Predictable Stock Returns: Reality or Statistical Illusion?
Charles R. Nelson
Myung J. Kim
Recent research suggests that stock returns are predictable from
fundamentals such as dividend yield, and that the degree of predictability
rises with the length of the horizon over which return is measured. This paper
investigates the magnitude of two sources of small ssmple bias in these
First, it is a standard result in econometrics that regression on the
lagged value of the dependent variable is biased in finite samples. Since a
fundamental such as the price/dividend ratio is a statistical proxy for lagged
price, predictive regressions are potentially subject to a corresponding small
sample bias. This may create the illusion that one can buy low and sell high
in the sample even if the relationship is useless for forecasting. Second,
multiperiod returns are positively autocorrelated by construction, raising the
possibility of spurious regression. Standard errors which are computed from
the asymptotic formula may not be large enough in small samples.
A set of Monte Carlo experiments are presented in which data are generated
by a version of the present value model in which the discount rate is constant
so returns are not in fact predictable. We show that a number of the
characteristica of the historical results can be replicated simply by the
combined effects of the two small sample biases.
In order to set up a list of libraries that you have access to,
you must first login
or sign up.
Then set up a personal list of libraries from your profile page by
clicking on your user name at the top right of any screen.