2026/01/10 by Jin Du, Alexander Walter, Maxim Ulrich · 2 voices
Economics, Econometrics and Finance · #q-fin.ST
arxiv published 2026/01/10 · arxiv updated 2026/05/21
While traditional equity factor investing relies heavily on slow-moving fundamental accounting metrics, these models frequently suffer from factor crowding and miss real-time, sentiment-driven market dislocations. This study explores how institutional investors can leverage a high-dimensional library of 191 short-term, trading-based signals, originally developed for the retail-heavy Chinese A-share market, to enhance alpha generation within the highly institutionalized U.S. S&P 500 universe from 2002 to 2022. Utilizing a robust double-selection LASSO framework to control for 151 established fundamental factors, we isolate 17 distinct price-volume and microstructural signals that capture significant, non-redundant risk premiums. Our empirical evidence demonstrates that these fast trading signals capture universal behavioral dynamics that do not dilute over a monthly rebalancing horizon. Integrating these short-term behavioral footprints with slow fundamental data offers a powerful dual-horizon framework to mitigate model misspecification risk and enhance large-cap portfolio diversification.