# Physics Informed Neural Network for Option Pricing

arXiv: https://arxiv.org/abs/2312.06711 · HTML: https://mlsanity.com/paper/2312.06711/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [DGM: A deep learning algorithm for solving partial differential equations](https://mlsanity.com/paper/1708.07469/) (2017)

## Built on by (most cited first, 3 of 3)

- [Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity](https://mlsanity.com/paper/2409.18168/) (2024)
- [MLP, XGBoost, KAN, TDNN, and LSTM-GRU Hybrid RNN with Attention for SPX and NDX European Call Option Pricing](https://mlsanity.com/paper/2409.06724/) (2024)
- [INI-VPINN: A Variational Physics-Informed Neural Network with Implicit Neumann and Interface Handling for Multi-Material Domains with Geometric Singularities](https://mlsanity.com/paper/2606.18032/) (2026)

## Abstract (arXiv)

We apply a physics-informed deep-learning approach the PINN approach to the Black-Scholes equation for pricing American and European options. We test our approach on both simulated as well as real market data, compare it to analytical/numerical benchmarks. Our model is able to accurately capture the price behaviour on simulation data, while also exhibiting reasonable performance for market data. We also experiment with the architecture and learning process of our PINN model to provide more understanding of convergence and stability issues that impact performance.