Lassoed Boosting and Linear Prediction in the Equities Market

Xiao Huang

Papers from arXiv.org

Abstract: We consider a two-stage estimation method for linear regression. First, it uses the lasso in Tibshirani (1996) to screen variables and, second, re-estimates the coefficients using the least-squares boosting method in Friedman (2001) on every set of selected variables. Based on the large-scale simulation experiment in Hastie et al. (2020), lassoed boosting performs as well as the relaxed lasso in Meinshausen (2007) and, under certain scenarios, can yield a sparser model. Applied to predicting equity returns, lassoed boosting gives the smallest mean-squared prediction error compared to several other methods.

Date: 2021-12, Revised 2024-05
New Economics Papers: this item is included in nep-big and nep-ecm
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