Predictability of Equity Returns over Different Time Horizons: A Nonparametric Approach

70 Pages Posted: 6 Jun 2019

See all articles by Qingqing Chen

Qingqing Chen

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Yongmiao Hong

Cornell University - Department of Economics

Date Written: May 20, 2016

Abstract

This paper aims to test whether equity returns are predictable over various horizons. We propose a reliable and powerful nonparametric test to examine the predictability of equity returns, which can be interpreted as a signal-to-noise ratio test. Our comprehensive in-sample and out-of-sample analysis shows that the commonly used predictive variables such as short rate, dividend yields and earnings yields have good predictability power at both short and long horizons, different from both the conventional wisdom and Ang and Bekaert (2007). Contrary to Goyal and Welch (2007), an out-of-sample nonparametric forecast outperforms the historical mean model and linear predictive models.

Keywords: Asset Return Predictability, bootstrap, Hypothesis Testing, Kernel, Nonlinearity, Signal-to-Noise

JEL Classification: G17 C14 C53

Suggested Citation

Chen, Qingqing and Hong, Yongmiao, Predictability of Equity Returns over Different Time Horizons: A Nonparametric Approach (May 20, 2016). Available at SSRN: https://ssrn.com/abstract=3390982 or http://dx.doi.org/10.2139/ssrn.3390982

Qingqing Chen (Contact Author)

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th Street SW
Washington, DC 20219
United States

Yongmiao Hong

Cornell University - Department of Economics ( email )

Department of Statistical Science
414 Uris Hall
Ithaca, NY 14853-7601
United States
607-255-5130 (Phone)
607-255-2818 (Fax)

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