Extraction of rules from artificial neural networks for nonlinear regression

10.1109/TNN.2002.1000125IEEE Transactions on Neural Networks133564-577ITNN

Computational intelligence methods for rule-based data understanding

10.1109/JPROC.2004.826605Proceedings of the IEEE925771-805IEEP

A Simple Method of Forecasting Option Prices Based on Neural Networks

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.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000269972300059&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceEngineering, IndustrialEICPCI-S(ISTP)