approach can help to develop a sensitive ‘‘financial seismograph’’ to detect early signs of global financial crises so they can be treated before they develop into worldwide event