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at SSRN 3446653, (2019)
52. Giada, L., Marsili, M.: Data clustering and noise undressing of correlation matrices. Phys.
Rev. E 63(6), 061101 (2001)
53. Giada, L., Marsili, M.: Algorithms of maximum likelihood data clustering with applications.
Phys. A: Stat. Mech. Appl. 315(3), 650–664 (2002)
54. Goh, Y.K., Hasim, H.M., Antonopoulos, C.G.: Inference of financial networks using the
normalised mutual information rate. PloS One 13(2), e0192160 (2018)
55. Guo, L., Peng, L., Tao, Y., Tu, J.: Media Network Based Investors’ Attention: A Powerful
Predictor of Market Premium (2018)
56. Guo, X., Hu, Z., Tianhai, T.: Development of stock correlation networks using mutual information and financial big data. PloS One 13(4), e0195941 (2018)
57. Harmon, D., Stacey, B., Bar-Yam, Y., Bar-Yam, Y.: Networks of Economic Market Interdependence and Systemic Risk (2010). arXiv:1011.3707
58. Hartman, D., Hlinka, J.: Nonlinearity in Stock Networks (2018). arXiv:1804.10264
59. Heckens, A.J., Krause, S.M., Guhr, T.: Uncovering the Dynamics of Correlation Structures
Relative to the Collective Market Motion (2020). arXiv:2004.12336
60. Hoberg, G., Phillips, G.: The stock market, product uniqueness, and comovement of peer
firms. SSRN eLibrary (2012)
61. Hoberg, G., Phillips, G.: Text-Based Industry Momentum (2017)
62. Huang, F., Gao, P., Wang, Y.: Comparison of Prim and Kruskal on Shanghai and Shenzhen 300
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63. Huang, W.-Q., Zhuang, X.-T., Yao, S., Uryasev, S.: A financial network perspective of financial
institutions’ systemic risk contributions. Phys. A: Stat. Mech. Appl. 456, 183–196 (2016)
64. Hüttner, A., Mai, J.-F. Mineo, S.: Portfolio selection based on graphs: Does it align with
markowitz-optimal portfolios? Dependence Modeling (2018)
65. Jain, P., Jain, S.: Can machine learning-based portfolios outperform traditional risk-based
portfolios? the need to account for covariance misspecification. Risks 7(3), 74 (2019)
66. Johnson, N.F., McDonald, M., Suleman, O., Williams, S., Howison, S.: What shakes the FX
tree? understanding currency dominance, dependence, and dynamics (keynote address). In:
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67. Jung, W.-S., Kwon, O., Wang, F., Kaizoji, T., Moon, H.-T., Stanley, H.E.: Group dynamics of
the Japanese market. Phys. A: Stat. Mec. Appl. 387(2), 537–542 (2008)
68. Kakushadze, Z., Yu, W.: Statistical Industry Classification (2016)
69. Kaya, H.: Eccentricity in asset management. J. Netw. Theory Finance 1(1), 1–32 (2014)
70. Kenett, D.Y., Preis, T., Gur-Gershgoren, G., Ben-Jacob, E.: Dependency network and node
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71. Kenett, D.Y., Shapira, Y., Madi, A., Bransburg-Zabary, S., Gur-Gershgoren, G., Ben-Jacob,
E.: Dynamics of stock market correlations. AUCO Czech Econ. Rev. 4(3), 330–341 (2010)
72. Kenett, D.Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R.N., Ben-Jacob,
E.: Dominating clasp of the financial sector revealed by partial correlation analysis of the
stock market. PloS One 5(12), e15032 (2010)
73. Kim, H.J., Kim, I.M., Lee, Y., Kahng, B.: Scale-free network in stock markets. J.-Korean
Phys. Soc. 40, 1105–1108 (2002)
74. King, B.F.: Market and industry factors in stock price behavior. J. Bus. 39(1), 139–190 (1966)
75. Kocheturov, A., Batsyn, M., Pardalos, P.M.: Dynamics of cluster structures in a financial
market network. Phys. A: Stat. Mech. Appl. 413, 523–533 (2014)
76. Krüger, P., Landier, A., Thesmar, D.: Categorization bias in the stock market. Available SSRN
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G. Marti et al.
50. Gao, Y.-C., Zeng, Y., Cai, S.-M.: Influence network in the Chinese stock market. J. Stat.
Mech.: Theory Experiment 2015(3), P03017 (2015)
51. Gava, J., Lefebvre, W., Turc, J.: Beyond carry and momentum in government bonds. Available
at SSRN 3446653, (2019)
52. Giada, L., Marsili, M.: Data clustering and noise undressing of correlation matrices. Phys.
Rev. E 63(6), 061101 (2001)
53. Giada, L., Marsili, M.: Algorithms of maximum likelihood data clustering with applications.
Phys. A: Stat. Mech. Appl. 315(3), 650–664 (2002)
54. Goh, Y.K., Hasim, H.M., Antonopoulos, C.G.: Inference of financial networks using the
normalised mutual information rate. PloS One 13(2), e0192160 (2018)
55. Guo, L., Peng, L., Tao, Y., Tu, J.: Media Network Based Investors’ Attention: A Powerful
Predictor of Market Premium (2018)
56. Guo, X., Hu, Z., Tianhai, T.: Development of stock correlation networks using mutual information and financial big data. PloS One 13(4), e0195941 (2018)
57. Harmon, D., Stacey, B., Bar-Yam, Y., Bar-Yam, Y.: Networks of Economic Market Interdependence and Systemic Risk (2010). arXiv:1011.3707
58. Hartman, D., Hlinka, J.: Nonlinearity in Stock Networks (2018). arXiv:1804.10264
59. Heckens, A.J., Krause, S.M., Guhr, T.: Uncovering the Dynamics of Correlation Structures
Relative to the Collective Market Motion (2020). arXiv:2004.12336
60. Hoberg, G., Phillips, G.: The stock market, product uniqueness, and comovement of peer
firms. SSRN eLibrary (2012)
61. Hoberg, G., Phillips, G.: Text-Based Industry Momentum (2017)
62. Huang, F., Gao, P., Wang, Y.: Comparison of Prim and Kruskal on Shanghai and Shenzhen 300
Index hierarchical structure tree. In: International Conference on Web Information Systems
and Mining. WISM 2009, pp. 237–241. IEEE (2009)
63. Huang, W.-Q., Zhuang, X.-T., Yao, S., Uryasev, S.: A financial network perspective of financial
institutions’ systemic risk contributions. Phys. A: Stat. Mech. Appl. 456, 183–196 (2016)
64. Hüttner, A., Mai, J.-F. Mineo, S.: Portfolio selection based on graphs: Does it align with
markowitz-optimal portfolios? Dependence Modeling (2018)
65. Jain, P., Jain, S.: Can machine learning-based portfolios outperform traditional risk-based
portfolios? the need to account for covariance misspecification. Risks 7(3), 74 (2019)
66. Johnson, N.F., McDonald, M., Suleman, O., Williams, S., Howison, S.: What shakes the FX
tree? understanding currency dominance, dependence, and dynamics (keynote address). In:
SPIE Third International Symposium on Fluctuations and Noise, pp. 86–99. International
Society for Optics and Photonics (2005)
67. Jung, W.-S., Kwon, O., Wang, F., Kaizoji, T., Moon, H.-T., Stanley, H.E.: Group dynamics of
the Japanese market. Phys. A: Stat. Mec. Appl. 387(2), 537–542 (2008)
68. Kakushadze, Z., Yu, W.: Statistical Industry Classification (2016)
69. Kaya, H.: Eccentricity in asset management. J. Netw. Theory Finance 1(1), 1–32 (2014)
70. Kenett, D.Y., Preis, T., Gur-Gershgoren, G., Ben-Jacob, E.: Dependency network and node
influence: application to the study of financial markets. Int. J. Bifurcat. Chaos 22(07), 1250181
(2012)
71. Kenett, D.Y., Shapira, Y., Madi, A., Bransburg-Zabary, S., Gur-Gershgoren, G., Ben-Jacob,
E.: Dynamics of stock market correlations. AUCO Czech Econ. Rev. 4(3), 330–341 (2010)
72. Kenett, D.Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R.N., Ben-Jacob,
E.: Dominating clasp of the financial sector revealed by partial correlation analysis of the
stock market. PloS One 5(12), e15032 (2010)
73. Kim, H.J., Kim, I.M., Lee, Y., Kahng, B.: Scale-free network in stock markets. J.-Korean
Phys. Soc. 40, 1105–1108 (2002)
74. King, B.F.: Market and industry factors in stock price behavior. J. Bus. 39(1), 139–190 (1966)
75. Kocheturov, A., Batsyn, M., Pardalos, P.M.: Dynamics of cluster structures in a financial
market network. Phys. A: Stat. Mech. Appl. 413, 523–533 (2014)
76. Krüger, P., Landier, A., Thesmar, D.: Categorization bias in the stock market. Available SSRN
2034204, (2012)