Stock return predictability: A factor-augmented predictive regression system with shrinkage method
研究了利用数千个金融和经济变量，通过因子增强预测回归与收缩方法预测股票市场行为，包括预期收益和收益分布尾部，并在东京证券交易所验证了众多变量的预测信息价值。
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.