Stock return predictability: A factor-augmented predictive regression system with shrinkage method

To predict stock market behaviors, we use a factor-augmented predictive regression with shrinkage to incorporate the information available across literally thousands of financial and economic variables. The system is constructed in terms of both expected returns and the tails of the return distribution. We develop the variable selection consistency and asymptotic normality of the estimator. To select the regularization parameter, we employ the prediction error, with the aim of predicting the behavior of the stock market. Through analysis of the Tokyo Stock Exchange, we find that a large number of variables provide useful information for predicting stock market behaviors.