The paper examines the informational content of a series of macroeconomic indicator variables with the intention to predict stock market downturns - colloquially also referred to as 'bear markets' - for G7 countries. The sample consists of monthly stock market indices and a set of exogenous indicator variables that are subject to examination, ranging from January 1970 to September 2008. The methodical approach is twofold. In the first step, a modified version of the Bry-Boschan business cycle dating algorithm is used to identify bull and bear markets from the data by creating dummy variable series. In the second step, a substantial number of probit estimations is carried out, by regressing the newly identified dummy variable series on different specifications of indicator variables. By applying widely used in- and out-of-sample measures, the specifications are evaluated and the forecasting performance of the indicators is assessed. The results are mixed. While industrial production, and money stock measures seem to have no predictive power, short and long term interest rates, term spreads as well as unemployment rate exhibit some. Here, it is clearly possible to extract some informational content even three months in advance and so to beat the predictions made by a recursively estimated constant.