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.