Predicting Relative Returns

56 Pages Posted: 2 Oct 2017

See all articles by Valentin Haddad

Valentin Haddad

University of California, Los Angeles (UCLA) - Anderson School of Management; National Bureau of Economic Research (NBER)

Serhiy Kozak

University of Maryland - Robert H. Smith School of Business

Shrihari Santosh

University of Maryland

Multiple version iconThere are 3 versions of this paper

Date Written: September 2017

Abstract

Across a variety of asset classes, we show that relative returns are highly predictable in the time series in and out of sample, much more so than aggregate returns. For Treasuries, slope is more predictable than level. For equities, dominant principal components of anomaly long-short strategies are more predictable than the market. For foreign exchange, a carry portfolio is more predictable than a basket of all currencies against the dollar. We show the commonly used practice to predict each individual asset is often equivalent to predicting only their first principal component, the index, which obscures the predictability of relative returns. Our findings highlight that focusing on important dimensions of the cross-section allows one to uncover additional economically relevant and statistically robust patterns of predictability.

Suggested Citation

Haddad, Valentin and Kozak, Serhiy and Santosh, Shrihari, Predicting Relative Returns (September 2017). NBER Working Paper No. w23886, Available at SSRN: https://ssrn.com/abstract=3046355

Valentin Haddad (Contact Author)

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Serhiy Kozak

University of Maryland - Robert H. Smith School of Business ( email )

7621 Mowatt Ln
College Park, MD 20742
United States

HOME PAGE: http://https://serhiykozak.com

Shrihari Santosh

University of Maryland ( email )

College Park
College Park, MD 20742
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
59
Abstract Views
904
rank
10,296
PlumX Metrics