Neural Network Learning of Black-Scholes Equation for Option Pricing

TL;DR

This paper introduces a neural network approach to solve the Black-Scholes Equation using real-world stock options data, achieving more accurate short-term option price forecasts than traditional methods.

Contribution

It presents a novel neural network-based method for solving the Black-Scholes Equation with real market data, improving option pricing accuracy.

Findings

Neural networks can learn to solve the Black-Scholes Equation for real stock options.

The proposed method outperforms traditional analytical solutions in forecasting accuracy.

The approach enables effective short-term option price predictions.

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.