iJADE Stock Advisor: An Intelligent Agent Based Stock Prediction System Using Hybrid RBF Recurrent Network

Abstract

Financial predictions, such as stock forecasts, is always one of the hottest topics for research studies and commercial applications. With the rapid growth of Internet technology in recent years, e-finance has become a vital application of e-commerce. However, in this "sea" of information, made available through the Internet, an "intelligent" financial web-mining and stock prediction system can be a key to success. In this paper, the author introduces the iJADE Stock Advisor-an intelligent agent-based stock prediction system using our proposed hybrid radial basis-function recurrent network (HRBFN). By using ten-year stock pricing information (1990-1999), consisting of 33 major Hong Kong stocks for testing, the iJADE Stock Advisor has achieved promising results in terms of efficiency, accuracy, and mobility as compared with other contemporary stock prediction models. Also, various analyzes on this stock advisory system have been performed: including round trip time (RTT) analysis, window-size evaluation test (for both long-term trend and short-term prediction), and stock prediction performance test.

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