Anticipative portfolio optimisation using an interpretable evolving fuzzy transformer network - Ong, Bryan Shao En (Nanyang Technological University, 2024)
The accurate prediction of stock market prices remains one of the most challenging and studied problems within the field of financial engineering. However, the inherently uncertain and fuzzy nature of financial markets has often limited the effectiveness of conventional crisp computational models. Furthermore, as financial institutions deploy increasingly complex machine learning models, the opacity of such 'black box' systems raises significant issues. The imperative for explainable AI becomes all the more pressing in an industry that requires trust, transparency, and compliance with regulatory standards. Another challenge is optimal portfolio allocation. Classically, indicators driven by historical data is used which limits its applicability.