AI Optimizes Algorithmic Trading: Leveraging Physics-Informed Neural Networks
Published:Dec 14, 2025 14:20
•1 min read
•ArXiv
Analysis
This research explores the application of physics-informed neural networks to solve Hamilton-Jacobi-Bellman (HJB) equations in the context of optimal execution, a crucial area in algorithmic trading. The paper's novelty lies in its multi-trajectory approach, and its validation on both synthetic and real-world SPY data is a significant contribution.
Key Takeaways
- •The paper uses physics-informed neural networks to tackle the HJB equation for optimal execution.
- •The research validates its approach using both synthetic and real-world market data (SPY).
- •This work has potential implications for improving the efficiency of algorithmic trading strategies.
Reference
“The research focuses on optimal execution using physics-informed neural networks.”