Taming the Factor Zoo

By Guanhao Feng, Stefano Giglio, D. Xiu. 2017

114 citations recorded by the source corpus.

Subfields: Management Information Systems, Marketing

How researchers see this paper

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

In Management Information Systems

1. Nicholas G. Polson
2. Pedro C. L. Souza
3. Clifford Lam
4. Jianeng Xu
5. Jingyu He
6. Guanhao Feng

In Marketing

1. Nicholas G. Polson
2. Pedro C. L. Souza
3. Clifford Lam
4. Jianeng Xu
5. Jingyu He
6. Guanhao Feng
7. Michael Wolf

Citations in the corpus

• Deep Learning for Predicting Asset Returns (2018, methodology): “Improved methodologies have been suggested by Campbell and Thompson (2007), Rapach et al. (2010) and Harvey et al. (2016) and, more recently, Feng et al. (2017) and Kozak et al. (2017).”
• Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, background): “But many other factors have been proposed in the literature; for example, see Bai and Shi (2011, Section 4) and Feng, Giglio, and Xiu (2017).”
• Shrinking the Cross Section∗ (2017, unknown): “(2017) and Feng et al. (2017) focus on characteristics-based factor selection in Lasso-style estimation with L1-norm penalties. Their findings are suggestive of a relatively high degree of redundancy among cross-sectional stock return predictors. Yet, as our results show, for the purposes of SDF est”
• Empirical Asset Pricing via Machine Learning (2018, background): “A nascent literature has begun to make progress marrying machine learning with equilibrium asset pricing (for example, Kelly and Pruitt, 2013; Feng et al., 2017; Kelly et al., 2017), and this remains an exciting direction for future research.”
• A Transaction-Cost Perspective on the Multitude of Firm Characteristics (2020, unknown): “Feng, Giglio, and Xiu (2017) combine the double-selection lasso in Belloni, Chernozhukov, and Hansen (2014) with two-pass regressions to estimate risk prices and evaluate the marginal contribution of a new factor with respect to an existing high-dimensional set 8Note that one can also regress the…”
• Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model (2019, methodology): “The BCD algorithm is closely related to the iterative coordinate descent of Fan and Lv (2011), and is also discussed by Friedman., Hastie, and Tibshirani (2010) and Dicker, Huang, and Lin (2013).”
• Bond Risk Premia with Machine Learning∗ (2018, unknown): “More recent work by Kelly and Pruitt (2013), Kelly and Pruitt (2015), Kozak, Nagel and Santosh (2017), Feng et al. (2017), Freyberger et al. (2017), Giglio and Xiu (2017), Feng, Polson and Xu (2018), Kelly, Pruitt and Su (2018), and Messmer (2017) further show the advantages and promises of…”
• Deep Factor Alpha (2018, methodology): “Feng, He, and Polson (Feng et al.) apply deep learning and find the existence of nonlinear factors for predicting asset returns at the extremes of the characteristic space. Harvey and Liu (Harvey and Liu) refine the alpha estimates by drawing information from the underlying cross-sectional distribut”