Prediction Sorted Portfolio

Prediction-sorted portfolios aim to optimize investment strategies by leveraging predictive models to rank assets and allocate capital accordingly, aiming for superior risk-adjusted returns compared to traditional methods. Current research focuses on employing advanced machine learning techniques, such as graph neural networks and ensemble Gaussian process regression, to improve prediction accuracy and portfolio construction, often incorporating risk management considerations like Value at Risk (VaR). These advancements offer the potential for more efficient and robust portfolio management, impacting both academic understanding of financial markets and practical investment strategies.