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arXiv · 2112.08934

Lassoed Boosting and Linear Prediction in the Equities Market

Xiao Huang
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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.

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Xiao Huang. 2021-12-16. Lassoed Boosting and Linear Prediction in the Equities Market. https://arxiv.org/abs/2112.08934

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