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

Markus Schäfer; Thomas Müller; Hugo Fernandez

doi:10.62177/apemr.v2i2.210

Authors

Markus Schäfer RWTH Aachen University
Thomas Müller University of Barcelona
Hugo Fernandez University of Barcelona

DOI:

https://doi.org/10.62177/apemr.v2i2.210

Keywords:

Stock Performance Prediction, Deep Learning, Sharpe Ratio Optimization, Risk-Aware Forecasting, Portfolio Management, Reinforcement Learning

Abstract

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.

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