No abstract

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two e¡ects reinforce each other, because more highly persistent series are more likely to be found signi¢cant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious. PREDICTIVE MODELS FOR COMMON STOCK RETURNS have long been a staple of ¢nancial economics. Early studies, reviewed by Fama (1970), used such models to examine market e⁄ciency. Stock returns are assumed to be predictable, based on lagged instrumental variables, in the current conditional asset pricing literature. Standard lagged variables include the levels of short-term interest rates, payout-toprice ratios for stock market indexes, and yield spreads between low-grade and high-grade bonds or between long-and short-term bonds. Many of these variables behave as persistent, or highly autocorrelated, time series. This paper studies the ¢nite sample properties of stock return regressions with persistent lagged regressors. We focus on two issues. The ¢rst is spurious regression, analogous to Yule (1926) and Granger and Newbold (1974). These studies warned that spurious relations may be found between the levels of trending time series that are actually independent. For example, given two independent random walks, it is likely that a regression of one on the other will produce a ''signi¢cant'' slope coe⁄cient, evaluated by the usual t-statistics.In this paper, the dependent variables are asset rates of return, which are not highly persistent. Thus, one may think that spurious regression problems

No abstract

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious