Generalizing the number of states in Bayesian belief propagation, as applied to portfolio management.
Date
1996
Authors
Kruger, Jan Walters.
Journal Title
Journal ISSN
Volume Title
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Abstract
This research report describes the use or the Pearl's algorithm in Bayesian belief
networks to induce a belief network from a database. With a solid grounding in
probability theory, the Pearl algorithm allows belief updating by propagating
likelihoods of leaf nodes (variables) and the prior probabilities.
The Pearl algorithm was originally developed for binary variables and a
generalization to more states is investigated.
The data 'Used to test this new method, in a Portfolio Management context, are the
Return and various attributes of companies listed on the Johannesburg Stock
Exchange ( JSE ).
The results of this model is then compared to a linear regression model. The
bayesian method is found to perform better than a linear regression approach.
Description
A research report submitted to the Faculty of Science, University of the
Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the
degree of Master of' Science.
Keywords
BAYESIAN STATISTICAL DECISION THEORY., PROBABILITIES., PORTFOLIO MANAGEMENT--MATHEMATICAL MODELS., ALGORITHMS.