Portfolio Selection

Portfolio selection aims to optimize investment strategies by allocating assets across various options to maximize returns while managing risk. Current research heavily utilizes machine learning, employing diverse models like deep reinforcement learning (including LSTM and Transformer networks), Hopfield networks, and conditional GANs, often integrated with traditional methods such as the Markowitz framework and Black-Litterman model, to improve prediction accuracy and portfolio construction. This field is significant for its direct impact on financial markets and investment decisions, with ongoing efforts focused on enhancing model robustness, addressing model risk, and developing more efficient algorithms for high-dimensional datasets.