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Quantifying influence in financial markets via partial correlation network inference

Quantifying influence in financial markets via partial correlation network inference
Quantifying influence in financial markets via partial correlation network inference
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.
IEEE
Millington, Tristan
53030837-7d43-4389-b676-1dcdabeff250
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Millington, Tristan
53030837-7d43-4389-b676-1dcdabeff250
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Millington, Tristan and Niranjan, Mahesan (2019) Quantifying influence in financial markets via partial correlation network inference. In 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE.. (doi:10.1109/ISPA.2019.8868437).

Record type: Conference or Workshop Item (Paper)

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.

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e-pub ahead of print date: 17 October 0009
Published date: 2019

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Local EPrints ID: 435217
URI: http://eprints.soton.ac.uk/id/eprint/435217
PURE UUID: f2b7a6cc-47b9-473f-a75f-824918677f6e

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Date deposited: 28 Oct 2019 17:30
Last modified: 14 Sep 2021 20:38

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Author: Tristan Millington
Author: Mahesan Niranjan

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