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Brokerage relationships and analyst forecasts: evidence from the protocol for broker recruiting

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Abstract

We offer novel evidence on how the nature of brokerage-client relationships can influence the quality of equity research. We exploit a unique setting provided by the Protocol for Broker Recruiting to examine whether relaxed broker noncompete agreement enforcement generates spillover effects on sell-side analysts. Entry into this agreement reassigns ownership of the client relationship from the brokerage to individual brokers, potentially generating a greater standard of care. Using a generalized difference-in-differences research design, we provide evidence consistent with brokers reducing pressure on analysts to produce optimistic research following protocol entry. This effect is concentrated among less experienced and non-All Star analysts, who previously may have faced the greatest pressures to sacrifice objectivity. Additionally, we find that analysts issue more accurate forecasts and generate reports with heightened market reactions following protocol entry. Our collective evidence sheds new light on how the nature of brokerage relationships can influence analysts’ research production.

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Notes

  1. Financial advisers are generally subject to either a fiduciary duty or the suitability standard, both of which emphasize client welfare. In this study, we use the term “broker” and “financial adviser” interchangeably.

  2. Specifically, regulatory initiatives such as the Global Settlement have alleviated many investment banking-related conflicts (Kadan et al. 2009; Corwin et al. 2017). However, recent evidence suggests that brokerage-related conflicts, such as trading huddles and catered services, may have intensified in recent years (Brown et al. 2016; Pacelli 2019; Drake et al. 2020). Regulators also continue to raise concerns that brokerage activities compromise equity research analyst objectivity (SEC 2010).

  3. While over 1300 brokerages have joined the protocol, many of these do not have an equity research arm. In fact, generally only very large brokerages have financial advisory and equity research divisions. Thus because our research question requires the brokerages in our treatment sample to have both divisions (financial advisory and research), we are constrained to evaluating treatment effects for larger brokerages (which are fewer in number but account for a major portion of all forecasting activity on I/B/E/S).

  4. Tash Elwyn (president of Raymond James & Associates Private Client Group) makes an analogy to the health care industry. He states that just as “it would be ‘unconscionable’ to prevent patients from moving their medical records to another office or hospital,” it is unethical to prevent clients from moving with their advisers (Elwyn 2017).

  5. https://www.nysscpa.org/news/publications/the-trusted-professional/article/banks-blindsided-by-loophole-enabling-client-poaching-101716

  6. We note, however, that observing an association between trading volume and forecast optimism does not necessarily imply an incentive-based explanation. For example, prior studies find that analysts exhibit behavioral biases (De Bondt and Thaler 1990) and may view certain stocks through “rose-colored glasses” (Bradshaw 2011). Proxies for brokerage incentives, such as trading volume, may thus reflect the market’s overall sentiment about a stock, and analysts may issue an optimistic forecast simply because they truly feel optimistic about the stock’s prospects.

  7. http://www.thebrokerprotocol.com

  8. We use an I/B/E/S detail file that was downloaded in April 2018.

  9. ProtocolEntry is essentially an interaction term for indicator variables of protocol analysts and the post-protocol period. The main effects of these variables are subsumed by our fixed effects structure (discussed in the next paragraph).

  10. Our results are robust to alternative clustering methods, including clustering by firm, brokerage, or analyst, as well as double clustering by firm and brokerage or firm and analyst (untabulated).

  11. We obtain the brokerage reputation rankings from Professor Jay Ritter’s website.

  12. We conduct additional analyses examining the robustness of our findings to differences across protocol and non-protocol brokerages along these dimensions in Section 4.3.

  13. We require non-missing Ritter reputation rankings for this analysis, resulting in a substantial decrease in sample size. In untabulated analyses, we find similar results if we control for this variable and the percentage of All Stars instead of partitioning the sample.

  14. If the Bloomberg business profile is missing or we cannot determine which type of activities the brokerage conducts, we do not classify the brokerage.

  15. We use firm-year fixed effects in this model, rather than firm-quarter, as recommendations are issued far less frequently, on average, than earnings forecasts.

  16. In addition to affecting analysts’ research production, protocol entry could impact analyst turnover if analysts form productive partnerships with brokers and follow them to new firms. In untabulated analyses, we estimate a model at the analyst-year level where Exit, an indicator variable equal to one if the analyst leaves the brokerage the following year (zero otherwise), is regressed on ProtocolEntry and control variables. We find no evidence that analysts become more or less likely to depart their brokerage following protocol entry, compared to non-protocol analysts.

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Acknowledgments

We thank Kimball Chapman, Dane Christensen, Janet Gao, Jared Jennings, Jaewoo Kim, Ken Merkley, and workshop participants at the University of Rochester for helpful comments and suggestions. We also thank Joshua Andreason, Jared Garlick, and Jared Stack for excellent research assistance.

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Authors and Affiliations

  1. University of Georgia, Athens, GA, USA

    Braiden Coleman

  2. Brigham Young University, Provo, UT, USA

    Michael Drake

  3. Harvard Business School, Boston, MA, USA

    Joseph Pacelli

  4. University of Oregon, Eugene, OR, USA

    Brady Twedt

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  1. Braiden Coleman
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  2. Michael Drake
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  3. Joseph Pacelli
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  4. Brady Twedt
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Correspondence to Michael Drake.

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Table 11 Variable Definitions
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Coleman, B., Drake, M., Pacelli, J. et al. Brokerage relationships and analyst forecasts: evidence from the protocol for broker recruiting. Rev Account Stud 28, 2075–2103 (2023). https://doi.org/10.1007/s11142-022-09682-4

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