Can Machines Learn Weak Signals?

Zhouyu Shen and Dacheng Xiu

No 33421, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: In high-dimensional regressions with low signal-to-noise ratios, we assess the predictive performance of several prevalent machine learning methods. Theoretical insights show Ridge regression's superiority in exploiting weak signals, surpassing a zero benchmark. In contrast, Lasso fails to exceed this baseline, indicating its learning limitations. Simulations reveal that Random Forest generally outperforms Gradient Boosted Regression Trees when signals are weak. Moreover, Neural Networks with l2-regularization excel in capturing nonlinear functions of weak signals. Our empirical analysis across six economic datasets suggests that the weakness of signals, not necessarily the absence of sparsity, may be Lasso's major limitation in economic predictions.

JEL-codes: C45 C52 C53 C55 C58 (search for similar items in EconPapers)
Date: 2025-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
Note: AP
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.nber.org/papers/w33421.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:33421

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w33421

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().