Intraday Market Return Predictability Culled from the Factor Zoo

Abstract

We document strong intraday market return predictability based on lagged high-frequency
cross-sectional returns of the factor zoo. Our results rely crucially on LASSO to regularize our predictive regressions along with techniques from financial econometrics to differentiate between continuous and discontinuous price increments. Trading strategies that utilize our forecasts generate sizeable out-of-sample Sharpe ratios and alphas after accounting for transaction costs. We trace the superior performance to periods of high economic uncertainty and a few key factors related to tail risk and liquidity, pointing to slow-moving capital and the gradual incorporation of new information as the underlying economic mechanisms at work.

Keywords: High-frequency data, market return predictability, factor zoo, machine learning, market timing, market frictions, slow-moving capital

JEL Classification: G12, G14, G17, C45, C55