Artificial Intelligence in Quantitative Finance: Leveraging Deep Learning for Smarter Portfolio Management and Asset Allocation

 Advances in artificial intelligence (AI) and deep learning are transforming quantitative finance, offering new ways to model complex market dynamics. This study explores how deep learning models can enhance portfolio management and asset allocation, addressing the limitations of traditional models. The rapid advancement of Artificial Intelligence (AI) has transformed financial markets by enhancing risk management (6). We develop a deep learning-driven portfolio strategy using Long Short-Term Memory (LSTM) networks and Transformer models, applied to real historical financial data from U.S. and global markets. We aim to answer: (1) Can deep learning models improve the prediction of asset returns for portfolio allocation? (2) Do AI-based portfolio strategies achieve better risk-adjusted performance than traditional methods? (3) What are the implications and challenges of using deep learning in portfolio management across different markets? Methods: We conduct a comprehensive literature review on AI in finance. We then design a methodology where LSTM and Transformer models forecast asset returns, which inform a dynamic asset allocation via a mean-variance optimization framework.