Towards an intelligent web-based agent system (iWAF) for e-Finance application

E-Finance is a complex challenge, requiring complex strategy development and technology implementation. AI techniques applied to stock market prediction include: expert system, fuzzy logic, neural network, genetic algorithm and some statistical model. But neither one can give confidence for investors to rely on due to lack of unbiased market mo vement analysis, trend prediction, human behaviour and psychological implication studies. In this study, we proposed a multi agent framework which combine all the advantages of different forecasting methods, for predicting the best stock marketing timing. The system contains five forecasting agents using fundamental, technical and statistical analyses to forecast the market. Each agent is a domain expert in a particular forecasting method and work together to fill the knowledge gap. The paper focuses on the construct of a coordinate agent to collect all the recommendations from different agents to produce the final result. As the proposed framework contains different forecasting techniques, we anticipate that the system can provide different measure of the financial market and allow investors to position their investment better and more effective.