Network topology of economic sectors
A lot of studies dealing with stock network analysis, where each individual stock is represented by a univariate time series of its closing price, have been published. In these studies, the similarity of two different stocks is quantified using a Pearson correlation coefficient on the logarithmic pr...
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| Published in | Journal of statistical mechanics Vol. 2016; no. 9; pp. 93401 - 93420 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
IOP Publishing and SISSA
09.09.2016
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| Online Access | Get full text |
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| Summary: | A lot of studies dealing with stock network analysis, where each individual stock is represented by a univariate time series of its closing price, have been published. In these studies, the similarity of two different stocks is quantified using a Pearson correlation coefficient on the logarithmic price returns. In this paper, we generalize the notion of similarity between univariate time series into multivariate time series which might be of different dimensions. This allows us to deal with economic sector network analysis, where the similarity between economic sectors is defined using Escoufier's vector correlation RV. To the best of our knowledge, there is no study dealing with this notion of economic sector similarity. Two examples of data from the New York stock exchange will be presented and discussed, and some important results will be highlighted. |
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| Bibliography: | JSTAT_006P_0416 |
| ISSN: | 1742-5468 1742-5468 |
| DOI: | 10.1088/1742-5468/2016/09/093401 |