Deep Learning for Option Pricing: A Neural Network Approach to Black-Scholes Valuation

Abstract

Option pricing is a fundamental problem in financial engineering, with the Black-Scholes model serving as a cornerstone for theoretical valuation. However, traditional models have limitations when applied to real-world market conditions, where assumptions such as constant volatility may not hold. This paper explores a data-driven approach using a deep learning model to approximate Black-Scholes option prices. A neural network is trained on synthetic data generated using the Black-Scholes formula, leveraging techniques such as batch normalization, dropout regularization, and early stopping to improve generalization. The results demonstrate that the proposed model can accurately predict option prices and provide a potential foundation for extending deep learning to more complex financial derivatives.