Analysing the Returns-Earnings Relationship: Dempster-Shafer Theory and Evolutionary Computation Based Analyses Using the Classification and Ranking Belief Simplex

Abstract

This chapter considers the problem of understanding the relationship between company stock returns and earnings components, namely accruals and cash flows. The problem is of interest, because earnings are a key output of the accounting process, and investors have been shown to depend heavily on earnings in their valuation models. This chapter offers an elucidation on the application of a nascent data analysis technique, the Classification and Ranking Belief Simplex (CaRBS) and a recent development of it, called RCaRBS, in the returns-earnings relationship problem previously described. The approach underpinning the CaRBS technique is closely associated with uncertain reasoning, with methodological rudiments based on the Dempster-Shafer theory of evidence. With the analysis approach formed as a constrained optimisation problem, details on the employment of the evolutionary computation based technique trigonometric differential evolution are also presented. Alongside the presentation of results, in terms of model fit and variable contribution, based on a CaRBS classification-type analysis, a secondary analysis is performed using a development RCaRBS, which is able to perform multivariate regression-type analysis. Comparisons are made between the results from the two different types of analysis, as well as briefly with more traditional forms of analysis, namely binary logistic regression and multivariate linear regression. Where appropriate, numerical details in the construction of results from both CaRBS and RCaRBS are presented, as well emphasis on the graphical elucidation of findings.