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

67 Pages Posted: 17 Jan 2020 Last revised: 1 Nov 2021

See all articles by Oleg Rytchkov

Oleg Rytchkov

Temple University - Department of Finance

Xun Zhong

Fordham University - Finance Area

Date Written: October 20, 2021

Abstract

We explore whether the empirical success of seven characteristics-based asset pricing models can be explained by their ability to identify macroeconomic risks. We find that although the stochastic discount factors (SDFs) of some models are weakly related to macroeconomic shocks, the SDFs' non-market components are totally unrelated to them. The result also holds for macroeconomic news and real-time macroeconomic shocks. Our analysis involves a comprehensive set of more than 100 macroeconomic indicators and uses machine learning techniques to mitigate the overfitting problem caused by a large number of explanatory variables. Our paper illustrates how machine learning can be used for analyzing the explainability of one variable by many others.

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 (October 20, 2021). 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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