Generative AI for European asset pricing: alleviating the momentum anomaly
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
Abstract
We challenge the notion of classical factor models that concentrated factors, particularly the anomalous momentum factor, dominate the European stock market. We use a generative artificial intelligence (generative AI) asset pricing model that incorporates the economic rationale of no-arbitrage and treats the European capital market as a complex system. This model outperforms all European benchmarks over 16 years out-of-sample, with an annualized Sharpe ratio of 3.68, a cross-sectional (Formula presented.) of over 22%, and an explained variation of over 13%. Using interpretable AI techniques, we find that the model sees a zoo of factors in the European market rather than just a concentrated set. These excellent results stem from time-conditional modeling, which requires momentum, especially for tangency portfolio weights. Conditional betas can substitute momentum more efficiently. Overall, the risk-sharing mechanism for European assets is more complex than previously thought.
Details
Originalsprache | Englisch |
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Fachzeitschrift | European Journal of Finance |
Publikationsstatus | Elektronische Veröffentlichung vor Drucklegung - 18 Dez. 2024 |
Peer-Review-Status | Ja |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- factor zoo, generative AI, Momentum anomaly, Sharpe ratio