Utilizing Deep Neural Networks for Portfolio Optimization in Financial Markets

Portfolio optimization has become increasingly important in financial markets, driven by the need for robust and adaptive investment strategies. This paper explores the application of Deep Neural Networks (DNNs) for portfolio optimization in financial markets, presenting a comprehensive methodology that leverages advanced machine learning to enhance investment strategies. A diverse dataset, including historical prices, technical indicators, macroeconomic factors, and company fundamentals, is collected from sources like Kaggle. Preprocessing steps, such as handling missing values and applying Min-Max normalization, ensure consistent input for the DNN. An attention mechanism is employed for feature selection, dynamically assigning importance weights to key financial indicators and prioritizing relevant data points. The DNN architecture includes multiple layers with ReLU activations for non-linearity, along with dropout and L2 regularization to prevent overfitting. The model is trained using gradient-based optimization algorithms like Adam, aiming to maximize risk-adjusted returns by minimizing portfolio variance or maximizing the Sharpe ratio. The dataset is divided into training, validation, and test sets, with cross-validation used to improve model robustness. Hyperparameters are fine-tuned for optimal performance. The model’s effectiveness is evaluated through rigorous backtesting against historical data, with performance metrics such as annualized return, Sharpe ratio, and maximum drawdown compared to other indices. The DNN achieved a remarkable accuracy of $\mathbf{9 9. 1 3 \%}$. Finally, the model is deployed in a live trading environment, where continuous monitoring and periodic retraining ensure adaptation to evolving market conditions and sustained portfolio optimization.