Cross-Market Alpha: Testing Short-Term Trading Factors in the U.S. Market via Double-Selection LASSO

TL;DR

This paper introduces a double-selection LASSO approach to identify and leverage short-term trading signals from a high-dimensional set of indicators, improving alpha generation in the U.S. market.

Contribution

It applies a robust statistical framework to extract non-redundant, short-term signals from a large indicator library, demonstrating their effectiveness in the U.S. market.

Findings

Identified 17 significant short-term signals that capture behavioral market dynamics.

Short-term signals remain effective over monthly rebalancing periods.

Combining short-term signals with fundamental data enhances portfolio diversification.

Abstract

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.