Testing the Predictability of Stock Returns
- 1 August 2002
- journal article
- Published by MIT Press in The Review of Economics and Statistics
- Vol. 84 (3) , 407-415
- https://doi.org/10.1162/003465302320259439
Abstract
Previous literature indicates that stock returns are predictable by several strongly autocorrelated forecasting variables, especially at longer horizons. It is suggested that this finding is spurious and follows from a neglected near unit root problem. Instead of the commonly used t-test, we propose a test that can be considered as a general test of whether the return can be predicted by any highly persistent variable. Using this test, no predictability is found for U.S. stock return data from the period 1928-1996. Simulation experiments show that the standard t-test clearly overrejects whereas our proposed test controls size much better. © 2002 by the President and Fellows of Harvard College and the Massachusetts Institute of TechnologyKeywords
This publication has 22 references indexed in Scilit:
- The Restrictions on Predictability Implied by Rational Asset Pricing ModelsThe Review of Financial Studies, 1998
- Measuring the Predictable Variation in Stock and Bond ReturnsThe Review of Financial Studies, 1997
- Inference in Models with Nearly Integrated RegressorsEconometric Theory, 1995
- Inference in Time Series Regression When the Order of Integration of a Regressor is UnknownEconometric Theory, 1994
- Residual-Based Tests for the Null of Stationarity with Applications to U.S. Macroeconomic Time SeriesEconometric Theory, 1994
- Testing the Predictive Power of Dividend YieldsThe Journal of Finance, 1993
- Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and MeasurementThe Review of Financial Studies, 1992
- Bootstrapping Unstable First-Order Autoregressive ProcessesThe Annals of Statistics, 1991
- Mean Reversion in Stock Prices? A Reappraisal of the Empirical EvidenceThe Review of Economic Studies, 1991
- Heteroskedasticity and Autocorrelation Consistent Covariance Matrix EstimationEconometrica, 1991