In this post, we looked at the impact of prior work experience on analyst forecasts. But of course, it’s not only what you did before you became an analyst, it’s also whom you know.  This question is explore by Fang and Huang in their 2017 Review of Financial Studies paper “Gender and Connections among Wall Street Analysts”.

Analyst connections

In their paper, Fang and Huang analyze whether having ties with corporate board members affects the performance and career paths of female and male analysts. To measure these ties, the authors follow a well-established methodology of Cohen, Frazzini, and Malloy (2008) (who explored a similar question for fund managers and board members). Using information on the educational background of analysts and board members, they construct three connection variables:

  • Connect1: analyst and director/officer attended the same university

  • Connect2: analyst and officer/director attended the same school within the same university

  • Connect3: analyst and director/officer attended the same school within the same university during an overlapping period

What are connections good for?

The main hypothesis of the paper is that connected analysts should perform better, i.e., make more accurate forecasts than their peers. Consequently, they compute the demeaned absolute forecast error (Fore_error) as the difference between the absolute forecast error of an analyst and the mean forecast error of all analysts covering a specific firm, relative to the mean forecast error. The smaller this measure is, the more accurate is the respective analyst.

The impact of connections on analyst performance

To examine the relation between connections and performance, Fang and Huang run a linear regression with Fore_error as the dependent variable. The independent variables are the dummy variables Connect1, Connect2, and Connect3, as well as a gender dummy (one for male analysts, zero for female analysts) and their interaction terms. Table 1 displays the results for Connect1.

Table 1: Analyst forecast accuracy and connection
Connection -6.4*** -2.6*** -2.4***
Gender -0.7 0.3  
Gender * Connection   -4.4*** -4.6***

*** indicates significance at the 1%-level. All coefficients are multiplied by 100 for ease of exposition. Source: Table 4 Panel A, Fang and Huang (2017).

Table 1 shows that connection improves performance by 6.4%. In general, both male and female analysts have the same forecast accuracy (since the gender dummy is not significant). But column 2 and 3 show that male analysts profit more: The coefficient of connection in -2%, which corresponds to the effect for female analysts (meaning an improvement of 2%). The interaction term gives the additional improvement for male analysts, showing again an improvement of 4%. Hence, the impact of connections is three times larger for male analysts than for female analysts. The results using the other measures of connection are similar.

Do connections foster careers?

Given that connections help analysts to perform better, it is natural to ask whether connections also help them in their careers. To explore this question, the authors analyze the relation between receiving an All American (AA) election and %Connection: AAs are more highly paid, and it is a key marker of the career success of an analyst. %Connection measures the percentage of covered firms to which an analyst is connected. Table 2 displays the results, again using the Connect1 measure.

  Table 2: AA election and connectedness
%Connection 0.479*** 1.631***
Fore_error -0.591*** -0.299
%Connection * Fore_error 0.068* -0.343**

***, **, and * indicate significance on a 1%, 5%, and 10% level, respectively. Source: Table 6, Fang and Huang (2017).

Column 1 (2) of Table 2 reports the results for male (female) analysts. A large percentage of collections increases the likelihood of an AA election for both male and female analysts. But this is where the similarities end:

  • Male analysts are “punished” for bad performance (negative coefficient estimate for Fore_error), but less so when they are better connected.
  • Female analysts are not “punished” for bad performance if they are unconnected. But the better they are connected, the more are they punished for mistakes.

In summary, connections are a partial substitute to performance when it comes to career success for male analysts, but a complement to performance for female analysts.

Take-away: Stay connected!

People benefit from their connections, and Wall Street analysts are no exception: the better connected they are, the better they do their jobs. But when it comes to career succes, gender starts to matter: male analysts benefit more. However, the effect of connection is positive and significant. So: Stay connected!

Author: Monika Gehde-Trapp