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A Linear Belief Function Approach to Portfolio Evaluation

dc.contributor.authorLiu, Liping
dc.contributor.authorShenoy, Catherine
dc.contributor.authorShenoy, Prakash P.
dc.date.accessioned2004-12-14T22:09:38Z
dc.date.available2004-12-14T22:09:38Z
dc.date.issued2003-08
dc.identifier.citationU. Kjærulff and C. Meek (eds.), Uncertainty in Artificial Intelligence, 2003, pp. 370--377, Morgan Kaufmann, San Francisco, CA
dc.identifier.isbn0-127-05664-5
dc.identifier.urihttp://hdl.handle.net/1808/154
dc.descriptionThis paper is a condensed 8-pp version of a longer paper titled "Knowledge Representation and Integration for Portfolio Evaluation Using Linear Belief Functions," School of Business Working Paper, December 2003, that has been conditionally accepted in Nov. 2004 for publication in IEEE Transactions on Systems, Man & Cybernetics, Part A.
dc.description.abstractWe show how to use linear belief functions to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, dis-tributional assumptions, linear relations, and em-pirical asset pricing models. We then appeal to Dempster’s rule of combination to integrate the knowledge for assessing an overall belief on portfolio performance, and to update this belief by incorporating additional information.
dc.format.extent1727612 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherMorgan Kaufmann Publishers
dc.subjectNormal belief functions
dc.subjectMultivariate normal distribution
dc.subjectDempster-Shafer belief function theory
dc.subjectPortfolio theory
dc.titleA Linear Belief Function Approach to Portfolio Evaluation
dc.typeBook chapter
kusw.oastatusna
dc.identifier.orcidhttps://orcid.org/0000-0002-8425-896X
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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