Market timing and return prediction under model instability

Abstract

Despite mounting empirical evidence to the contrary, the literature on predictability of stock returns almost uniformly assumes a time-invariant relationship between state variables and returns. In this paper, we propose a two-stage approach for forecasting of financial return series that are subject to breaks. The first stage adopts a reversed ordered Cusum (ROC) procedure to determine in real time when the most recent break has occurred. In the second stage, post-break data is used to estimate the parameters of the forecasting model. We compare this approach to existing alternatives for dealing with parameter instability such as the Bai–Perron method and the time-varying parameter (TVP) model. An out-of-sample forecasting experiment demonstrates considerable gains in market timing precision from adopting the proposed two-stage forecasting method.