We would like to thank Peter Schotman, Roger Otten, and our colleagues at Maastricht University and ABP Investments Research for helpful comments. All remaining errors are the sole responsibility of In this paper we develop a trading strategy in which the difference in observed returns of value and growth stocks in the US stock market is exploited. In the literature this return spread is often called the “value premium”. In our modeling process we use a procedure similar to the recursive modeling approach as proposed by Pesaran and Timmerman (1995). We first estimate a universe of parsimonious models in an insample setting using a base set of technical and economic forecasting variables. Subsequently, we generate out-of-sample forecasts in a rolling window framework for all models and evaluate the performance in a socalled model training period. This adjustment directly relates to the critique of Bossaerts and Hillion (1999), who showed the insufficiency of in-sample criteria to forecast out-of-sample information ratios. Mode
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