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Neural Network Learning of Black-Scholes Equation for Option Pricing

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  • Daniel de Souza Santos
  • Tiago Alessandro Espinola Ferreira

Abstract

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.

Suggested Citation

  • Daniel de Souza Santos & Tiago Alessandro Espinola Ferreira, 2024. "Neural Network Learning of Black-Scholes Equation for Option Pricing," Papers 2405.05780, arXiv.org.
  • Handle: RePEc:arx:papers:2405.05780
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    File URL: http://arxiv.org/pdf/2405.05780
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