Predicting Premiums for the Market, Size, Value, and Momentum Factors

This paper investigates the out-of-sample predictability of monthly market as well as size, value, and momentum premiums. We use a sample from each of the US and the Swiss stock markets between 1989 and 2007. Using the Swiss sample provides an important new perspective as the repeated evaluation of the same (US) data set leads to data mining problems. To avoid data mining in our predictability study, we test both statistical significance and robustness in the two samples. Our key results are as follows. We find no robust indication that the market premium is predictable, which is also true for the momentum and value premiums. It cannot be excluded that the results from the US may be caused by data mining in light of the results from the Swiss sample. However, the size premium seems to be somewhat predictable, due to the credit spread. We theorize that there are three possible reasons for this rare evidence for predictability. First, predictability may have disappeared over the last decade, as academic research made the respective information public. Second, predictability seems, as we demonstrate, not to be robust to the choice of methodology. Third, robustness tests in the Swiss sample reveal that many of the supposedly statistically significant interrelations from the US sample may be attributed to randomness, which, in that case, would be data mining. Therefore, we think that future discussions of predictability should address the issue of data mining by applying robustness tests