Understanding Rankings of Financial Analysts

38 Pages Posted: 27 Oct 2015 Last revised: 11 Nov 2015

See all articles by Artur Aiguzhinov

Artur Aiguzhinov

Universidade do Porto - Faculdade de Economia (FEP)

Ana Paula Serra

Universidade do Porto - Faculdade de Economia (FEP)

Carlos Soares

Universidade do Porto - Faculty of Engineering

Date Written: November 9, 2015

Abstract

The prediction of the most accurate analysts is typically modeled in terms of individual analyst characteristics. This approach has the disadvantage that these data are hard to collect and often unreliable. We follow a different approach in which we characterize the general behavior of rankings of analysts based upon state variables rather than individual analyst characteristics or past accuracy. We use a common learning algorithm, naive Bayes, that we adapted to address the problem of ranking the analysts. Our results show that it is possible to model the relation between the selected variables and the rankings. We show that the uncertainty about future stock performance influences the rankings of the analysts while the macroeconomic variables have the most contribution to the changes in rankings.

Keywords: Financial Analysts; Rankings; State Variables; EPS forecast

JEL Classification: G11

Suggested Citation

Aiguzhinov, Artur and Serra, Ana Paula Sousa Freitas Madureira and Soares, Carlos, Understanding Rankings of Financial Analysts (November 9, 2015). Available at SSRN: https://ssrn.com/abstract=2680301 or http://dx.doi.org/10.2139/ssrn.2680301

Artur Aiguzhinov (Contact Author)

Universidade do Porto - Faculdade de Economia (FEP) ( email )

Rua Roberto Frias
s/n
Porto, 4200-464
Portugal

Ana Paula Sousa Freitas Madureira Serra

Universidade do Porto - Faculdade de Economia (FEP) ( email )

Rua Roberto Frias
s/n
Porto, 4200-464
Portugal

Carlos Soares

Universidade do Porto - Faculty of Engineering ( email )

Rua Dr. Roberto Frias
4200-464 Porto
Portugal

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