Option price forecasting using neural networks

• Published in: Omega (Oxford) Vol. 28; no. 4; pp. 455 - 466
• Main Authors: Yao, Jingtao, Li, Yili, Tan, Chew Lim
• Format: Journal Article
• Language: English
• Published: Exeter Elsevier Ltd 01.08.2000; Elsevier; Pergamon Press; Pergamon Press Inc
• Series: Omega
• Subjects: Applied sciences; Back propagation; Black–Scholes model; Exact sciences and technology; Forecasting; Inventory control, production control. Distribution; Neural networks; Neural networks Forecasting Option pricing Black-Scholes model; Operational research and scientific management; Operational research. Management science; Option pricing; Options markets; Rates of return; Securities prices; Stochastic models; Studies; Volatility; Neural networks; Option pricing; Black–Scholes model; Forecasting; Backpropagation; Pricing; Economic forecasting; Risk; Neural network; Economic data
• ISSN: 0305-0483; 1873-5274
• DOI: 10.1016/S0305-0483(99)00066-3

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.