AI and quantitative investing
From: Sim Kee Boon Institute for Financial Economics (@SKBI_SMU)
[CALL FOR PAPERS] Don’t miss the opportunity to share your research at our upcoming Quant Investment Forum: AI and Finance. ⏰ Submissions close this Friday (10 April 2026). 📩 Submit here >> https://t.co/g96akr34ht https://t.co/WG89woCmob
Suggested talking points
Empirical findings on AI model performance degradation in quantitative strategies during regime shifts, with direct implications for risk management frameworks that currently assume stable feature relationships
Comparative analysis of transformer-based versus traditional machine learning architectures for multi-asset class signal generation, addressing the practical implementation challenges institutional investors face when transitioning from statistical models to deep learning approaches
Governance and validation protocols for AI-driven portfolio construction, including backtesting biases specific to neural networks and the operational controls required for regulatory compliance in systematic trading
Position your research as addressing the specific infrastructure and validation gaps that asset managers must solve to operationalize AI in quantitative investing, rather than exploring AI capabilities in isolation.
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