Financial markets are a typical example of complex systems where interactions between constituants lead to many remarkable features. Here we give empirical evidences , by making as few assumptions as possible, that the market microstructure capturing almost all of the available information in the data of stock markets does not involve higher order than pairwise interactions. We give an economic interpretation of this pairwise model. We show that it accurately recovers the empirical correlation coefficients thus the collective behaviors are quantitatively described by models that capture the observed pairwise correlations but no higher-order interactions. Furthermore, we show that an order-disorder transition occurs as predicted by the pairwise model. Last, we make the link with the graph theoretic description of stock markets recovering the non-random and scale free topology, shrinking length during crashes and meaningful clustering features as expected.
Financial markets are a typical example of complex systems where interactions between constituents lead to many remarkable features. Here we give empirical evidence , by making as few assumptions as possible, that the market microstructure capturing almost all of the available information in the data of stock markets does not involve higher order than pairwise interactions. We give an economic interpretation of this pairwise model. We show that it accurately recovers the empirical correlation coefficients thus the collective behaviors are quantitatively described by models that capture the observed pairwise correlations but no higher-order interactions. Furthermore, we show that an order-disorder transition occurs as predicted by the pairwise model. Last, we make the link with the graph-theoretic description of stock markets recovering the non-random and scale-free topology, shrinking length during crashes and meaningful clustering features as expected.
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constituants
constituents
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evidences
evidence
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graph theoretic
graph-theoretic
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scale free
scale-free