A Linear Belief Function Approach to Portfolio Evaluation
Abstract
By elaborating on the notion of linear belief functions (Dempster 1990; Liu 1996), we propose an elementary approach to knowledge representation for expert systems using linear belief functions. We show 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. We then appeal to Dempster's rule of combination to integrate the knowledge for assessing an overall belief of portfolio performance, and updating the belief by incorporating additional information. We use an example of three gold stocks to illustrate the approach.
Keywords
Cite
@article{arxiv.1212.2473,
title = {A Linear Belief Function Approach to Portfolio Evaluation},
author = {Liping Liu and Catherine Shenoy and Prakash P. Shenoy},
journal= {arXiv preprint arXiv:1212.2473},
year = {2012}
}
Comments
Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)