Predicting and capitalizing on stock market bears in the U.S

his paper attempts to predict the bear conditions on the US stock market. To this aim we elaborate simple predictive regressions, static and dynamic binary choice (BCM) as well as Markov-switching models. The in- and out-of-sample prediction ability is evaluated and we compare the forecasting performance of various specifications across as well as within models. It turns out that various dynamic extensions of static versions of probit and logit models reveal additional predictive information for both in- and out-of-sample fit. We also find that binary models outperform the Markov-switching model. With respect to the macro-financial variables, terms spreads, inflation and money supply turn out to be useful predictors. The results lead to useful implications for investors practicing active portfolio and risk management and for policy makers as tools to get early warning signals.(This abstract was borrowed from another version of this item.)