Dynamics in two networks based on stocks of the US stock market

TL;DR

This study analyzes the evolving relationships between US stocks from 2003 to 2012 using correlation and Transfer Entropy to understand network dynamics during normal periods and crises, and to identify stocks likely to propagate volatility.

Contribution

It introduces a dual-measure network approach combining correlation and Transfer Entropy to study stock interactions and crisis spreading in financial markets.

Findings

Networks change significantly during crises.

Transfer Entropy reveals asymmetric influence among stocks.

Identifies stocks likely to spread financial volatility.

Abstract

We follow the main stocks belonging to the New York Stock Exchange and to Nasdaq from 2003 to 2012, through years of normality and of crisis, and study the dynamics of networks built on two measures expressing relations between those stocks: correlation, which is symmetric and measures how similar two stocks behave, and Transfer Entropy, which is non-symmetric and measures the influence of the time series of one stock onto another in terms of the information that the time series of one stock transmits to the time series of another stock. The two measures are used in the creation of two networks that evolve in time, revealing how the relations between stocks and industrial sectors changed in times of crisis. The two networks are also used in conjunction with a dynamic model of the spreading of volatility in order to detect which are the stocks that are most likely to spread crises, according to the model. This information may be used in the building of policies aiming to reduce the effect of financial crises.