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Zunino et al. use an innovative statistical tool in the financial literature: the complexity-entropy causality plane. This Cartesian representation establish an efficiency ranking of different markets and distinguish different bond market dynamics. Moreover, the authors conclude that the classification derived from the complexity-entropy causality plane is consistent with the qualifications assigned by major rating companies to the sovereign instruments. A similar study developed by Bariviera et al. explore the relationship between credit ratings and informational efficiency of a sample of corporate bonds of US oil and energy companies using also the complexity–entropy causality plane. They find that this classification agrees with the credit ratings assigned by Moody's.

Zunino et al. use an innovative statistical tool in the financial literature: the complexity-entropy causality plane. This Cartesian representation establish an efficiency ranking of different markets and distinguish different bond market dynamics. Moreover, the authors conclude that the classification derived from the complexity-entropy causality plane is consistent with the qualifications assigned by major rating companies to the sovereign instruments. A similar study developed by Bariviera et al. explore the relationship between credit ratings and informational efficiency of a sample of corporate bonds of US oil and energy companies using also the complexity–entropy causality plane. They find that this classification agrees with the credit ratings assigned by Moody's.

祖尼诺等。在金融文献中使用创新的统计工具: 复杂性-熵因果关系平面。这种笛卡尔式表示建立了不同市场的效率排名，并区分了不同的债券市场动态。此外，从复杂熵因果关系平面导出的分类结果与主权证券评级公司对主权证券的评级结果一致。由 Bariviera 等人开发的一个类似的研究。以美国石油和能源公司债券为样本，运用复杂熵因果关系平面，探讨了信用评级与信息效率的关系。他们发现，这一分类与穆迪给予的信用评级相一致。

祖尼诺等。在金融文献中使用创新的统计工具: 复杂性-熵因果关系平面。这种笛卡尔式表示建立了不同市场的效率排名，并区分了不同的债券市场动态。此外，从复杂熵因果关系平面导出的分类结果与主权证券评级公司对主权证券的评级结果一致。由 Bariviera 等人开发的一个类似的研究。以美国石油和能源公司债券为样本，运用复杂熵因果关系平面，探讨了信用评级与信息效率的关系。他们发现，这一分类与穆迪给予的信用评级相一致。

Another good example is random matrix theory, which can be used to identify the noise in financial correlation matrices.  One paper has argued that this technique can improve the performance of portfolios, e.g., in applied in portfolio optimization.

Another good example is random matrix theory, which can be used to identify the noise in financial correlation matrices.  One paper has argued that this technique can improve the performance of portfolios, e.g., in applied in portfolio optimization.

Another good example is [[random matrix theory]], which can be used to identify the noise in financial correlation matrices.  One paper has argued that this technique can improve the performance of portfolios, e.g., in applied in [[Modern portfolio theory|portfolio optimization]].<ref>{{cite journal |author1=Vasiliki Plerou |author2=Parameswaran Gopikrishnan |author3=Bernd Rosenow |author4=Luis Amaral |author5=Thomas Guhr |author6=H. Eugene Stanley |title=Random matrix approach to cross correlations in financial data |journal=Physical Review E|volume= 65|page= 066126 |year=2002 |doi=10.1103/PhysRevE.65.066126 |pmid=12188802 | issue = 6|arxiv = cond-mat/0108023 |bibcode = 2002PhRvE..65f6126P |s2cid=2753508 }}</ref>

Another good example is [[random matrix theory]], which can be used to identify the noise in financial correlation matrices.  One paper has argued that this technique can improve the performance of portfolios, e.g., in applied in [[Modern portfolio theory|portfolio optimization]].<ref>{{cite journal |author1=Vasiliki Plerou |author2=Parameswaran Gopikrishnan |author3=Bernd Rosenow |author4=Luis Amaral |author5=Thomas Guhr |author6=H. Eugene Stanley |title=Random matrix approach to cross correlations in financial data |journal=Physical Review E|volume= 65|page= 066126 |year=2002 |doi=10.1103/PhysRevE.65.066126 |pmid=12188802 | issue = 6|arxiv = cond-mat/0108023 |bibcode = 2002PhRvE..65f6126P |s2cid=2753508 }}</ref>

另一个很好的例子是随机矩阵理论，它可以用来识别金融相关矩阵中的噪声。一篇论文认为，这种技术可以改善投资组合的性能，例如，应用于投资组合优化。

另一个很好的例子是随机矩阵理论，它可以用来识别金融相关矩阵中的噪声。一篇论文认为，这种技术可以改善投资组合的性能，例如，应用于投资组合优化。