Believing Finite-State Cascades in Knowledge-Based Information Extraction
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Intelligent technologies for investing: a review of engineering literature
Intelligent Decision Technologies
Large-scale public R&D portfolio selection by maximizing a biobjective impact measure
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Improving fuzzy knowledge integration with particle swarmoptimization
Expert Systems with Applications: An International Journal
Imaginary numbers for combining linear equation models via Dempster's rule
International Journal of Approximate Reasoning
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This paper proposes a linear belief function (LBF) approach to evaluate portfolio performance. By drawing on the notion of LBFs, an elementary approach to knowledge representation in expert systems is proposed. It is shown how to use basic matrices to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset-pricing models. The authors then appeal to Dempster's rule of combination to integrate the knowledge for assessing the overall belief of portfolio performance and updating the belief by incorporating additional evidence. An example of three gold stocks is used to illustrate the approach