Neural Network Learning of Black-Scholes Equation for Option Pricing
This provides a more accurate method for short-term call option price forecasting in financial markets, though it is incremental as it applies an existing neural network approach to a known equation.
The paper tackled stock option pricing by using neural networks to solve the Black-Scholes equation with real-world data from Brazilian companies, resulting in more accurate forecasts than traditional analytical solutions.
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.