Structure and dynamics of financial networks by feature ranking method.

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 whic

SUBMITTER: Rakib MI 

PROVIDER: S-EPMC8413283 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature