Forecasting the Direction of the U.S. StockMarket with Dynamic Binary Probit Models

Several empirical studies have documented that the signs of excess stock returns are, to some extent, predictable. In this paper, we consider the predictive ability of the binary dependent dynamic probit model in predicting the direction of monthly excess stock returns. The recession forecast obtained from the model for a binary recession indicator appears to be the most useful predictive variable and once it is employed, the sign of the excess return is predictable in-sample. A new dynamic ''error correction'' probit model proposed in the paper yields the best out-of-sample forecasts with the average trading strategy returns higher than in the buy-and-hold strategy or in the ARMAX models. JEL Classification: C22, C25, E44, G11