The Myth of Long-Horizon Predictability
Interdisciplinary Center (IDC) Herzliyah - Arison School of Business
Matthew P. Richardson
New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER)
New York University; National Bureau of Economic Research (NBER)
NBER Working Paper No. w11841
The prevailing view in finance is that the evidence for long-horizon stock return predictability is significantly stronger than that for short horizons. We show that for persistent regressors, a characteristic of most of the predictive variables used in the literature, the estimators are almost perfectly correlated across horizons under the null hypothesis of no predictability. For example, for the persistence levels of dividend yields, the analytical correlation is 99% between the 1- and 2-year horizon estimators and 94% between the 1- and 5-year horizons, due to the combined effects of overlapping returns and the persistence of the predictive variable. Common sampling error across equations leads to ordinary least squares coefficient estimates and R2s that are roughly proportional to the horizon under the null hypothesis. This is the precise pattern found in the data. The asymptotic theory is corroborated, and the analysis extended by extensive simulation evidence. We perform joint tests across horizons for a variety of explanatory variables, and provide an alternative view of the existing evidence.
Number of Pages in PDF File: 39
Date posted: March 29, 2006
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