In this paper, we present a general model of the joint data generating process underlying economic activity and stock market returns allowing for complex nonlinear feedbacks and interdependencies between the conditional means and conditional volatilities of the variables. We propose statistics that capture the long and short run responses of the system to the arrival of fundamental and non-fundamental news, conditioning on the sign and time of arrival of the news. The model is applied to US data. We find that there are significant differences between the short and long run responses of economic activity and stock returns to the arrival of news. Moreover, for certain classifications of news, the respective responses of economic activity and stock returns vary according to the nature of the news and the phase of the business cycle at which the news arrives.