Anticipative portfolio optimisation using an interpretable evolving fuzzy transformer network

• Main Author: Ong, Bryan Shao En
• Other Authors: Quek Hiok Chai
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science; Intepretable; Fuzzy; Transformer; Portfolio optimisation; Explainable; Evolving
• Online Access: https://hdl.handle.net/10356/175135
• Institution: Nanyang Technological University

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. This paper proposes a novel Evolving Fuzzy Transformer Network that utilises a fuzzy system to model the underlying uncertainty in a market. Through the creation of this neuro-fuzzy system, a quantitative explanation can be generated, allowing for interpretation of the system. Finally, we use the predictive model to generate forecast indicators to dynamically balance a portfolio with a deep reinforcement learning model.