Deep Learning for Stock Performance Prediction: A Sharpe Ratio-Optimized Approach

Accurate stock performance prediction is critical for portfolio management, risk assessment, and algorithmic trading. Traditional forecasting models often focus on minimizing prediction error but fail to consider risk-adjusted returns, making them suboptimal for real-world investment applications. Recent advances in deep learning have significantly improved financial time series forecasting, yet existing models primarily optimize for accuracy rather than maximizing risk-adjusted performance metrics such as the Sharpe ratio. This study proposes a Sharpe ratio-optimized deep learning framework for stock performance prediction, integrating risk-sensitive forecasting mechanisms directly into model training. By embedding Sharpe ratio-based loss functions, the model prioritizes investment strategies that yield higher returns per unit of risk. The framework utilizes temporal convolutional networks (TCNs) and attention-based transformers, allowing for both short-term price trend detection and long-range dependency modeling. Additionally, reinforcement learning is employed to dynamically adjust portfolio allocation strategies based on evolving market conditions, ensuring adaptability across different asset classes. Empirical results on real-world stock market datasets demonstrate that the proposed model outperforms traditional forecasting approaches in both predictive accuracy and financial performance. The study highlights the importance of integrating risk-sensitive optimization techniques within deep learning-based stock prediction frameworks, offering a more practical and scalable solution for quantitative investment strategies.