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Lassoed Boosting and Linear Prediction in Equities Market

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  • Xiao Huang

Abstract

We consider a two-stage estimation method for linear regression that uses the lasso in Tibshirani (1996) to screen variables and re-estimate 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), the performance of lassoed boosting is found to be as competitive as the relaxed lasso in Meinshausen (2007) and can yield a sparser model under certain scenarios. An application to predict equity returns also shows that lassoed boosting can give the smallest mean square prediction error among all methods under consideration.

Suggested Citation

  • Xiao Huang, 2021. "Lassoed Boosting and Linear Prediction in Equities Market," Papers 2112.08934, arXiv.org, revised Apr 2022.
    • Handle: RePEc:arx:papers:2112.08934
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    File URL: http://arxiv.org/pdf/2112.08934
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