Portfolio Theory: A Literature Review
Literature Review, Portfolio Theory, Machine-Learning
This study explores the evolution of portfolio optimization from Modern Portfolio Theory (MPT) and Post-Modern Portfolio Theory (PMPT) to machine learning (ML)-driven models. While MPT and PMPT provide foundational risk-return frameworks, ML techniques, including deep learning and reinforcement learning, offer advanced solutions to longstanding challenges. Through a systematic review and empirical analysis, this work compares traditional and ML-driven approaches, assessing their theoretical foundations and real-world applicability. The findings highlight the potential of AI-driven strategies to enhance portfolio management.