Abstract
Network models for stock market attract a great attention in last decades. Different measures of similarity between stocks attributes are largely used. In general, different measures of similarity can generate different network structures. For instance, Fechner and Pearson correlations networks can have different minimum spanning trees, market graphs, maximum cliques, and maximum independent sets. At the same time it is known that Fechner and Pearson correlations are connected by a monotonic transformation for bivariate Gaussian distributions. This connection can be generalized to bivariate elliptically contoured distributions. In this case it can be shown that network structures are connected too. This fact can be used for data mining in market network. In the present paper we study the connection between Fechner and Pearson correlations for the real market data.
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Acknowledgements
This work is partly supported by LATNA laboratory, Russian Federation Government grant 11.G34.31.0057, and RFHR grant 15-32-01052.
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Latyshev, A., Koldanov, P. (2016). Investigation of Connections Between Pearson and Fechner Correlations in Market Network: Experimental Study. In: Kalyagin, V., Koldanov, P., Pardalos, P. (eds) Models, Algorithms and Technologies for Network Analysis. NET 2014. Springer Proceedings in Mathematics & Statistics, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-319-29608-1_12
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DOI: https://doi.org/10.1007/978-3-319-29608-1_12
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