# A Topological Approach to Parameterizing Deep Hedging Networks

arXiv: https://arxiv.org/abs/2510.16938 · HTML: https://mlsanity.com/paper/2510.16938/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 19 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists](https://mlsanity.com/paper/1710.04019/) (2017)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Deep Hedging of Derivatives Using Reinforcement Learning](https://mlsanity.com/paper/2103.16409/) (2021)
- [Fast Deep Hedging with Second-Order Optimization](https://mlsanity.com/paper/2410.22568/) (2024)
- [Deep Hedging Under Non-Convexity: Limitations and a Case for AlphaZero](https://mlsanity.com/paper/2510.01874/) (2025)

## Abstract (arXiv)

Deep hedging uses recurrent neural networks to hedge financial products that cannot be fully hedged in incomplete markets. Previous work in this area focuses on minimizing some measure of quadratic hedging error by calculating pathwise gradients, but doing so requires large batch sizes and can make training effective models in a reasonable amount of time challenging. We show that by adding certain topological features, we can reduce batch sizes substantially and make training these models more practically feasible without greatly compromising hedging performance.