Industry equi-correlation: A powerful predictor of stock returns

Yudong Wang, Zhiyuan Pan, Chongfeng Wu and Wenfeng Wu

Journal of Empirical Finance, 2020, vol. 59, issue C, 1-24

Abstract: We show that the detrended equi-correlation of the returns of industry portfolios is a strong predictor of excess returns to the S&P 500 Index. Using a sample from 1927 to 2015, our monthly industry equi-correlation (IEC) index produces an out-of-sample R2 of as high as 0.888%. For an investor with mean–variance utility, the IEC index can generate utility gains of 120.5 basis points over the benchmark model of the historical average. The return predictability of the IEC index is stronger than that of all of the popular predictor variables. Furthermore, we find that incorporating IEC in a univariate predictive regression with a popular predictor can significantly improve the out-of-sample forecasting performance of the individual models and their forecast combinations. These findings are confirmed by a large battery of robustness checks.

Keywords: Stock excess return; Predictive regression; Industry portfolio; Dynamic equi-correlation; Popular predictor variables (search for similar items in EconPapers)
JEL-codes: C11 C22 G11 G12 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S092753982030044X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:59:y:2020:i:c:p:1-24

DOI: 10.1016/j.jempfin.2020.07.005

Access Statistics for this article

Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

More articles in Journal of Empirical Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().