A Linear Belief Function Approach to Portfolio Evaluation
This work addresses portfolio evaluation for financial experts, but it appears incremental as it builds on existing linear belief function theory.
The paper tackles the problem of portfolio evaluation by proposing a linear belief function approach to represent and integrate diverse market information and financial knowledge, demonstrating its application with an example of three gold stocks.
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.