# Neural networks for option pricing and hedging: a literature review

arXiv: https://arxiv.org/abs/1911.05620 · HTML: https://mlsanity.com/paper/1911.05620/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 30 of 40)

- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations](https://mlsanity.com/paper/2112.08125/) (2021)
- [Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality](https://mlsanity.com/paper/2106.08900/) (2021)
- [Multi-Asset Spot and Option Market Simulation](https://mlsanity.com/paper/2112.06823/) (2021)
- [Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates](https://mlsanity.com/paper/2407.08100/) (2024)
- [Chaotic Hedging with Iterated Integrals and Neural Networks](https://mlsanity.com/paper/2209.10166/) (2022)
- [Deep neural network expressivity for optimal stopping problems](https://mlsanity.com/paper/2210.10443/) (2022)
- [Robust Risk-Aware Option Hedging](https://mlsanity.com/paper/2303.15216/) (2023)
- [A Quantum Generative Adversarial Network for distributions](https://mlsanity.com/paper/2110.02742/) (2021)
- [Interpretability in deep learning for finance: a case study for the Heston model](https://mlsanity.com/paper/2104.09476/) (2021)
- [Backdoor Attack and Defense for Deep Regression](https://mlsanity.com/paper/2109.02381/) (2021)
- [An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning](https://mlsanity.com/paper/2408.13222/) (2024)
- [Fast Deep Hedging with Second-Order Optimization](https://mlsanity.com/paper/2410.22568/) (2024)
- [Non-convergence to the optimal risk for Adam and stochastic gradient descent optimization in the training of deep neural networks](https://mlsanity.com/paper/2503.01660/) (2025)
- [Pricing options on flow forwards by neural networks in Hilbert space](https://mlsanity.com/paper/2202.11606/) (2022)
- [Applying Informer for Option Pricing: A Transformer-Based Approach](https://mlsanity.com/paper/2506.05565/) (2025)
- [Solving the optimal stopping problem with reinforcement learning: an application in financial option exercise](https://mlsanity.com/paper/2208.00765/) (2022)
- [Machine learning for option pricing: an empirical investigation of network architectures](https://mlsanity.com/paper/2307.07657/) (2023)
- [Hedging with memory: shallow and deep learning with signatures](https://mlsanity.com/paper/2508.02759/) (2025)
- [Hedging option books using neural-SDE market models](https://mlsanity.com/paper/2205.15991/) (2022)
- [Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate](https://mlsanity.com/paper/2412.11257/) (2024)
- [Distributional Adversarial Attacks and Training in Deep Hedging](https://mlsanity.com/paper/2508.14757/) (2025)
- [Deep Calibration With Artificial Neural Network: A Performance Comparison on Option Pricing Models](https://mlsanity.com/paper/2303.08760/) (2023)
- [Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resampling](https://mlsanity.com/paper/2409.06514/) (2024)
- [Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging](https://mlsanity.com/paper/2510.09247/) (2025)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Trading Signals In VIX Futures](https://mlsanity.com/paper/2103.02016/) (2021)
- [Beyond Surrogate Modeling: Learning the Local Volatility Via Shape Constraints](https://mlsanity.com/paper/2212.09957/) (2022)
- [Pricing European Options with Google AutoML, TensorFlow, and XGBoost](https://mlsanity.com/paper/2307.00476/) (2023)
- [American Option Pricing using Self-Attention GRU and Shapley Value Interpretation](https://mlsanity.com/paper/2310.12500/) (2023)

## Abstract (arXiv)

Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.