Journal of Network Theory in Finance
ISSN:
2055-7795 (print)
2055-7809 (online)
Editor-in-chief: Ron Berndsen
Volume 2, Number 1 (March 2016)
Editor's Letter
Before discussing the current issue, I would like to make two announcements. First, I am pleased to announce that Tiziana Di Matteo has joined me as Co-Editorin- Chief of The Journal of Network Theory in Finance. Tiziana is a professor of econophysics in the department of mathematics at King's College London. As well as being one of the world's leading figures in her field, Tiziana has been a supportive and engaged member of the editorial board since the journal was launched in March 2015. As Co-Editor-in-Chief, she brings a wealth of research experience and a depth of knowledge to the journal derived from authoring more than ninety papers and giving invited and keynote talks at major international conferences around the world.
Second, I would like to invite all of the journal's readers to our third conference: Financial Risk and Network Theory 2016, on September 13-14, 2016 at the University of Cambridge. The conference will once again bring together academics, regulators and industry practitioners, providing a unique overview of cutting edge research and presenting invited talks on recent industry applications of network theory in finance. More details on the conference and a call for papers can be found on the conference website. I hope to see many of you there, and look forward to another very successful edition.
I am pleased to note that the the three papers in this issue deal with different research topics showing a widening of the journal's scope. The first, "Close communications: hedge funds, brokers and the emergence of a consensus trade" by Jan Simon, Yuval Millo, Neil Kellard and Ofer Engel, examines the network of communication practices among hedge fund managers. It is the first paper to come from a social network analysis (SNA) tradition that has appeared in the journal. The paper examines how social networks affect financial markets and finds cohesive communication clusters among similarly minded managers that enforce consensus trades and reduce the use of information from sources outside the trusted connections. The results of the paper highlight the fact that financial systems are influenced by human bias in information processing - bias that has measurable effects on risks and prices.
The issue's second paper, "Credit risk spillover between financials and sovereigns in the euro area, 2007-15" by Olivier Vergote, proposes a method based on Granger causality to measure the level of contagion between financial institutions and sovereigns. Monitoring contagion should be of interest both to investors who analyze the risk of financial institutions and sovereigns, and to policymakers from the perspective of financial stability.
Our third paper, "A network-based method for visual identification of systemic risks" by Samantha Cook, Alan Laubsch and myself, introduces the topic of network visualization to the journal by proposing the use of a combination of data reduction techniques and overlays that allowdetection of large-scale patterns and outlier activity. Communication of risk and portfolio decisions among managers and to asset owners, investors and other stakeholders is an important part of risk management. The ability to convey complex risk themes in a simpler visual way should be valuable to many researchers and practitioners.
Kimmo Soramäki
Financial Network Analytics Ltd.
Papers in this issue
Close communications: hedge funds, brokers and the emergence of a consensus trade
This paper examines the network of communication practices among hedge fund managers.
Credit risk spillover between financials and sovereigns in the euro area, 2007–15
This paper proposes a method based on Granger causality to measure the level of contagion between financial institutions and sovereigns.
A network-based method for visual identification of systemic risks
This paper introduces the topic of network visualization to the journal by proposing the use of a combination of data reduction techniques and overlays that allow detection of large-scale patterns and outlier activity.