Economic Cycles and Expected Stock Returns

50 Pages Posted: 2 Jul 2013

See all articles by Alessandro Beber

Alessandro Beber

Cass Business School; Centre for Economic Policy Research (CEPR)

Michael W. Brandt

Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER)

Maurizio Luisi

Independent; affiliation not provided to SSRN

Multiple version iconThere are 2 versions of this paper

Date Written: June 2013

Abstract

We construct daily real-time indices capturing the public information on realized and anticipated economic activity. The one-month change in realized fundamentals predicts US stock returns across horizons with strongest results between a month and a quarter. The information in anticipated fundamentals that is orthogonal to the realized data predicts returns even more strongly particularly at longer horizons up to two quarters. Splitting the sample into times of high versus low uncertainty, as measured by the cross-sectional dispersion of economist forecasts, we show that the predictability is largely concentrated in high-uncertainty times. Finally, extending the analysis internationally, we find similar results that are curiously much stronger when US data are used as predictors than global composites or local data.

Keywords: macroeconomic uncertainty, state of the economy, stock market predictability

JEL Classification: G12

Suggested Citation

Beber, Alessandro and Brandt, Michael W. and Luisi, Maurizio, Economic Cycles and Expected Stock Returns (June 2013). CEPR Discussion Paper No. DP9528. Available at SSRN: https://ssrn.com/abstract=2288493

Alessandro Beber (Contact Author)

Cass Business School ( email )

London, EC2Y 8HB
Great Britain

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

Michael W. Brandt

Duke University - Fuqua School of Business ( email )

1 Towerview Drive
Durham, NC 27708-0120
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Maurizio Luisi

Independent ( email )

No Address Available

affiliation not provided to SSRN

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