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Lassoed boosting and linear prediction in the equities market

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  • Huang Xiao

Abstract

We consider a two-stage estimation method for linear regression. First, it uses the lasso in Tibshirani to screen variables and, second, re-estimates the coefficients using the least-squares boosting method in Friedman on every set of selected variables. Based on the large-scale simulation experiment in Hastie, Tibshirani, and Tibshirani, lassoed boosting performs as well as the relaxed lasso in Meinshausen 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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  • Huang Xiao, 2024. "Lassoed boosting and linear prediction in the equities market," Econometric Reviews, Taylor & Francis Journals, vol. 43(9), pages 733-751, October.
    • Handle: RePEc:taf:emetrv:v:43:y:2024:i:9:p:733-751
    DOI: 10.1080/07474938.2024.2359475
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