Factor Timing

Factor timing aims to optimize investment returns by strategically adjusting exposure to various market factors based on their predicted performance. Current research heavily emphasizes the application of deep learning techniques, including neural networks and graph-based models, to improve the accuracy and efficiency of factor prediction and portfolio construction. While these advanced models show promise in outperforming traditional linear approaches, challenges remain in interpreting the resulting factors and managing the increased transaction costs associated with frequent portfolio rebalancing. This research area holds significant potential for enhancing investment strategies and advancing our understanding of market dynamics.