# Hedging with Linear Regressions and Neural Networks

arXiv: https://arxiv.org/abs/2004.08891 · HTML: https://mlsanity.com/paper/2004.08891/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Distributional Adversarial Attacks and Training in Deep Hedging](https://mlsanity.com/paper/2508.14757/) (2025)
- [Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging](https://mlsanity.com/paper/2510.09247/) (2025)

## Abstract (arXiv)

We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimise the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. However, a similar benefit arises by simple linear regressions that incorporate the leverage effect.