Neural Network Learning of Black-Scholes Equation for Option Pricing
<|reference_start|>Neural Network Learning of Black-Scholes Equation for Option Pricing: One of the most discussed problems in the financial world is stock option pricing. The Black-Scholes Equation is a Parabolic Partial Differential Equation which provides an option pricing model. The present work proposes an approach based on Neural Networks to solve the Black-Scholes Equations. Real-world data from the stock options market were used as the initial boundary to solve the Black-Scholes Equation. In particular, times series of call options prices of Brazilian companies Petrobras and Vale were employed. The results indicate that the network can learn to solve the Black-Sholes Equation for a specific real-world stock options time series. The experimental results showed that the Neural network option pricing based on the Black-Sholes Equation solution can reach an option pricing forecasting more accurate than the traditional Black-Sholes analytical solutions. The experimental results making it possible to use this methodology to make short-term call option price forecasts in options markets.<|reference_end|>
@article{santos2024neural,
    title={Neural Network Learning of Black-Scholes Equation for Option Pricing},
    author={Daniel de Souza Santos and Tiago Alessandro Espinola Ferreira},
    journal={arXiv preprint arXiv:2405.05780},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05780},
    primaryClass={cs.LG q-fin.CP q-fin.PR}
}