10 Correlations, Hierarchies, Networks and Clustering
269
24. Buraschi, A., Porchia, P.: Dynamic networks and asset pricing. In: AFA 2013 San Diego
Meetings Paper (2012)
25. Campajola, C., Lillo, F., Tantari, D.: Unveiling the relation between herding and liquidity
with trader lead-lag networks (2019). arXiv:1909.10807, 2019
26. Carlsson, G., MÊmoli, F.: Characterization, stability and convergence of hierarchical clustering methods. J. Mach. Learn. Res. 11, 1425–1470 (2010)
27. Chen, H., Cohen, L., Lou, D.: Industry window dressing. Rev. Financial Stud. 29(12), 3354–
3393 (2016)
28. Cimini, G., Squartini, T., Garlaschelli, D., Gabrielli, A.: Systemic risk analysis on reconstructed economic and financial networks. Sci. Rep. 5, (2015)
29. Colla, P., Mele, A.: Information linkages and correlated trading. Rev. Financial Stud. 23(1),
203–246 (2009)
30. Rama Cont. Empirical properties of asset returns: stylized facts and statistical issues (2001)
31. Cordoba, A., Castillejo, C., García-Machado, J.J., Lara, A.M.: Anticipating abrupt changes
in complex networks: Significant falls in the price of a stock index. In: Nonlinear Systems,
vol. 1, pp. 317–338. Springer, Berlin (2018)
32. Curme, C., Tumminello, M., Mantegna, R.N., Stanley, H.E., Kenett, D.Y.: Emergence of
statistically validated financial intraday lead-lag relationships. Quantitative Finance 15(8),
1375–1386 (2015)
33. Curme, C., Tumminello, M., Mantegna, R.N., Stanley, H.E., Kenett, D.Y.: How lead-lag
correlations affect the intraday pattern of collective stock dynamics. Office Financial Res.
Working Paper, 15 (2015)
34. van Lidth de Jeude, J., Aste, T., Caldarelli, G.: The multilayer structure of corporate networks
(2019). arXiv:1901.07411
35. de Prado, M.L.: Advances in Financial Machine Learning. Wiley, New York (2018)
36. Denev, A.: Probabilistic Graphical Models: A New Way of Thinking in Financial Modelling.
Risk Books (2015)
37. Donnat, P., Marti, G., Very, P.: Toward a generic representation of random variables for
machine learning. Pattern Recognit. Lett. 70, 24–31 (2016)
38. Dose, C., Cincotti, S.: Clustering of financial time series with application to index and
enhanced index tracking portfolio. Phys. A: Stat. Mech. Appl. 355(1), 145–151 (2005)
39. Dro˙ zd˙ z, S., Grümmer, F., Górski, A.Z., Ruf, F., Speth, J.: Dynamics of competition between
collectivity and noise in the stock market. Phys. A: Stat. Mech. Appl. 287(3), 440–449 (2000)
40. Durante, F., Foscolo, E., Pappadà, R., Wang, H.: A Portfolio Diversification Strategy Via Tail
Dependence Measures (2015)
41. Durante, F., Pappada, R.: Cluster analysis of time series via kendall distribution. In: Strengthening Links Between Data Analysis and Soft Computing, pp. 209–216. Springer, Berlin
(2015)
42. Durante, F., Pappadà, R., Torelli, N.: Clustering of financial time series in risky scenarios.
Adv. Data Anal. Classif. 8(4), 359–376 (2014)
43. Elshendy, M., Colladon, A.F.: Big data analysis of economic news: Hints to forecast macroeconomic indicators. Int. J. Eng. Bus. Manag. 9, 1847979017720040 (2017)
44. Elton, E.J., Gruber, M.J.: Improved forecasting through the design of homogeneous groups.
J. Bus. 44(4), 432–450 (1971)
45. Epps, T.W.: Comovements in stock prices in the very short run. J. Am. Stat. Assoc. 74(366a),
291–298 (1979)
46. Fan, J., Cohen, K., Shekhtman, L.M., Liu, S., Meng, J., Louzoun, Y., Havlin, S.: Topology of
products similarity network for market forecasting. Appl. Netw. Sci. 4(1), 1–15 (2019)
47. Fiedor, P.: Information-theoretic approach to lead-lag effect on financial markets. Eur. Phys.
J. B 87(8), 1–9 (2014)
48. Fiedor, P.: Networks in financial markets based on the mutual information rate. Phys. Rev E
89(5), 052801 (2014)
49. Forss, T., Sarlin, P.: News-Sentiment Networks as a Risk Indicator (2017). arXiv:1706.05812,
2017
269
24. Buraschi, A., Porchia, P.: Dynamic networks and asset pricing. In: AFA 2013 San Diego
Meetings Paper (2012)
25. Campajola, C., Lillo, F., Tantari, D.: Unveiling the relation between herding and liquidity
with trader lead-lag networks (2019). arXiv:1909.10807, 2019
26. Carlsson, G., MÊmoli, F.: Characterization, stability and convergence of hierarchical clustering methods. J. Mach. Learn. Res. 11, 1425–1470 (2010)
27. Chen, H., Cohen, L., Lou, D.: Industry window dressing. Rev. Financial Stud. 29(12), 3354–
3393 (2016)
28. Cimini, G., Squartini, T., Garlaschelli, D., Gabrielli, A.: Systemic risk analysis on reconstructed economic and financial networks. Sci. Rep. 5, (2015)
29. Colla, P., Mele, A.: Information linkages and correlated trading. Rev. Financial Stud. 23(1),
203–246 (2009)
30. Rama Cont. Empirical properties of asset returns: stylized facts and statistical issues (2001)
31. Cordoba, A., Castillejo, C., García-Machado, J.J., Lara, A.M.: Anticipating abrupt changes
in complex networks: Significant falls in the price of a stock index. In: Nonlinear Systems,
vol. 1, pp. 317–338. Springer, Berlin (2018)
32. Curme, C., Tumminello, M., Mantegna, R.N., Stanley, H.E., Kenett, D.Y.: Emergence of
statistically validated financial intraday lead-lag relationships. Quantitative Finance 15(8),
1375–1386 (2015)
33. Curme, C., Tumminello, M., Mantegna, R.N., Stanley, H.E., Kenett, D.Y.: How lead-lag
correlations affect the intraday pattern of collective stock dynamics. Office Financial Res.
Working Paper, 15 (2015)
34. van Lidth de Jeude, J., Aste, T., Caldarelli, G.: The multilayer structure of corporate networks
(2019). arXiv:1901.07411
35. de Prado, M.L.: Advances in Financial Machine Learning. Wiley, New York (2018)
36. Denev, A.: Probabilistic Graphical Models: A New Way of Thinking in Financial Modelling.
Risk Books (2015)
37. Donnat, P., Marti, G., Very, P.: Toward a generic representation of random variables for
machine learning. Pattern Recognit. Lett. 70, 24–31 (2016)
38. Dose, C., Cincotti, S.: Clustering of financial time series with application to index and
enhanced index tracking portfolio. Phys. A: Stat. Mech. Appl. 355(1), 145–151 (2005)
39. Dro˙ zd˙ z, S., Grümmer, F., Górski, A.Z., Ruf, F., Speth, J.: Dynamics of competition between
collectivity and noise in the stock market. Phys. A: Stat. Mech. Appl. 287(3), 440–449 (2000)
40. Durante, F., Foscolo, E., Pappadà, R., Wang, H.: A Portfolio Diversification Strategy Via Tail
Dependence Measures (2015)
41. Durante, F., Pappada, R.: Cluster analysis of time series via kendall distribution. In: Strengthening Links Between Data Analysis and Soft Computing, pp. 209–216. Springer, Berlin
(2015)
42. Durante, F., Pappadà, R., Torelli, N.: Clustering of financial time series in risky scenarios.
Adv. Data Anal. Classif. 8(4), 359–376 (2014)
43. Elshendy, M., Colladon, A.F.: Big data analysis of economic news: Hints to forecast macroeconomic indicators. Int. J. Eng. Bus. Manag. 9, 1847979017720040 (2017)
44. Elton, E.J., Gruber, M.J.: Improved forecasting through the design of homogeneous groups.
J. Bus. 44(4), 432–450 (1971)
45. Epps, T.W.: Comovements in stock prices in the very short run. J. Am. Stat. Assoc. 74(366a),
291–298 (1979)
46. Fan, J., Cohen, K., Shekhtman, L.M., Liu, S., Meng, J., Louzoun, Y., Havlin, S.: Topology of
products similarity network for market forecasting. Appl. Netw. Sci. 4(1), 1–15 (2019)
47. Fiedor, P.: Information-theoretic approach to lead-lag effect on financial markets. Eur. Phys.
J. B 87(8), 1–9 (2014)
48. Fiedor, P.: Networks in financial markets based on the mutual information rate. Phys. Rev E
89(5), 052801 (2014)
49. Forss, T., Sarlin, P.: News-Sentiment Networks as a Risk Indicator (2017). arXiv:1706.05812,
2017