Abstract
AbstractThis paper describes a framework for the integration of a rule‐based system capable of identifying an investor's risk preference into a quantitative portfolio model based on risk and expected return. By inferring rules consisting of an investor's objective and subjective risk preferences, the integrated methodology provides the assets suitable for the preferences. Through investment in the portfolio composed of the assets, the investor is able to obtain the following benefits: reduction of costs and time spent to determine target assets, and alleviation of anxiety from ‘out‐of‐favor’ assets.The framework is applied to the development of a knowledge‐based portfolio system for constructing an investor's preference‐oriented portfolio. In the procedure of the system for finding an optimal portfolio, the system uses an artificial intelligence method of a case‐based reasoning to obtain preference thresholds for an investor when the investor's past investment records are available. Experimental results show that the framework contributes significantly to the construction of a better portfolio from the perspective of an investor's benefit/cost ratio than that produced by the existing portfolio models. Copyright © 2004 John Wiley & Sons, Ltd.
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