In-sample vs. out-of-sample tests of stock return predictability in the context of data mining

In this paper, we undertake an extensive analysis of in-sample and out-of-sample tests of stock return predictability in an effort to better understand the nature of the empirical evidence on return predictability. We show that a number of financial variables appearing in the literature display both insample and out-of-sample predictive ability with respect to stock returns in annual data covering most of the twentieth century. In contrast to the extant literature, we demonstrate that there is little discrepancy between in-sample and out-of-sample test results once we employ recently developed out-of-sample tests with good power properties. While conventional wisdom holds that out-of-sample tests help guard against data mining, Inoue and Kilian (2004) recently argue that in-sample and out-of-sample tests are equally susceptible to data mining biases. With this in mind, we test for return predictability using a bootstrap procedure that explicitly accounts for data mining when calculating critical values, and we still find that certain financial variables display significant in-sample and out-of-sample predictive ability with respect to stock returns. JEL classifications: C22, C52, C53, G12, G14