Networks of equities in financial markets

Abstract.We review the recent approach of correlation based networks of financial equities. We investigate portfolio of stocks at different time horizons, financial indices and volatility time series and we show that meaningful economic information can be extracted from noise dressed correlation matrices. We show that the method can be used to falsify widespread market models by directly comparing the topological properties of networks of real and artificial markets.

[1]  Edwin J. Elton,et al.  Improved Forecasting Through the Design of Homogeneous Groups , 1971 .

[2]  B. Swart,et al.  Quantitative Finance , 2006, Metals and Energy Finance.

[3]  Journal of business , 2022 .

[4]  N. L. Johnson,et al.  Multivariate Analysis , 1958, Nature.

[5]  K. Kaski,et al.  Dynamic asset trees and portfolio analysis , 2002, cond-mat/0208131.

[6]  A. Stuart,et al.  Portfolio Selection: Efficient Diversification of Investments. , 1960 .

[7]  M. Mézard,et al.  Spin Glass Theory and Beyond , 1987 .

[8]  K Kaski,et al.  Time-dependent cross-correlations between different stock returns: a directed network of influence. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  R. Mantegna.  Hierarchical structure in financial markets , 1998, cond-mat/9802256.

[10]  R Pastor-Satorras,et al.  Dynamical and correlation properties of the internet. , 2001, Physical review letters.

[11]  K. Kaski,et al.  Asset Trees and Asset Graphs in Financial Markets , 2003 .

[12]  K. Kaski,et al.  Dynamics of market correlations: taxonomy and portfolio analysis. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Thorsten Rheinländer.  Risk Management: Value at Risk and Beyond , 2003 .

[14]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[15]  V. Plerou,et al.  Universal and Nonuniversal Properties of Cross Correlations in Financial Time Series , 1999, cond-mat/9902283.

[16]  T. Andersen.  THE ECONOMETRICS OF FINANCIAL MARKETS , 1998, Econometric Theory.

[17]  M. Marsili.  Dissecting financial markets: sectors and states , 2002, cond-mat/0207156.

[18]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[19]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[20]  J. Gower.  Some distance properties of latent root and vector methods used in multivariate analysis , 1966 .

[21]  K. Pearson.  Biometrika , 1902, The American Naturalist.

[22]  G. Caldarelli,et al.  The fractal properties of Internet , 2000, cond-mat/0009178.

[23]  O. Maurice Joy,et al.  Comovement of International Equity Markets: A Taxonomic Approach , 1976, Journal of Financial and Quantitative Analysis.

[24]  Fabrizio Lillo,et al.  Degree stability of a minimum spanning tree of price return and volatility , 2003 .

[25]  R. Engle,et al.  Do Bulls and Bears Move Across Borders? International Transmission of Stock Returns and Volatility , 1994 .

[26]  Journal of Econometrics , 2022 .

[27]  Nicolas Vandewalle,et al.  Non-random topology of stock markets , 2001 .

[28]  F. Lillo,et al.  High-frequency cross-correlation in a set of stocks , 2000 .

[29]  K. Kaski,et al.  Dynamic asset trees and Black Monday , 2002, cond-mat/0212037.

[30]  Paul H. Malatesta.  インタビュー "Journal of Financial and Quantitative Analysis" 編集長Paul Malatesta教授 , 2005 .

[31]  D. Saad.  Europhysics Letters , 1997 .

[32]  S H Strogatz,et al.  Random graph models of social networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[33]  A. Stuart,et al.  Portfolio Selection: Efficient Diversification of Investments , 1959 .

[34]  Rosario N. Mantegna,et al.  Book Review: An Introduction to Econophysics, Correlations, and Complexity in Finance, N. Rosario, H. Mantegna, and H. E. Stanley, Cambridge University Press, Cambridge, 2000. , 2000 .

[35]  F. Lillo,et al.  Topology of correlation-based minimal spanning trees in real and model markets. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  J. Bouchaud,et al.  Noise Dressing of Financial Correlation Matrices , 1998, cond-mat/9810255.

[37]  R. N. Mantegna,et al.  Identification of clusters of companies in stock indices via Potts super-paramagnetic transitions , 2000 .

[38]  G. Toulouse,et al.  Ultrametricity for physicists , 1986 .

[39]  E. Elton.  Modern portfolio theory and investment analysis , 1981 .

[40]  M Marsili,et al.  Data clustering and noise undressing of correlation matrices. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Kathryn Fraughnaugh,et al.  Introduction to graph theory , 1973, Mathematical Gazette.