Rakib Mahmudul Islam, Hossain Md Javed, Nobi Ashadun
Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh.
PLoS One. 2022 Jun 3;17(6):e0269483. doi: 10.1371/journal.pone.0269483. eCollection 2022.
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.
应用机器学习的特征排序方法来研究21个世界股票指数的特征排序和网络特性。特征排序是每个指数对目标的影响概率。通过使用某一天指数的收益率，利用随机森林和梯度提升来预测次日的价格收益率，从而确定特征排序矩阵。我们发现，在全球金融危机期间，北美指数对其他指数有显著影响，而在欧洲主权债务危机期间，有显著影响的指数是美国和欧洲的指数。在大多数时期，美国股票指数主导着世界股票市场。两个亚洲国家（印度和中国）的指数在某些时期有显著影响，这是由于这些市场的动荡状态所致。基于特征排序的网络是通过在特征排序矩阵的均值处设置一个阈值来构建的。发现阈值网络的全局可达中心性在全球金融危机期间显著增加。最后，我们根据指数对目标的影响概率确定香农熵。在重大危机期间观察到熵的急剧下降，这是由于在这些时期少数指数占主导地位，这可以用作影响总体分布的一种度量。通过这种技术，我们识别出与其他指数相比有影响力的指数，特别是在危机期间，这对于研究全球股票市场的传染情况可能是有用的。
