The effects of recent subprime financial crisis on the US stock market are analyzed. To investigate this problem, a Bayesian panel data analysis to identify common factors that explain the movement of stock returns when the dimension is high is developed. For highdimensional panel data, it is known that previously proposed approaches cannot estimate accurately the variance–covariance matrix. An advantage of the proposed method is that it considers parameter uncertainty in variance–covariance estimation and factor selection. Two new criteria for determining the number of factors in the data are developed and the consistency of the selection criteria as both the number of observations and the crosssection dimension tend to infinity is established. An empirical analysis indicates that the US stock market was subject to 8 common factors before the outbreak of the subprime crisis, but the number of factors reduced substantially after the outbreak. In particular, a small number of common factors govern the fluctuations of the stock market after the collapse of Lehman Brothers. In other words, empirical evidence that the structure of US stock market has changed drastically after the subprime crisis is obtained. It is also shown that the factor models selected by the proposed criteria work well in out-of-sample forecasting of asset returns.