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According to a recent study led by Assistant Professor of Accounting Omri Even-Tov from the University of California, Berkeley, it was found that there is a positive correlation between an analyst’s ability to forecast the company’s earnings if that individual shared the same name as the CEO of their firm.
For the experiment, the study examined the annual earning forecasts issued by analysts on Wall Street between the period 1992 and 2018. The study would then calculate the earnings forecast accuracy of each analyst compared to the others at the firm.
It found that relative to those who did not share the same name as the CEO (unmatched), analysts who did share the same first name predicted earnings with greater accuracy (matched).
The study conjectures that the greater affinity translated into a greater willingness from the CEO to share less commonly known or even private information with the analyst – which would then allow the analyst to obtain more knowledge – thus increases the accuracy of their forecasts.
The study’s hypothesis is further supported in case the CEO of the company were to be replaced – meaning some previously unmatched analysts became matched and vice versa.
The study seemingly rules out gender and ethnic ties of matched analysts and has found that the forecast accuracy of matched analysts still remained significantly greater than that of unmatched analysts. In another test, all analyst-CEO pairs in which both are either women or have the same ethnicity were excluded. Again, matched pairs maintained a significantly greater forecast accuracy.
The study, however, does note that there is less salience for those who possess a more common name, due to how often the individuals are likely to be meeting people who share their first names.
The study places the reasoning of this down to a “manifestation of implicit egotism,” a hypothesis which states that humans have an unconscious preference for things they associate with themselves.