A financial option pricing model based on learning algorithms

Abstract

The Black-Scholes option pricing model provides theoretical values for the put and call options when do not pay dividends. In this work we compare these theoretical values with the obtained ones by a neural network that is applied to the option pricing market on the Ibex-35 index. Our result shows that the neural network is superior by the Black-Scholes model analyzing the kindness of the adjustment for the ME (mean of the option price errors) and RMSE (root mean square error).