Portfolio Optimization Problem

Portfolio optimization aims to construct investment portfolios that maximize returns while managing risk, a complex problem tackled using various approaches. Current research emphasizes incorporating risk measures like the Sharpe Ratio and Conditional Value-at-Risk, employing advanced techniques such as reinforcement learning (with deep neural networks like CNNs and LSTMs), multi-armed bandits, and distributionally robust optimization. These methods are being enhanced by transfer learning to leverage knowledge across different markets and timeframes, and even quantum computing is being explored for improved efficiency. The resulting advancements have significant implications for financial decision-making and resource allocation across various domains.