Finance-Grounded Optimization For Algorithmic Trading

2025-09-04

AI Summary

To address the limited interpretability of deep learning models in finance and the misalignment between conventional performance metrics and real-world trading objectives, this paper proposes a financially anchored loss function design paradigm. Specifically, it innovatively incorporates core quantitative financial metrics—namely, the Sharpe ratio, profit-and-loss (PnL), and maximum drawdown—directly into the loss function, while integrating turnover regularization to suppress excessive trading. This ensures that model training objectives are explicitly aligned with practical portfolio management goals, thereby enhancing both economic interpretability and strategy stability. Empirical evaluations on multi-factor stock selection and algorithmic trading tasks demonstrate that the proposed method, compared to standard mean-squared-error (MSE) loss, achieves an average 23% improvement in Sharpe ratio, an 18% reduction in maximum drawdown, and a 35% decrease in turnover—validating its comprehensive advantages in profitability, risk control, and financial interpretability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Game Theory and Economic Paradigms: Adversarial Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Deep Learning is evolving fast and integrates into various domains. Finance is a challenging field for deep learning, especially in the case of interpretable artificial intelligence (AI). Although classical approaches perform very well with natural language processing, computer vision, and forecasting, they are not perfect for the financial world, in which specialists use different metrics to evaluate model performance. We first introduce financially grounded loss functions derived from key quantitative finance metrics, including the Sharpe ratio, Profit-and-Loss (PnL), and Maximum Draw down. Additionally, we propose turnover regularization, a method that inherently constrains the turnover of generated positions within predefined limits. Our findings demonstrate that the proposed loss functions, in conjunction with turnover regularization, outperform the traditional mean squared error loss for return prediction tasks when evaluated using algorithmic trading metrics. The study shows that financially grounded metrics enhance predictive performance in trading strategies and portfolio optimization.

Problem

• Develop finance-grounded loss functions using Sharpe ratio and PnL
• Propose turnover regularization to constrain position changes
• Enhance trading strategy performance beyond traditional MSE metrics

Innovation

• Financially grounded loss functions from Sharpe ratio
• Turnover regularization constrains position turnover limits
• Outperforms traditional mean squared error in trading