Internal and external influence in the US stock market

We analyze the multivariate distribution of the US stock returns using pairwise interaction models, inspired by Ising models in glasses and neural networks. Using the inference methods from neural networks analysis we find unique descriptors of the dynamics of stock returns in periods of crisis. Our findings suggest that the near crash dynamics is primarily governed by external factors (external fields), while internal network structure (J couplings) are not significantly affected.