Dissecting Characteristics Nonparametrically

By Joachim Freyberger, Andreas Neuhierl, Michael Weber. The Review of financial studies, 2020

480 citations recorded by the source corpus.

We propose a nonparametric method to study which characteristics provide incremental information for the cross-section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how selected characteristics affect expected returns nonparametrically. Our method can handle a large number of characteristics and allows for a flexible functional form. Our implementation is insensitive to outliers. Many of the previously identified return predictors don’t provide incremental information for expected returns, and nonlinearities are important. We study our method’s properties in simulations and find large improvements in both model selection and prediction compared to alternative selection methods. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/94fa3c3dc2647460238d00b7a6c7a12bf48fb210

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Citations in the corpus

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…”
Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (2023, unknown): “…(2017), Hansen, McMahon, and Prat (2018), Ke, Kelly, and Xiu (2019), Ke, Montiel Olea, and Nesbit (2019), Bybee et al. (2019), Gu, Kelly, and Xiu (2020), Cohen, Malloy, and Nguyen (2020), Freyberger, Neuhierl, and Weber (2020), Lopez-Lira 2019, Binsbergen et al. (2020), Bybee et al. (2021)).”
Forecasting: theory and practice (2020, background): “and Furlan (2014), Guidolin and Guseo (2012), and Furlan et al. (2016). The basic rationale for applying this class of models to the energy context is that learning from others’ experience, spread of relevant information and information sharing within communities are primary drivers of these process”
Taming the Factor Zoo: A Test of New Factors (2019, unknown): “Recently, Freyberger, Neuhierl, and Weber (2020) propose a group LASSO procedure to select characteristics and to estimate how they affect expected returns nonparametrically.”
Deep Learning in Asset Pricing (2019, methodology): “Hence, we follow the same approach as in Freyberger, Neuhierl, and Weber (2020) and Kelly, Pruitt, and Su (2019) to use only stocks that have all firm characteristics available in a given month, which has the additional benefit of removing predominantly stocks with a very small market…”
A diagnostic criterion for approximate factor structure (2016, background): “3 in the proof of Lemma 1 building on Geman (1980), Yin et al.”
Shrinking the Cross Section∗ (2017, unknown): “Fama and French (2008); Freyberger et al. (2017) provide evidence of nonlinear effects for other anomalies, but in terms of portfolio sorts and cross-sectional return prediction rather than SDF estimation.”
Deep Factor Alpha (2018, unknown): “Freyberger, Neuhierl, and Weber (2017) use an adaptive group LASSO, to select characteristics that provide independent information.”
Forest Through the Trees: Building Cross-Sections of Stock Returns (2020, unknown): “11See, e.g., Freyberger, Neuhierl, and Weber (2020) for the use of adaptive group lasso to estimate the nonlinear impact of characteristics on expected returns, and Gu, Kelly, and Xiu (2020b) and Chen, Pelger, and Zhu (2019) for the machine learning approach. medium and small securities reveal a…”