Portfolio Performance

Portfolio performance optimization aims to maximize investment returns while managing risk, a challenge addressed through diverse approaches. Current research focuses on incorporating real-time market conditions into risk assessment models (like Value at Risk and stress testing) using machine learning techniques such as variational inference and deep learning architectures (e.g., iTransformers, LSTMs), and also explores novel methods like topological data analysis and crowding networks for portfolio construction and risk hedging. These advancements offer improved portfolio strategies, potentially leading to more efficient resource allocation and enhanced investment decision-making across various sectors, including finance and renewable energy.