Artificial Intelligence in Investment Portfolio Optimization: A Comparative Study of Machine Learning Algorithms

This paper examines the use of artificial intelligence in managing investment portfolios, emphasizing the performance of different machine learning techniques. Conventional approaches to portfolio optimization are, however, unable to handle large, multi-variate financial data sets and unstable market conditions, which makes them less useful in dynamic investment settings. Thus, this research aims to apply AI technologies to improve portfolio models' accuracy, risk management, and returns. We compare machine learning methods, such as Support Vector Machines, Random Forests, LSTM, and Reinforcement Learning, to analyze their effectiveness based on criteria such as Sharpe ratio, volatility, and annualized returns. The research suggests that AI models can enhance portfolio optimization results by a wide margin depending on the algorithm used and market conditions. These results highlight that AI can help revolutionize financial decisions, providing enhanced, flexible, and precise mechanisms for managing today’s portfolios.