Introduction to artificial neural systems
Introduction to artificial neural systems
Pricing options in hong kong market based on neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Improving option pricing with the product constrained hybrid neural network
IEEE Transactions on Neural Networks
Simulating wheat yield in New South Wales of Australia using interpolation and neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
| Hi-index | 0.00 |
Options are an important financial derivative for the investors to control their investment risks in the security markets. The forecasting activity should realistically identify the option price in the future without knowing underlying asset price in advance. In this paper, a simple method of forecasting option prices based on neural networks is presented. We modify the traditional option pricing methods, enabling them to be eligible for forecasting the option prices. Then we employ the neural networks to further decrease the forecasting errors of the modified conventional methods. Finally, the experimental studies are conducted on the data of the Hong Kong option market, and the results demonstrate that the neural networks are able to improve the forecasting performance considerably. Conclusively, our neural network methods on option forecasting are fairly effectual in practice.