Technical Analysis and Stock Returns : An Aggregate Approach

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 exhibit statistically and economically significant power in forecasting stock market excess returns. Moreover, the new index can deliver sizable economic gains for mean-variance investors in asset allocation analysis even taking both transaction costs and data snooping bias into account. We find that the predictive power of the aggregate technical trading index derives from its ability to predict returns in business-cycle expansions and returns of telecom and technology stocks