Index Tracking

Index tracking aims to replicate the performance of a market index using a smaller, more manageable portfolio, thereby reducing costs and improving efficiency. Current research focuses on optimizing portfolio selection through techniques like sparse portfolio construction using L0-norm constraints and advanced algorithms such as primal-dual splitting methods, as well as leveraging reinforcement learning and deep learning models to predict market sensitivities and dynamically adjust portfolio weights. These advancements offer improved tracking accuracy and cost reduction, impacting both passive investment strategies and the broader field of portfolio optimization.