| Summary: | 碩士 === 元智大學 === 資訊管理研究所 === 91 === Asset allocation is the choice of how much to invest among classes of assets to achieve the best portfolio given the investor’s objectives and investment constraints. The approach that explores the nature of risk-return trade-off and the principles of rational portfolio selection associated with it was first proposed by the Markowitz Model. In practice, the number of required data inputs for applying Markowitz model with multi-scenario analysis is extremely large. In addition, the sophisticated valuation models and computations complicate the situation. We therefore proposed an intelligent agent system for multi-scenario asset allocation decision support. By way of many intelligent agents’ co-work and coordination, the large amount of data computation and various jobs of analysis can be flexibly performed. In this thesis, we defined the roles and jobs of different agents, including: financial data collection agent, scenario-defined agent, scenario probability forecasting agent, investor agent, asset evaluation agent, and asset allocation decision support agent, to simulate multi-scenario asset allocation decisions.
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