Beschreibung:
We propose an aggregate technical trading index by extracting the most relevant forecasting information contained in 7,846 technical trading rules to predict equity risk premium in the U.S. The proposed method is based on the false discovery rate and partial least squares approaches that alleviate the impact of data-snooping bias and idiosyncratic noise components in technical indicators. We find evidence that the aggregate technical trading index can deliver sizable economic gains for mean-variance investors in asset allocation analysis after taking both transaction costs and data snooping bias into account