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Feature ranking and network analysis of global financial indices

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  • Mahmudul Islam Rakib
  • Md Javed Hossain
  • Ashadun Nobi

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.

Suggested Citation

  • Mahmudul Islam Rakib & Md Javed Hossain & Ashadun Nobi, 2022. "Feature ranking and network analysis of global financial indices," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-16, June.
    • Handle: RePEc:plo:pone00:0269483
    DOI: 10.1371/journal.pone.0269483
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    References listed on IDEAS

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    1. Pollet, Joshua M. & Wilson, Mungo, 2010. "Average correlation and stock market returns," Journal of Financial Economics, Elsevier, vol. 96(3), pages 364-380, June.
    2. repec:plo:pone00:0033799 is not listed on IDEAS
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    4. Pawe{l} Fiedor, 2014. "Mutual Information Rate-Based Networks in Financial Markets," Papers 1401.2548, arXiv.org.
    5. Ashadun Nobi & Sungmin Lee & Doo Hwan Kim & Jae Woo Lee, 2014. "Correlation and Network Topologies in Global and Local Stock Indices," Papers 1402.1552, arXiv.org.
    6. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.
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    Cited by:

    1. Siudak, Dariusz & Świetlik, Agata, 2025. "Unsupervised learning modeling of the impact of Black Swan events on financial network reconfiguration: New insights from the COVID-19 outbreak and the Russia-Ukraine war," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 658(C).
    2. Mahmudul Islam Rakib & Md Jahidul Alam & Nahid Akter & Kamrul Hasan Tuhin & Ashadun Nobi, 2024. "Change in hierarchy of the financial networks: A study on firms of an emerging market in Bangladesh," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-16, May.

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