Taming the Factor Zoo: A Test of New Factors

By Guanhao Feng, Stefano Giglio, D. Xiu. Journal of Finance, 2019

528 citations recorded by the source corpus.

We propose a model-selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high-dimensional set of existing factors explains. Our methodology explicitly accounts for potential model-selection mistakes, unlike the standard approaches that assume perfect variable selection, which rarely occurs in practice and produces a bias due to the omitted variables. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are found to be redundant relative to the existing factors, a few — such as profitability — have statistically significant explanatory power beyond the hundreds of factors proposed in the past. In addition, we show that our estimates and their significance are stable, whereas the model selected by simple LASSO is not.

Subfields: Accounting, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/63d23333207c2c21302314fda7703b4b5c6e58f9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

In Economics and Econometrics

In Finance

Citations in the corpus

Factors That Fit the Time Series and Cross-Section of Stock Returns (2018, methodology): “For example, DeMiguel et al. (2017), Freyberger et al. (2019) and Feng et al. (2019) employ factor selection with Lasso-style 𝐿1-norm penalties.”
Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model (2019, background): “See Feng, Giglio, and Xiu (2017) for additional factors and measures of market risk.”