Structure and dynamics of financial networks by feature ranking method

Ashadun Nobi,Jae Woo Lee,Mahmudul Islam Rakib

doi:10.1038/s41598-021-97100-1

Ashadun Nobi, Jae Woo Lee + Show 1 more

Open Access

https://doi.org/10.1038/s41598-021-97100-1

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Abstract

Much research has been done on time series of financial market in last two decades using linear and non-linear correlation of the returns of stocks. In this paper, we design a method of network reconstruction for the financial market by using the insights from machine learning tool. To do so, we analyze the time series of financial indices of S&P 500 around some financial crises from 1998 to 2012 by using feature ranking approach where we use the returns of stocks in a certain day to predict the feature ranks of the next day. We use two different feature ranking approaches—Random Forest and Gradient Boosting—to rank the importance of each node for predicting the returns of each other node, which produces the feature ranking matrix. To construct threshold network, we assign a threshold which is equal to mean of the feature ranking matrix. The dynamics of network topology in threshold networks constructed by new approach can identify the financial crises covered by the monitored time series. We observe that the most influential companies during global financial crisis were in the sector of energy and financial services while during European debt crisis, the companies are in the communication services. The Shannon entropy is calculated from the feature ranking which is seen to increase over time before market crash. The rise of entropy implies the influences of stocks to each other are becoming equal, can be used as a precursor of market crash. The technique of feature ranking can be an alternative way to infer more accurate network structure for financial market than existing methods, can be used for the development of the market.

Highlights

We can improve any machine learning model by discarding the insignificant features. We can use this whole process of modern complex network reconstruction method to identify the backend structure of stock markets
We assume the structure of the studied network to be hidden in a black box and try to reconstruct it using the time series provided by all the stocks individually
We propose our method of reconstructing networks for stock markets from discrete time series by applying the Feature Ranking[19], which has never been used with the financial time series data before

Summary

Introduction

We can improve any machine learning model by discarding the insignificant features We can use this whole process of modern complex network reconstruction method to identify the backend structure of stock markets. We propose our method of reconstructing networks for stock markets from discrete time series by applying the Feature Ranking[19], which has never been used with the financial time series data before. We use the monitored discrete time series data to measure how much the target is influenced by each feature and compute the feature ranking . Some features have such strong impact on the target that we can safely assume that those features have some connection edges with the target n ode[15]. Feature ranking approach infer more accurate network structure for financial market than existing methods

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