Paper Info
Title
Quantifying Influence in Financial Markets via Partial Correlation Network Inference
Abstract
Network based methods to study the financial markets have been popular due to their ability to represent a complex system in a simple manner. We are interested to see if we can measure the influence between various companies by using partial correlation. Calculating partial correlation can be challenging with financial data so to rectify this we use the SPACE estimator. With this estimator we infer networks from daily S&P500 returns, study how these networks vary over time and draw parallels to the macroeconomic events that may explain the changes. We see that companies tend to have more connections to those in the same sector and some sectors tend to be more self contained than others. By measuring the centrality of the various sectors in the network we find that the financial sector is regarded as the most important for the majority of the dataset. Finally we show there is mild negative correlation between the centrality of a company and its out-of-sample risk.
Year
DOI
Venue
2019
10.1109/ISPA.2019.8868437
2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)
Keywords
Field
DocType
financial markets,partial correlation network inference,network based methods,complex system,financial data,SPACE estimator,financial sector,S&P500 returns
Complex system,Econometrics,Parallels,Partial correlation,Computer science,Inference,Centrality,Financial market,Estimator,Distributed computing
Conference
ISSN
ISBN
Citations 
1845-5921
978-1-7281-3141-2
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Tristan Millington100.34
Mahesan Niranjan2775120.43