In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 6 ( 2022-6-3), p. e0269483-
Abstract:
The feature ranking method of machine learning is applied to investigate the feature ranking and network properties of 21 world stock indices. The feature ranking is the probability of influence of each index on the target. The feature ranking matrix is determined by using the returns of indices on a certain day to predict the price returns of the next day using Random Forest and Gradient Boosting. We find that the North American indices influence others significantly during the global financial crisis, while during the European sovereign debt crisis, the significant indices are American and European. The US stock indices dominate the world stock market in most periods. The indices of two Asian countries (India and China) influence remarkably in some periods, which occurred due to the unrest state of these markets. The networks based on feature ranking are constructed by assigning a threshold at the mean of the feature ranking matrix. The global reaching centrality of the threshold network is found to increase significantly during the global financial crisis. Finally, we determine Shannon entropy from the probabilities of influence of indices on the target. The sharp drops of entropy are observed during big crises, which are due to the dominance of a few indices in these periods that can be used as a measure of the overall distribution of influences. Through this technique, we identify the indices that are influential in comparison to others, especially during crises, which can be useful to study the contagions of the global stock market.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0269483
DOI:
10.1371/journal.pone.0269483.g001
DOI:
10.1371/journal.pone.0269483.g002
DOI:
10.1371/journal.pone.0269483.g003
DOI:
10.1371/journal.pone.0269483.g004
DOI:
10.1371/journal.pone.0269483.g005
DOI:
10.1371/journal.pone.0269483.s001
DOI:
10.1371/journal.pone.0269483.s002
DOI:
10.1371/journal.pone.0269483.r001
DOI:
10.1371/journal.pone.0269483.r002
DOI:
10.1371/journal.pone.0269483.r003
DOI:
10.1371/journal.pone.0269483.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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