Multipoint contact without forbearance? How coverage synergies shape equity analysts' forecasting performance

• DOI: http://doi.org/10.1002/smj.3188
• Author: Jose N. Uribe
• Date: 01 October 2020
• Published date: 01 October 2020

RESEARCH ARTICLE

Multipoint contact without forbearance? How

coverage synergies shape equity analysts'

forecasting performance

Jose N. Uribe

Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan

Correspondence

Jose N. Uribe, Stephen M. Ross School of

Business, University of Michigan, 701

Tappan St., Ann Arbor, MI 48109.

Email: jnu@umich.edu

Abstract

Research Summary: Scholars regularly use multipoint

contact (MPC) to explain how encountering rivals in

different domains shapes performance. While most

explanations rely on mutual forbearance theory, I pro-

pose that competitive deterrence does not adequately

explain how MPC shapes performance in knowledge

intensive work and argue instead that cross-domain

synergies may play a central role. I examine how secu-

rity analysts' MPC with publicly traded firms captures

synergies in their coverage portfolio, which improves

forecasting accuracy and information leadership. The

advantages of greater MPC for a focal analyst are

counterbalanced by rivals' observational learning,

which reduces the focal analyst's forecasting differentia-

tion. A natural experiment helps corroborate my argu-

ment: rival analysts' forecasting accuracy dropped for

firms in which high MPC analysts perished in the ter-

rorist attack on September 11, 2001.

Managerial Summary: Competition in the knowledge

economy often unfolds across multiple domains includ-

ing product markets, geographic locations, and cus-

tomer segments. In these settings, an actor's level of

multipoint contact (MPC) in a domain captures the

Received: 22 July 2019 Revised: 28 April 2020 Accepted: 2 May 2020 Published on: 29 June 2020

DOI: 10.1002/smj.3188

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and

reproduction in any medium, provided the original work is properly cited.

© 2020 The Authors. Strategic Management Journal published by John Wiley & Sons, Inc. on behalf of Strate gic Management Society.

Strat Mgmt J. 2020;41:1901–1932. wileyonlinelibrary.com/journal/smj 1901

knowledge and other synergies available to the focal

actor, which can improve performance in the domain.

In the equity research setting, an analyst's MPC on a

focal firm captures the likelihood that the analyst also

covers that firm's suppliers, customers and important

competitors. Using data on analysts' forecasting perfor-

mance between 2001 and 2013, I find that greater levels

of MPC on a focal firm predicts greater forecasting

accuracy and information leadership but also lowers

forecasting differentiation by attracting rivals who

observe and benefit from the focal analyst's knowledge.

KEYWORDS

analysts, multipoint contact, mutual forbearance, networks,

relatedness

1|INTRODUCTION

Competition across multiple domains, including product markets, geographic locations, and

customer segments is commonplace in the knowledge economy. A notable factor shaping an

actor's performance in a focal domain of competition is multipoint contact (MPC), a structural

feature that results from the overlap of multiple actors in multiple domains. Most extant expla-

nations of the effect of MPC on an actor's performance invoke mutual forbearance theory,

which posits that MPC leads to competitive deterrence by enabling rivals to retaliate in other

domains against an actor who refuses to forbear in a focal domain (Bernheim & Whinston, 1990;

Edwards, 1955; Karnani & Wernerfelt, 1985; Yu & Cannella, 2013).

However, MPC and competitive forbearance are very different concepts that need not co-

occur. A focal actor's MPC in a domain can be driven by any process that attracts rivals to other

domains in which the actor competes. Mutual forbearance is one specific dynamic that hinges

on a feedback loop between an actor's competitive intensity and rivals' strategic outcomes. For

example, work on mutual forbearance between airlines assumes, quite reasonably, that changes

in rival airlines' ticket prices on a route exert predictable effects on the profits of a focal airlines

on that route (Gimeno & Woo, 1999; Korn & Baum, 1999; Singal, 1996). The feedback loop is

reflected in the common knowledge that competitors' price changes can directly influence the

focal airline's profits. Without a feedback loop, multipoint competitors would have no incentive

to strategically reduce competitive intensity and would lack the capability to either signal or

enforce mutual forbearance. In settings where a strong feedback loop cannot be firmly

established, forbearance does not provide a satisfactory explanation for the effect of MPC on

performance.

An actor's MPC in a focal domain describes the incidence of joint participation with compet-

itors in other domains. Extant work shows that competitors' joint participation across domains

can embed important information about how resources and knowledge relate across these

domains. For example, the joint participation patterns of diversified companies across industries

reflects relatedness in resources between these industries (Bryce & Winter, 2009; Lien &

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Klein, 2009; Wan, Hoskisson, Short, & Yiu, 2011). Other research shows that workers' patterns

of inter-industry labor mobility reflect the underlying skill relatedness between these industries

(Neffke & Henning, 2013; Neffke, Otto, & Weyh, 2017). From this perspective, greater MPC

indicates greater relatedness between the focal domain and other domains where a focal actor

competes, which may provide advantageous synergies. Existing work on MPC has not ade-

quately acknowledged the presence of this synergy channel, which may lead to incorrect infer-

ences regarding why an actor's MPC affects performance in a focal domain.

The core argument of the present paper is that the synergy channel of MPC is likely to play

a central role in explaining a focal actor's performance in knowledge-intensive tasks. First, the

deterrence channel is suppressed because the uncertain and unpredictable nature of knowl-

edge-based competition reduces actors' ability to influence their rivals' outcomes. Second, the

relatedness that MPC captures can shape the extent to which actors can leverage their existing

knowledge. Consider an academic field in which the knowledge structure is defined by scholars'

publications in several overlapping topics. A specific scholar's MPC on a focal topic reflects the

extent to which she publishes on research topics that relate closely to the focal topic. Achieving

scientific impact is highly uncertain, and the scholar's lack of publication effort in a given topic

is unlikely to enhance competing scholars' scientific impact on that topic. Although the

required feedback loop for competitive deterrence is absent, the synergy channel of MPC is

likely to shape performance. Greater MPC in the foregoing example indicates knowledge of

topics that are closely related to the focal topic. Ensuring performance-enhancing synergies

include accelerated learning rates (Schilling, Vidal, Ployhart, & Marangoni, 2003) and incen-

tives to further invest in the focal topic to capitalize on those synergies (Levitt & March, 1988;

March, 1991).

The synergy channel also has implications for the emergence and directionality of spillover

effects in performance. The tendency of less knowledgeable competitors to observe and learn

from the knowledge-based outputs of more knowledgeable competitors is well-documented in

research on industrial agglomeration (Shaver & Flyer, 2000). In addition, concealing valuable

knowledge from rivals is particularly difficult when competition extends across multiple

domains (Greve, 2009). Thus the valuable knowledge of actors with greater MPC, who enjoy

the benefits of relatedness, may be exposed and used by rivals. Greater MPC may therefore con-

strain a focal actor's ability to differentiate their output from the output of rivals who can

observe and learn from the focal actor's knowledge.

The competition between sell-side security analysts (actors) in the production of publicly

traded firms' (domains) earnings forecasts provides an ideal testing ground for the synergy view

of MPC for two reasons. First, accurately estimating firms' future earnings requires interpreting

and integrating information on the firm's accounting practices, economic fundamentals, busi-

ness strategy, operations, and corporate governance (Asquith, Mikhail, & Au, 2005; Beunza &

Garud, 2007). The uncertainty inherent in this task weakens the feedback loop between an ana-

lyst's competitive intensity and rivals' outcomes. Thus, analysts are unlikely to voluntarily

reduce the quality of their forecasts because doing so does not necessarily help rivals improve

their own forecasting accuracy. Second, the setting provides the necessary conditions for com-

petitors to benefit from synergies in the firms they cover. Analysts can deliver valuable advice

to their investment clients on a focal firm when they also cover the focal firm's industry compet-

itors, critical suppliers and important customers (Bhojraj, Lee, & Oler, 2003; Brochet, Miller, &

Srinivasan, 2013; Guan, Wong, & Zhang, 2015; Sonney, 2007).

I propose that an analyst's MPC on a focal firm indicates coverage of related firms, and that

the synergies associated with this type of coverage increase the quality of the analyst's earnings

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