8.5 Summary
This chapter has proposed an innovative, intelligent agent-based stock advisory application: the iJADE Stock Advisor. The major contributions of this chapter can be summarized under two headings: research and applications.
From the research point of view, this chapter demonstrates a feasible and efficient solution (iJADE Stock Advisor) for automatic intelligent agent-based stock prediction. With respect to the stock forecasting engine, this chapter adopts the hybrid RBF network (HRBFN) model for stock prediction. Through the integration of the iJADE framework with the HRBFN in the iJADE Stock Analyst agent, this chapter illustrates the efficiency and effectiveness of the iJADE Stock Advisor for online stock prediction. With respect to the interactive and mobile agent-based stock advisory and prediction processes, the chapter illustrates how intelligent agent technology can be successfully integrated with AI technology for online stock prediction.
From an applications point of view, this chapter demonstrates how the HRBFN model can be successfully integrated with mobile agent technology to provide a truly intelligent, mobile, and interactive stock advisory solution. Through the implementation of the iJADE Stock Advisor (with the adoption of our newly developed iJADE framework) and the adoption of the 10-year stock pricing information (1990–1999) for various system tests and evaluations, it has been demonstrated how intelligent agent technology can be successfully and fully integrated into other support technologies (such as recurrent neural networks for time series prediction, mobile agent paradigms as agent communication, etc.) to open a new era of intelligent mobile e-business (iMEB) for the development of future e-commerce.
As quoted at the beginning of the chapter, Phillips Brooks is right in saying that a good advisor should not tell us how to act, but rather should lead us (and leave us) to make our own decisions. It is anticipated that our latest and future development of Cogito advisory agents can possess this kind of caliber.
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© 2006 Springer-Verlag Berlin Heidelberg
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(2006). iJADE Stock Advisor — An Intelligent Agent-Based Stock Prediction System Using the Hybrid RBF Recurrent Network. In: Fuzzy-Neuro Approach to Agent Applications. Springer Series in Agent Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-30984-5_8
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DOI: https://doi.org/10.1007/3-540-30984-5_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21203-4
Online ISBN: 978-3-540-30984-0
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