Sell Side School Ties: Internet Appendix
Harvard Business School; National Bureau of Economic Research (NBER)
AQR Capital Management, LLC
Harvard Business School; National Bureau of Economic Research (NBER)
April 4, 2016
We study the impact of social networks on agents’ ability to gather superior information about firms. Exploiting novel data on the educational backgrounds of sell side equity analysts and senior officers of firms, we test the hypothesis that analysts’ school ties to senior officers impart comparative information advantages in the production of analyst research. We find evidence that analysts outperform on their stock recommendations when they have an educational link to the company. A simple portfolio strategy of going long the buy recommendations with school ties and going short buy recommendations without ties earns returns of 6.60% per year. We test whether Regulation FD, targeted at impeding selective disclosure, constrained the use of direct access to senior management. We find a large effect: pre-Reg FD the return premium from school ties was 9.36% per year, while post-Reg FD the return premium is nearly zero and insignificant. In contrast, in an environment that did not change selective disclosure regulation (the UK), the analyst school-tie premium has remained large and significant over the entire sample period.
The paper “Sell Side School Ties” may be found at http://ssrn.com/abstract=1095808.
Sell Side School Ties: Internet Appendix – Introduction
This appendix contains a number of additional tests, robustness checks, and summary statistics for Sell Side School Ties. We organize by the accompanying tables and figures. Thus each subsequent section corresponds to a table (or figure) in the appendix.
Table A1 – Summary Statistics on Links
Table A1 presents the percentage of linked stocks, the number of linked stocks, and the number of stocks covered for different categories of analysts. Note that we classify an analyst-stock pair as “linked” if an analyst attended at least one common school with at least one senior manager (or board member), and “unlinked” if an analyst did not attend a single common school with any senior manager (or board member). Also note that we require education data on at least one senior manager in order to define a valid analyst-stock link or non-link.1 Analysts, as a whole, are linked to an average of 18% of the stocks they cover; since the average analyst covers 6.2 stocks, this translates to just over 1 linked stock on average per analyst. Analysts who attended a school in the top 10 in terms of the number of links to firms are tied to an average of 35% of the stocks they cover, or roughly 2 stocks on average. Similarly, analysts who attended any university ranked in the top 40 by US News and World Report in the prior year have ties to 28% of the firms they cover. Meanwhile analysts from Ivy League schools are actually linked to slightly fewer stocks than the average analyst. Finally, there are no significant differences across any of the sub-categories in the percentage of linked stocks Pre- and Post-Regulation FD, suggesting that the population of analysts is unlikely to have changed over time in a way that is correlated with sell side school ties.
Table A2 – Pre- and Post-Reg FD
Table A2 provides evidence of the returns on buy recommendations pre- and post-Reg FD. Panel A indicates that the large returns to school ties for buy recommendations are concentrated in the pre-Reg FD period. Specifically, the school tie premium in the pre-Reg FD period ranges between 68 to 78 basis points per month, or 8.16% (t=4.35) to 9.36% (t=3.50) per year.2 Post-Regulation FD, this difference is only 14 to 26 basis points per month, and is statistically indistinguishable from zero. Panel B also reports results for abnormal returns following buy recommendations, obtaining the identical pattern of large and significant abnormal returns pre-Reg FD, and small and insignificant abnormal returns post-Reg FD.
Table A3 – Summary Statistics for UK Sample
Table A3 provides summary statistics on the UK sample of firms and analysts used in Table VII. The table mimics the setup of Table I in the paper, which provides these same statistics for the main US sample.
Table A4 – School Links and All-Star
Another way to quantify the value of the social networks we isolate in this paper is to test the extent to which sell side school ties predict the probability of that analyst’s becoming an All-Star. As in our prior tests, All-Star status is defined as being listed as an “All-Star” in the October issue of Institutional Investor magazine in a given year. All- Star status is a sought-after designation among analysts, and is typically associated with higher-compensation (Stickel (1992)).3 To assess the predictive power of an analyst’s network, we regress a dummy variable for All-Star status in a given year on the average number of school ties per analyst per year (Num Link) plus a host of control variables at the analyst- and stock-level. The dependent variable is a dummy variable equal to one if the analyst was voted as an All-Star analyst for that year. We employ a similar set of control variables as in Table IV, with the exception that affiliation status is now measured as the average percentage of stocks (over the year) in an analyst’s portfolio that have an underwriting relationship with the analyst’s brokerage. We also include a control variable for covered firm size, equal to the average size of the firms covered by the analyst in that year. All observations are at the analyst-year level; fixed effects at the year, analyst, and broker level are included where indicated, and all standard errors are adjusted for clustering by year. We run these regressions using both an OLS and a probit framework. We report both in Table A4.
Table A4 reports the coefficient estimates from these predictive regressions. Columns 1-8 are OLS panel regressions, while Column 9 is a probit regression with random effects (given the known statistical problems associated with fixed effects in nonlinear panel data estimation models (Greene (2003))). The coefficients on Linked to Mgmt indicate that the number of sell side school ties is a strong positive predictor of the likelihood of being an All-Star. The coefficient on connections in Column 1 implies that a one standard deviation increase in connections increases the probability of being an All-Star by nearly 50%, from 9.2% to 13.6%. Columns 3-5 show that analysts who attended Ivy League schools or the most linked schools in our sample are more likely to be All Star analysts. These school-specific effects, though, have almost no impact on the magnitude or significance of the effect of the specific links of analysts to the management of firms that they cover. Columns 7-9 illustrate the effect of Reg FD on this result: we include a post-Reg FD dummy variable plus an interaction term (Link Mgmt*post-Reg FD) designed to capture the predictive impact of the number of school ties on All-Star status in the post-Reg FD time period.4 Once again the interaction term is strongly negative, and the combined effect ([Link Mgmt*post-Reg FD]+[Linked to Mgmt]) is close to zero and insignificant, indicating that sell side school ties have no effect on being an All-Star in the post-Reg FD period. The fact that school ties predict All-Star status only before the imposition of Reg FD further highlights the value of social networks precisely during those times when selective disclosure is least inhibited.
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