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Jordi

This paper does something similar using the topic model methodology to
build a corporate network of common news topics among different European
companies.

Best, G. Creamer

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2196572

Impact of Dynamic Corporate News Networks on Assets Return and Volatility


*Abstract: *
This paper analyzes the relationship between assets return, volatility and
the centrality indicators of a corporate news network. We build a sequence
of daily corporate news network for the period 2005-2011 using companies of
the STOXX 50 index as nodes; the weights of the edges are the sum of the
number of news items with the same topic by every pair of companies
identified by the topic model methodology. The STOXX 50 includes the top 50
European companies by level of capitalization.

We conducted two studies to evaluate the impact of corporate news network
in the assets return dynamic. In the first study we conducted a
longitudinal network analysis using the stochastic actor oriented model
with daily return and news for March 2009. We found that there was a 0.55
correlation between the rate of change of the news network and the STOXX 50
index. In the second study we extended our longitudinal analysis of
networks using a sequence of daily corporate news networks for the period
2005-2011. We performed the Granger causality test and the Brownian
distance covariance test of independence among several measures of
centrality, return and volatility. We found that the average eigenvector
centrality of the corporate news networks at different points of time has
an impact on return and volatility of the STOXX 50 index. Likewise, return
and volatility of the STOXX 50 index also has an effect on average
eigenvector centrality. These results are more significant during the most
important period of the recent financial crisis (January2008-March 2009).
The same results hold when we examine this relationship at the level of
individual companies. So, we observe that there is a dynamic process that
affects and is affected by return, volatility, and centrality. The
causality tests suggest it is possible to improve the prediction of return
and volatility by extracting and analyzing a network based on the common
topics of news stories.


On Fri, Jun 13, 2014 at 9:55 AM, Jordi Comas <[log in to unmask]> wrote:

> ***** To join INSNA, visit http://www.insna.org *****
> Hello-
>
> SO a friend and colleague is starting to think about doing some network
> studies of intellectuals, their groups, and ideas or concepts.
>
> FOr example, in a period, there are intellectuals who have relations due
> to co-membership in journals, schools, universities, and so on.  That is a
> first bimodal network of groups by persons.  Second, he wants to look at a
> topic like baking (not his topic) and how it emerges in one journal/person
> cluster and perhaps "moves' over time to others.  Like a diffusion study.
>
> A question in my head is if the the people, groups and concepts can be one
> big bimodal network?  Or, can we have "trimodal" networks of three kinds of
> objects?
>
> Are there models of how to study this kind of question that is at the
> intersection of social structure and meaning-making/cultural dynamics.
>
> THANK YOU!
>
> Jordi
>
> --
> *Jordi Comas*
>
>
>
> *"There is nothing so practical as a good theory."  Kurt Lewin *Assistant
> Professor
> School of Management
> Bucknell University
> Taylor 112
> 570 577 3161
>
> Research and Writing Blog: Nets We Weave
> <http://netsweweave.wordpress.com>
>
>
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-- 

Germán (Herman) Creamer, PhD
Associate Professor
Stevens Institute of Technology
T 201.216.8986
F 201.216.5385
http://ssrn.com/author=487822
<http://hq.ssrn.com/GroupProcesses/RedirectClick.cfm?partid=487822&corid=649&runid=9602&url=http://ssrn.com/author=487822>

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