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.

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Percolation and jamming phenomena are investigated for random sequential deposition of rectangular needles on d = 2 square lattices. Associated thresholds p are found to be a constant 0.62 ± 0.01 for all sizes. In addition the ratio of jamming thresholds for respectively square blocks and needles is also found to be a constant 0.79 ± 0.01. These constants exhibit some universal connexion in the geometry of jamming and percolation for both anisotropic shapes (needles versus square lattices) and isotropic shapes (square blocks on square lattices). A universal empirical law is proposed for all three thresholds as a function of a.

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We investigate sets of financial nonredundant and nonsynchronously recorded time series. The sets are composed by a number of stock market indices located all over the world in five continents. By properly selecting the time horizon of returns and by using a reference currency we find a meaningful taxonomy. The detection of such a taxonomy proves that interpretable information can be stored in a set of nonsynchronously recorded time series.