It takes just a little talk with me to know that I'm a fan of the financial market and many subjects related to economics.
Given all these data, we conclude that the present relations in economic news actually reflect the data from our commercial relations. Maybe it was not different, but it is a way to show how everything is connected and in fact, given that markets are efficient (there is much discussion here and I tend to disagree with the theory), we have that trade relations will be reflected in some way in the behavior of market players and consequently,will be reflected upon pricing of financial assets.
Social scientists have never understood why some countries are more corrupt than others. But the first study that links corruption with wealth could help change that.
Paulus and Kristoufek use this data to search for find clusters of countries that share similar properties using a new generation of cluster-searching algorithms. And they say that the 134 countries they study fall neatly into four groups which are clearly correlated with the wealth of the nations within them.
The method that makes this possible is known as the average linkage clustering approach. It begins by assuming that each country represents a cluster in itself and then looking for the nearest neighbour in the ranking. This pair then become a new cluster and this cluster placed back into the list as a single entity. The process is then repeated until it produces a single cluster of all the countries.
Introduction Last weekend the G20 Conference was hosted in Brisbane Australia. According to the G20 official website the objectives of G20 are: The Group of Twenty (G20) is the premier forum for its members’ international economic cooperation and decision-making
ABSTRACT: After 2000, a growing number of foreign firms list in the United States through reverse merger, a non-IPO listing technique that requires less information disclosure. Are the US regulations rigorous enough to deter the listing attempts of weak foreign firms?
Using a social network analysis, I find that the firms are assisted by Western professionals to help them circumvent the US regulations, and they commit fraud and benefit from fast stock sales after listing. Further, I find that the social network of the linked directors facilitates the spread of their misconduct. During the wrongdoers’ listings, the investors in these firms lost at least $811 million. However, the penalties charged to the wrongdoers only accounted for 4.19% of this loss. I also find that the US-listed Chinese firms have a lower average Tobin’s q compared to the China-listed firms, in contrast to the prior research’s findings. These findings contradict the concurrent research that uses the reverse mergers’ financial data, which proves to be unreliable
We study a two states opinion formation model driven by PageRank node influence and report an extensive numerical study on how PageRank affects collective opinion formations in large-scale empirical directed networks. In our model the opinion of a node can be updated by the sum of its neighbor nodes' opinions weighted by the node influence of the neighbor nodes at each step.
First, we observe that all networks reach steady state opinion after a certain relaxation time. This time scale is decreasing with the heterogeneity of node influence in the networks. Second, we find that our model shows consensus and non-consensus behavior in steady state depending on types of networks: Web graph, citation network of physics articles, and LiveJournal social network show non-consensus behavior while Wikipedia article network shows consensus behavior. Third, we find that a more heterogeneous influence distribution leads to a more uniform opinion state in the cases of Web graph, Wikipedia, and Livejournal. However, the opposite behavior is observed in the citation network. Finally we identify that a small number of influential nodes can impose their own opinion on significant fraction of other nodes in all considered networks.
Network optimality has been described in genes, proteins and human communicative networks. In the latter, optimality leads to the efficient transmission of information with a minimum number of connections. Whilst studies show that differences in centrality exist in animal networks with central individuals having higher fitness, network efficiency has never been studied in animal groups. Here we studied 78 groups of primates (24 species). We found that group size and neocortex ratio were correlated with network efficiency. Centralisation (whether several individuals are central in the group) and modularity (how a group is clustered) had opposing effects on network efficiency, showing that tolerant species have more efficient networks. Such network properties affecting individual fitness could be shaped by natural selection. Our results are in accordance with the social brain and cultural intelligence hypotheses, which suggest that the importance of network efficiency and information flow through social learning relates to cognitive abilities.
In the second part of my “how to quickly visualize networks directly from R” series, I’ll discuss how to use R and the “rgexf” package to create network plots in Gephi. Gephi is a great network visualization tool that allows real-time network visualization and exploration, including network data spatializing, filtering, calculation of network properties, and clustering.
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