Predicting Bear and Bull Stock Markets with Dynamic Binary Time Series Models

Despite the voluminous empirical research on the potential predictability of stock returns, very little attention has been paid on the predictability of bear and bull stock markets. In this study, the aim is to predict the U.S. bear and bull stock markets with dynamic binary time series models. Based on the results of monthly U.S. data set, the bear and bull markets are predictable in and out of sample. In particular, substantial additional predictive power can be obtained by allowing for dynamic structures in the employed binary response model. Probability forecasts of the state of the stock market can also be utilized to obtain optimal asset allocation decisions between stocks and bonds. It turns out that the dynamic probit models yield much higher portfolio returns compared with the buy-and-hold trading strategy in a small-scale market timing experiment.