Macroeconomic Content of Characteristics-Based Asset Pricing Models: A Machine Learning Analysis

49 Pages Posted: 17 Jan 2020

See all articles by Oleg Rytchkov

Oleg Rytchkov

Temple University - Department of Finance

Xun Zhong

Fordham University - Finance Area

Date Written: December 30, 2019

Abstract

We consider five characteristics-based asset pricing models and study whether the non-market components of their stochastic discount factors (SDFs) are associated with macroeconomic shocks. Our analysis involves a comprehensive set of 127 macroeconomic variables and uses machine learning techniques to mitigate the overfitting problem caused by a large number of explanatory variables. We find that macroeconomic shocks are totally unrelated to the non-market components of the SDFs. This conclusion extends to several theory-motivated macroeconomic factors. Thus, our results suggest that the empirical success of characteristics-based asset pricing models is produced by their ability to identify behavioral factors in stock returns.

Keywords: asset pricing, stochastic discount factor, machine learning, elastic net, macroeconomic shocks

JEL Classification: G12, C58

Suggested Citation

Rytchkov, Oleg and Zhong, Xun, Macroeconomic Content of Characteristics-Based Asset Pricing Models: A Machine Learning Analysis (December 30, 2019). Available at SSRN: https://ssrn.com/abstract=3512123 or http://dx.doi.org/10.2139/ssrn.3512123

Oleg Rytchkov (Contact Author)

Temple University - Department of Finance ( email )

Fox School of Business and Management
Philadelphia, PA 19122
United States

Xun Zhong

Fordham University - Finance Area ( email )

45 Columbus Avenue, Room 620
New York, NY 10023
United States

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