While a great number of predictive variables for stock returns have been suggested, their prediction power is unstable. We propose a Least Absolute Shrinkage and Selection Operator (LASSO) estimator of a predictive regression in which stock returns are conditioned on a large set of lagged...

This paper investigates the performance of a factor-augmented regression (FAR) model with a mixture of stationary and nonstationary factors in stock return prediction. For comparison purpose, we also consider a traditional FAR model with only stationary factors. In an application with a dataset...

This paper investigates the performance of a factor-augmented regression (FAR) model with a mixture of stationary and nonstationary factors in stock return prediction. For comparison purpose, we also consider a traditional FAR model with only stationary factors. In an application with a dataset...

In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive...