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The collective phenomena of a liquid market is characterized in terms of a particle system scenario. This physical analogy enables us to disentangle intrinsic features from purely stochastic ones. The latter are the result of environmental changes due to a `heat bath acting on the many-asset system, quantitatively described in terms of a time dependent effective temperature. The remaining intrinsic properties can be widely investigated by applying standard methods of classical many body systems. As an example, we consider a large set of stocks traded at the NYSE and determine the corresponding asset--asset `interaction potential. In order to investigate in more detail the cluster structure suggested by the short distance behavior of the interaction potential, we perform a connectivity analysis of the spatial distribution of the particle system. In this way, we are able to draw conclusions on the intrinsic cluster persistency independently of the specific market conditions.
A new approach to the understanding of complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in Kolmogorov-Chaitin theory is applied to binary sequenc
We are interested in the existence of equivalent martingale measures and the detection of arbitrage opportunities in markets where several multi-asset derivatives are traded simultaneously. More specifically, we consider a financial market with multi
This paper investigates whether security markets price the effect of social distancing on firms operations. We document that firms that are more resilient to social distancing significantly outperformed those with lower resilience during the COVID-19
Following a long tradition of physicists who have noticed that the Ising model provides a general background to build realistic models of social interactions, we study a model of financial price dynamics resulting from the collective aggregate decisi
We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling. Because asset returns often exhibit sequential dependence that may not be effectively captured by conventional time s