Machine-Learning-Based Return Predictors and the Spanning Controversy in Macro-Finance
提出SAGLasso机器学习方法，从917个宏观变量中筛选出30个构建新因子，该因子能显著预测债券超额回报且不受收益率曲线影响，为解决宏观金融中的跨期争议提供了可能。
We propose a two-step machine learning algorithm—the Supervised Adaptive Group LASSO (SAGLasso) method—that is suitable for constructing parsimonious return predictors from a large set of macro variables. We apply this method to government bonds and a set of 917 macro variables and construct a new, transparent, and easy-to-interpret macro variable with significant out-of-sample predictive power for excess bond returns. This new macro factor, termed the SAGLasso factor, is a linear combination of merely 30 selected macro variables out of 917. Furthermore, it can be decomposed into three sublevel factors: a novel housing factor, an employment factor, and an inflation factor. Importantly, the predictive power of the SAGLasso factor is robust to bond yields, namely, the SAGLasso factor is not spanned by bond yields. Moreover, we show that the unspanned variation of the SAGLasso factor cannot be attributed to yield measurement error or macro measurement error. The SAGLasso factor therefore provides a potential resolution to the spanning controversy in the macro-finance literature. This paper was accepted by Haoxiang Zhu, finance. Funding: This work was supported by a grant from the Penn State Institute for Real Estate Studies. Supplemental Material: Data and the internet appendices are available at https://doi.org/10.1287/mnsc.2022.4386 .