Extracting meaningful information from financial data
Апстракт
A method for extracting information carrying eigenvalues of the correlation matrix is presented based on the topological transformation of the manifold defined by the data matrix itself. The transformation, performed with the use of the minimum spanning tree and the barycentric transformation, linearizes the topological manifold and the singular value decomposition is performed on the fnal data matrix corresponding to the linearized hypersurface. It is shown that the results of this procedure are superior to the results of the random matrix theory as applied to the financial data. The method may be used independently or in conjunction with the random matrix theory. Other possible uses of the method are mentioned. (C) 2000 Elsevier Science B.V. All rights reserved.
Кључне речи:
financial markets data / random matrices / minimum spanning tree / barycentric transformationИзвор:
Physica A: Statistical Mechanics and Its Applications, 2000, 287, 3-4, 383-395Напомена:
- 229th WE Heraeus Seminar on Economics Dynamics from the Physics Point of View, Apr 27-30, 2000, Bad Honnef, Germany
DOI: 10.1016/S0378-4371(00)00377-0
ISSN: 0378-4371
WoS: 000165587600005
Scopus: 2-s2.0-0034501778
Колекције
Институција/група
VinčaTY - JOUR AU - Rajković, Milan PY - 2000 UR - https://vinar.vin.bg.ac.rs/handle/123456789/6297 AB - A method for extracting information carrying eigenvalues of the correlation matrix is presented based on the topological transformation of the manifold defined by the data matrix itself. The transformation, performed with the use of the minimum spanning tree and the barycentric transformation, linearizes the topological manifold and the singular value decomposition is performed on the fnal data matrix corresponding to the linearized hypersurface. It is shown that the results of this procedure are superior to the results of the random matrix theory as applied to the financial data. The method may be used independently or in conjunction with the random matrix theory. Other possible uses of the method are mentioned. (C) 2000 Elsevier Science B.V. All rights reserved. T2 - Physica A: Statistical Mechanics and Its Applications T1 - Extracting meaningful information from financial data VL - 287 IS - 3-4 SP - 383 EP - 395 DO - 10.1016/S0378-4371(00)00377-0 ER -
@article{ author = "Rajković, Milan", year = "2000", abstract = "A method for extracting information carrying eigenvalues of the correlation matrix is presented based on the topological transformation of the manifold defined by the data matrix itself. The transformation, performed with the use of the minimum spanning tree and the barycentric transformation, linearizes the topological manifold and the singular value decomposition is performed on the fnal data matrix corresponding to the linearized hypersurface. It is shown that the results of this procedure are superior to the results of the random matrix theory as applied to the financial data. The method may be used independently or in conjunction with the random matrix theory. Other possible uses of the method are mentioned. (C) 2000 Elsevier Science B.V. All rights reserved.", journal = "Physica A: Statistical Mechanics and Its Applications", title = "Extracting meaningful information from financial data", volume = "287", number = "3-4", pages = "383-395", doi = "10.1016/S0378-4371(00)00377-0" }
Rajković, M.. (2000). Extracting meaningful information from financial data. in Physica A: Statistical Mechanics and Its Applications, 287(3-4), 383-395. https://doi.org/10.1016/S0378-4371(00)00377-0
Rajković M. Extracting meaningful information from financial data. in Physica A: Statistical Mechanics and Its Applications. 2000;287(3-4):383-395. doi:10.1016/S0378-4371(00)00377-0 .
Rajković, Milan, "Extracting meaningful information from financial data" in Physica A: Statistical Mechanics and Its Applications, 287, no. 3-4 (2000):383-395, https://doi.org/10.1016/S0378-4371(00)00377-0 . .