Understanding Rankings of Financial Analysts

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.