Before revision:  This hierarchical shrinkage prior effectively pulls the factor loadings of unimportant factors towards zero , thereby increasing parsimony even more. We apply the model to simulated data as well as  daily log-returns of 300 S&P 500 stocks and demonstrate its effectiveness to obtain sparse loadings matrices and more precise correlation estimates .
After revision:  This hierarchical shrinkage prior effectively pulls the factor loadings of unimportant factors towards zero . To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as daily log-returns of 300 S&P 500 stocks and demonstrate its effectiveness to obtain sparse loadings matrices and more precise correlation estimates .