Significance of Technical Indicators in the Era of Machine Learning

Abstract

This paper examines the significance of technical indicators in the context of machine learning. We commence by constructing a machine learning–based cross-sectional asset pricing model that incorporates 124 previously identified asset pricing characteristics and fourteen macroeconomic factors. Subsequently, we assess widely regarded technical indicators in their ability to influence machine learning–based model accuracy. Our findings indicate that Bollinger Bands, moving averages, momentum reversal, MIN–MAX retracement, trender, and the parabolic time/price system have a significant influence on the predictability of machine learning–based models. Interestingly, we find that a well-curated machine learning model with technical indicators is comparable in performance to models with much larger asset pricing characteristics. We also document a time-varying phenomenon in the predictability of asset pricing models. Finally, long–short portfolio analysis suggests that active investment using technical indicators and machine learning can yield positive alpha.