Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.06499 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917518549975040 |
|---|---|
| author | Du, Jin Walter, Alexander Ulrich, Maxim |
| author_facet | Du, Jin Walter, Alexander Ulrich, Maxim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06499 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cross-Market Alpha: Testing Short-Term Trading Factors in the U.S. Market via Double-Selection LASSO Du, Jin Walter, Alexander Ulrich, Maxim Statistical Finance 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. |
| title | Cross-Market Alpha: Testing Short-Term Trading Factors in the U.S. Market via Double-Selection LASSO |
| topic | Statistical Finance |
| url | https://arxiv.org/abs/2601.06499 |