An Empirical Examination of Sell-Side Brokerage Analysts’ Published Research, Concierge Services, and High-Touch Services

Abstract

This paper uses a proprietary panel dataset to categorize and quantify the activities that sell-side brokerage analysts use to build and sustain their network of buy-side client relations. We then examine the marginal impact of these activities on key analyst outcome metrics identified by prior literature. Our findings highlight the importance of two previously unstudied client-service activities – high-touch phone calls and non-deal road shows – and suggest that analysts may face a tradeoff between producing accurate earnings research and increasing the values of key performance measures that gauge the strength of their network of client relations.

Keywords:

• Sell-side analysts
• Analyst ratings
• Brokerage commissions
• Earnings forecasts

Acknowledgements

We are grateful to two anonymous reviewers and David Veenman for their valuable input on earlier versions of this paper. We also appreciate useful comments from Ray Ball, Liz Chuk, Marcus Kirk, Nancy Kotzian, Stepannie Larocque, Roby Lehavy, Jake Levine, Ryan McDonough, Greg Miller, Pat O’Brien, Andy Pickup, Jerry Zimmerman, and seminar participants at Columbia, Florida, George Washington, Georgia, LSE, Michigan, Northwestern, Notre Dame, UBC, Vanderbilt, Washington, Waterloo, and Wharton, as well as practitioners at The Wall Street Transcript’s 2014 Broker-Vote Summit. We especially thank those at the anonymous sample broker for providing data. We are grateful for the financial support of the Orfalea College of Business and the Division of Research at Harvard Business School. We retain responsibility for any errors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Additional (untabulated) analyses indicate that our sample analysts approximate the median I/B/E/S analyst along a host of commonly studied metrics, including professional credentials, education, experience, firm coverage, earnings forecast frequency, and earnings forecast accuracy. Furthermore, the average analyst compensation in our sample, $511000, closely approximates the $500000 market average reported by Fang and Huang (2011, p. 4).

2 Prior literature argues that a leader-follower ratio greater than one means that an analyst is a ‘first mover’ (Ertimur et al., 2007, p. 583) or an ‘information leader’ (Baum et al., 2015, p. 7).

3 We measure Firm-Specific Experience as the average number of years the analyst has a forecast for the stocks in her portfolio. We measure General Experience as the number of years the analyst has been on the I/B/E/S U.S. Quarterly Detail File. Similar to prior research (e.g., Rees et al., 2015), we do not include General Experience in our changes tests because its period-to-period change would be the same for each observation in the sample (0.5 in the broker-vote and forecast-accuracy samples, 0.08 in the commissions sample, and 1 in the compensation sample).

4 The broker’s concierge-service activity score assigned the following points to these activities: conferences = 3 points for each presenting company; non-deal road shows = 9 points per day; field trips = 12 points per event; meetings with corporate executives = 3 points per meeting.

5 Changes tests are also the most likely to be valid and generalizable since they use each analyst as her own control.