Technical analysis and stock return predictability: An aligned approach

Abstract

This paper provides an empirical evaluation of the U.S. aggregate stock market predictability based on a new technical analysis index that eliminates the idiosyncratic noise component in technical indicators. I find that the new index exhibits statistically and economically significant in-sample and out-of-sample predictive power and outperforms the well-known technical indicators and macroeconomic variables. In addition, it can predict cross-sectional stock portfolio returns sorted by size, value, momentum, and industry and generate substantial utility gains for a mean-variance investor. A vector autoregression-based stock return decomposition shows that the economic source of the predictive power predominantly comes from time variations in future cash flows (i.e., the cash flow channel).