Deep Learning for Portfolio Optimization An AI-Driven Approach to Risk-Adjusted Returns

Deep Learning for Portfolio Optimization: An AI-Driven Approach to Risk-Adjusted Returns explores the application of artificial intelligence methods to portfolio construction and risk management. The study benchmarks traditional allocation strategies — equal weighting, inverse-volatility, and mean–variance optimization — against AI-driven approaches, with a particular focus on deep learning (DL) and deep reinforcement learning (DRL). Using five years of S&P 500 equity data, the paper evaluates portfolios on metrics such as annualized return, volatility, Sharpe ratio, Sortino ratio, Conditional Value-at-Risk, and maximum drawdown. The results highlight the trade-offs between classical models and AI-driven methods: equal-weight portfolios provide robustness, inverse-volatility portfolios reduce overall risk exposure, and mean–variance optimization achieves higher in-sample Sharpe ratios but is highly sensitive to estimation error. The discussion emphasizes how DL and DRL can overcome these limitations by capturing nonlinear dependencies, adapting to market regimes, and enabling dynamic rebalancing. This research contributes to the growing literature at the intersection of finance and machine learning by providing both theoretical context and empirical analysis. It serves as a foundation for further exploration of AI-based strategies across equities, ETFs, and cryptocurrencies, with potential applications for both academic research and practical asset management.