Viktoria-Sophie Wendt

We analyze the performance of a comprehensive set of equity premium forecasting strategies. All strategies were found to outperform the mean in previous academic publications. However, using a multiple testing framework to account for data snooping, our findings support Welch and Goyal (2008) in that almost all equity premium forecasts fail to beat the mean out-of-sample. Only few forecasting strategies that are based on Ferreira and Santa-Clara’s (2011) sum-of-the-parts approach generate robust and statistically significant economic gains relative to the historical mean even after controlling for data snooping and accounting for transaction costs.

Factor‐based allocation embraces the idea of factors, as opposed to asset classes, as the ultimate building blocks of investment portfolios. We examine whether there is a superior way of combining factors in a portfolio and provide a comparison of factor‐based allocation strategies within a multiple testing framework. Factor‐based allocation is profitable beyond exploiting genuine risk premia, even when applying multiple testing corrections. Investment portfolios can be efficiently diversified using factor‐based allocation strategies, as demonstrated by robust economic performance over various economic scenarios. The naïve equally weighted factor portfolio, albeit simple and cost‐efficient, cannot be outperformed by more sophisticated allocation strategies.

The literature on gold is dominated by empirical studies on its diversification, hedging, and safe haven properties. In contrast, the question “When to invest in gold?” is generally not analyzed in much detail. We test more than 4000 seasonal, technical, and fundamental timing strategies for gold and find evidence for some market timing ability and economic gains relative to a passive buy-and-hold benchmark. However, since the results are not robust to data-snooping biases and limited to specific evaluation periods, we conclude that our findings support the efficiency of the gold market.