AI in Portfolio Optimization
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This study investigates the application of artificial intelligence (AI) in portfolio optimization, a fundamental problem in finance that focuses on selecting the best combination of assets to maximize return while minimizing risk. Traditional approaches, such as the Markowitz Mean- Variance model, rely on simplifying assumptions that often fail to reflect real-world market dynamics. With the rapid advancement of AI, new methods have emerged that allow for better modeling of complex, nonlinear relationships within financial data. The objective of this project is to explore how AI techniques, including machine learning and deep learning, can improve portfolio optimization. The methodology involves collecting historical financial data, preprocessing it, and applying various models to estimate expected returns, risks, and optimal asset allocation strategies. Performance is evaluated using financial metrics such as portfolio return, volatility, and the Sharpe ratio. The results indicate that AI-based approaches outperform traditional models in many scenarios, particularly in capturing dynamic market behavior and adapting to changing conditions. However, the study also highlights the limitations of AI, including sensitivity to data quality and market unpredictability. Therefore, AI should be viewed as a decision-support tool rather than a fully autonomous solution. Overall, this research contributes to understanding how AI can enhance portfolio optimization while acknowledging its practical constraints.
Keywords
Artificial Intelligence (AI), Financial Market, Investments, Portfolio Optimization