Credit Conditions and Stock Return Predictability

53 Pages Posted: 19 Mar 2010 Last revised: 26 Oct 2015

See all articles by Sudheer Chava

Sudheer Chava

Georgia Institute of Technology - Scheller College of Business

Michael F. Gallmeyer

University of Virginia (UVA) - McIntire School of Commerce

Heungju Park

Sungkyunkwan University - SKK Business School

Date Written: September 13, 2014

Abstract

We analyze U.S. stock return predictability using a measure of credit standards (Standards) derived from the Federal Reserve Board's Senior Loan Officer Opinion Survey on Bank Lending Practices. Standards is a strong predictor of stock returns at a business cycle frequency, especially in the post-1990 data period. Empirically, a tightening of Standards predicts lower future stock returns. Standards performs well both in-sample and out-of-sample and is robust to a host of consistency checks. Standards captures stock return predictability at a business cycle frequency and is driven primarily by the ability of Standards to predict cash flow news.

Keywords: Stock predictability, credit supply, macroeconomics, survey data

JEL Classification: E44, G12, G14, G17, G21

Suggested Citation

Chava, Sudheer and Gallmeyer, Michael F. and Park, Heungju, Credit Conditions and Stock Return Predictability (September 13, 2014). Available at SSRN: https://ssrn.com/abstract=1571958 or http://dx.doi.org/10.2139/ssrn.1571958

Sudheer Chava

Georgia Institute of Technology - Scheller College of Business ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States

HOME PAGE: http://www.prism.gatech.edu/~schava6/

Michael F. Gallmeyer (Contact Author)

University of Virginia (UVA) - McIntire School of Commerce ( email )

P.O. Box 400173
Charlottesville, VA 22904-4173
United States
434-243-4043 (Phone)
434-924-7074 (Fax)

HOME PAGE: http://www.commerce.virginia.edu/faculty_research/facultydirectory/Pages/Gallmeyer.aspx

Heungju Park

Sungkyunkwan University - SKK Business School ( email )

Seoul
Korea, Republic of (South Korea)

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
543
Abstract Views
2,816
rank
57,168
PlumX Metrics