Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
Year: 2025
Type: article
Abstract: This paper explores the application of deep neural networks (DNNs) as an alternative to the traditional Black–Scholes model for predicting European put option prices. Using synthetic datasets generated under the Black–Scholes framework, the proposed DNN achieved strong predictive performance, with a Mean Squared Error (MSE) of 0.0021 and a coefficient of determination (R2) of 0.9533. This study highlights the scalability and adaptability of DNNs to complex financial systems, offering potential applications in real-time risk management and the pricing of exotic derivatives. While synthetic datasets provide a controlled environment, this study acknowledges the challenges of extending the model to real-world financial data, paving the way for future research to address these limitations.
Source: Algorithms
DOI: 10.3390/a18030161
Authors: Zakaria Elbayed, Abdelmjid Qadi EI Idrissi
Institution: Université Hassan 1er
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4408338586