scholarly journals Lending Diversification and Interconnectedness of the Syndicated Loan Market

2021 ◽  
Vol 8 ◽  
Author(s):  
Gabjin Oh ◽  
A-young Park
Keyword(s):  
Statistical Significance ◽  
Investment Decisions ◽  
Portfolio Diversification ◽  
Centrality Measures ◽  
Bank Performance ◽  
Network Centrality ◽  
Financial Variables ◽  
Loan Portfolios ◽  
Syndicated Loan Market ◽  
Syndicated Loan

We investigate the effects of syndicated loan network centrality on bank performance. Syndicated loan network centrality measures the similarity and influence of the other banks within a given banks network. The network centrality constructed by syndicated loans can allow banks to gather and transfer valuable information and can thus facilitate profit-making acquisition in loan investment decisions. We use a planar maximally filtered graph to construct an interbank network using syndicated loan portfolios at the industry level. We show that the syndicated loan portfolios of high-centrality banks exhibit a higher level of portfolio diversification than those of low-centrality banks. We also document that our composite centrality measure of the bank network showed statistical significance in terms of bank performance even after controlling for the financial variables of market size, loan allocation, total asset, and loan diversification. Our findings suggest that the performance of a bank in a syndicated loan hierarchy is related to its position in this hierarchy.

Liquidity shock management: Lessons from Australian banks

2016 ◽  
Vol 42 (4) ◽  
pp. 637-652 ◽  
Author(s):  
Christine Brown ◽  
Viet Do ◽  
Oscar Trevarthen
Keyword(s):  
Loan Portfolios ◽  
Market Disruption ◽  
International Banks ◽  
Syndicated Loan Market ◽  
Average Share ◽  
Syndicated Loan ◽  
Domestic Banks ◽  
Global Operations ◽  
Liquidity Shock ◽  
Revolving Credit

Prior to the 2007–2009 financial crisis, international banks had an average share of around 65% of the syndicated loan market in Australia. When the crisis hit, the resulting liquidity shock resulted in globally active international banks exiting the Australian market. With limited global operations, the major Australian banks were able to absorb and manage the liquidity shock. This resulted in domestic banks carrying a significantly greater proportion of revolving credit facilities in their syndicated loan portfolios after 2008. Domestic bank willingness and ability to deal with the market disruption and to hold a greater proportion of high liquidity risk revolvers are directly linked to the level of their transaction deposits. Their increased involvement in revolving facilities cannot be fully explained by the certification effect or flight-to-home effect. It is not demand driven and is robust to endogeneity tests.

Comparison and Application of Metrics That Define the Components Modularity in Complex Products

2008 ◽  
Author(s):  
Qi D. Van Eikema Hommes
Keyword(s):  
Social Network ◽  
Centrality Measures ◽  
Software Systems ◽  
Network Centrality ◽  
Good Pair ◽  
Centrality Metrics ◽  
The Social ◽  
Social Network Centrality ◽  
Sample Test

As the content and variety of technology increases in automobiles, the complexity of the system increases as well. Decomposing systems into modules is one of the ways to manage and reduce system complexity. This paper surveys and compares a number of state-of-art components modularity metrics, using 8 sample test systems. The metrics include Whitney Index (WI), Change Cost (CC), Singular value Modularity Index (SMI), Visibility-Dependency (VD) plot, and social network centrality measures (degree, distance, bridging). The investigation reveals that WI and CC form a good pair of metrics that can be used to assess component modularity of a system. The social network centrality metrics are useful in identifying areas of architecture improvements for a system. These metrics were further applied to two actual vehicle embedded software systems. The first system is going through an architecture transformation. The metrics from the old system revealed the need for the improvements. The second system was recently architected, and the metrics values showed the quality of the architecture as well as areas for further improvements.

scholarly journals Identifying key papers within a journal via network centrality measures

2016 ◽  
Vol 107 (3) ◽  
pp. 1005-1020 ◽  
Author(s):  
Saikou Y. Diallo ◽  
Christopher J. Lynch ◽  
Ross Gore ◽  
Jose J. Padilla
Keyword(s):  
Centrality Measures ◽  
Network Centrality

scholarly journals Machine Learning in Network Centrality Measures

2019 ◽  
Vol 51 (5) ◽  
pp. 1-32 ◽  
Author(s):  
Felipe Grando ◽  
Lisandro Z. Granville ◽  
Luis C. Lamb
Keyword(s):  
Machine Learning ◽  
Centrality Measures ◽  
Network Centrality

scholarly journals Predicting dengue importation into Europe, using machine learning and model-agnostic methods

2019 ◽  
Author(s):  
Donald Salami ◽  
Carla Alexandra Sousa ◽  
Maria do Rosário Oliveira Martins ◽  
César Capinha
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Transport Network ◽  
Air Transport ◽  
Health Concern ◽  
Centrality Measures ◽  
Network Centrality

ABSTRACTThe geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation.Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale.Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions.We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.

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