Option price forecasting using neural networks

In this research, forecasting of the option prices of Nikkei 225 index futures is carried out using backpropagation neural networks. Different results in terms of accuracy are achieved by grouping the data differently. The results suggest that for volatile markets a neural network option pricing model outperforms the traditional Black-Scholes model. However, the Black-Scholes model is still good for pricing at-the-money options. In using the neural network model, data partition according to moneyness should be applied. Those who prefer less risk and less returns may use the traditional Black-Scholes model results while those who prefer high risk and high return may choose to use the neural network model results. (C) 2000 Elsevier Science Ltd. All rights reserved.