Corporate Spino s and Analysts’ Coverage Decisions: The ... · analysts’ economic incentives to...

Corporate Spinoffs and Analysts’ Coverage Decisions: The

Implications for Diversified Firms∗

Emilie R. Feldman†

April 18, 2015

Abstract

This paper investigates how spinoffs improve the quality of analysts’ research about diversifiedfirms, theorizing that these deals may induce analysts to revisit their earlier coverage decisions.The gains resulting from these shifts are expected to be more pronounced when a firm undertakesa legacy (rather than a non-legacy) spinoff, which removes the business that may be constrain-ing analysts’ coverage decisions in the first place. Consistent with this argument, firms thatundertake legacy spinoffs experience greater improvements in the composition and quality oftheir analyst coverage than their non-legacy counterparts, and in their overall forecast accuracyand stock market performance. Taken together, these findings shed light on the relationshipsamong the scope decisions, analyst coverage, and valuations of diversified firms.

Keywords: corporate spinoffs, securities analysts, coverage decisions, diversified firms, corpo-rate strategy

Forthcoming, Strategic Management Journal

∗I am very grateful to Stuart Gilson and Belen Villalonga for sharing the baseline dataset employed in this paper,and to Raffi Amit, Don Bergh, Matthew Bidwell, Brian Bushee, Laurence Capron, Olivier Chatain, Stuart Gilson,Martine Haas, Connie Helfat, Vit Henisz, Zeke Hernandez, Rahul Kapoor, Anoop Menon, Ethan Mollick, CynthiaMontgomery, Sanjay Patnaik, Evan Rawley, Lori Rosenkopf, Arkadiy Sakhartov, Metin Sengul, Belen Villalonga,Natalya Vinokurova, and Tyler Wry for their valuable suggestions on earlier drafts of this paper. I also appreciatethe comments of seminar participants at the University of Michigan, the University of Minnesota, the University ofChicago, Dartmouth, the University of North Carolina, the 2012 Atlanta Competitive Advantage Conference, andthe 2011 Academy of Management Annual Meeting. Any errors are my own.†The Wharton School, University of Pennsylvania, [email protected]

Introduction

Divestitures are an important strategic mechanism that managers can use to reconfigure their

firms’ resources (Chang, 1996; Capron, Mitchell, and Swaminathan, 2001; Helfat and Eisenhardt,

2004; Kaul, 2012). The decision to undertake these deals is made under the scrutiny of analysts,

who, as market intermediaries, drive investor behavior, and hence, share prices (Benner, 2007).

However, although analysts must gather, synthesize, and communicate to investors information

about the divestitures that firms undertake, these deals have also been shown to influence analysts’

effectiveness in performing their functions. For example, when firms undertake spinoffs (the divesti-

ture of a business via the pro-rata distribution of shares to existing shareholders), the quality of

the research that analysts produce about these firms improves (Krishnaswami and Subramaniam,

1999; Nanda and Narayanan, 1999; Ferris and Sarin, 2000; Gilson et al., 2001). Firms also enjoy a

favorable stock market response when they undertake spinoffs (Daley, Mehrotra, and Sivakumar,

1997; Desai and Jain, 1999; Bergh, Johnson, and DeWitt, 2008).

Why do spinoffs yield these improvements in both the quality of analysts’ research and the

stock market valuations of diversified firms? Existing research has attributed these improvements to

spinoffs reducing the complexity of divesting firms (Bhushan, 1989; Feldman, Gilson, and Villalonga,

2014) or removing businesses that the analysts following these firms may not be specialized to cover

(Zuckerman, 2000). Implicitly, these explanations hold fixed the pool of analysts covering these

firms, illustrating how spinoffs could influence the quality of research produced by a firm’s existing

set of analysts. However, these accounts may only be part of the story, in that they do not address

the possibility that analysts might change their behavior in response to the strategies that firms

undertake. Specifically, the current set of analysts covering a firm may not remain fixed when that

firm undertakes a spinoff, with distinct implications relative to existing explanations.

In this paper, I address this possibility by exploring how spinoffs might prompt analysts to

revisit their earlier coverage decisions. I argue that all spinoffs would be expected to improve

analysts’ economic incentives to terminate or initiate coverage of the divesting firms, resulting in

some change in the composition of analysts covering them. I then theorize that these shifts should

be more significant when a firm spins off its original, or “legacy” business (Feldman, 2014), since

legacy spinoffs remove the businesses that may have determined analysts’ initial coverage decisions

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in the first place. Thus, legacy spinoffs in particular should induce analysts to change their original

perceptions, and hence, coverage decisions about the divesting firms.

These predicted changes in analysts’ coverage decisions following legacy spinoffs have two impli-

cations for forecast accuracy. First, the analysts for whom a legacy spinoff induces terminations of

coverage are likely to be those who started covering the divesting firm because they specialized in

its legacy industry at the time when its legacy business was its main operation. However, a firm’s

legacy business usually comprises a small share of its operations before being divested, suggesting

that, out of all of the analysts who were covering the divesting firm pre-spinoff, the analysts who

later terminate coverage should have been producing the least accurate forecasts about it. Second,

the analysts for whom a legacy spinoff induces initiations of coverage are likely to be those who

specialize in the divesting firm’s current industry, yet who were unspecialized to cover that firm

at the time when its legacy business was its core operation. By comparison, the analysts who

already follow a firm that undertakes a legacy spinoff may need to update their models for covering

it, suggesting that, out of all of the analysts who are covering the divesting firm post-spinoff, the

analysts who have newly initiated coverage should produce the most accurate forecasts about it.

Consistent with these arguments, I find evidence that analysts are respectively more likely

to terminate and initiate coverage of firms that undertake legacy rather than non-legacy spinoffs.

Additionally, I show that the analysts who terminate coverage of firms that undertake legacy spinoffs

produce less accurate pre-spinoff forecasts about those firms than the analysts who continue covering

them, and the analysts who initiate coverage of firms that undertake legacy spinoffs produce more

accurate post-spinoff forecasts about those firms than the analysts who continue covering them.

However, neither of these effects occurs when firms undertake non-legacy spinoffs. As these changes

in the composition and quality of analyst coverage would imply, moreover, I establish that firms

that undertake legacy spinoffs enjoy larger overall improvements in their forecast accuracy and

stock market performance than their non-legacy counterparts.

In sum, the results in this paper illustrate how legacy spinoffs might induce analysts to revisit

and change their earlier coverage decisions, elucidating how these deals might improve the quality

of analysts’ research, why these deals may be undertaken in the first place, and most importantly,

why spinoffs have been shown to be positively associated with firm performance on average.

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Theory

Cognitive Inertia in Analysts’ Coverage Decisions

Decision-making processes exhibit a great deal of stickiness. Individuals develop “schemas” to

“represent knowledge about a concept or type of stimulus, including its attributes and the relations

among those attributes,” and these schemas strongly influence current behavior (Fiske and Taylor,

1991: 141). Accordingly, the way that managers make decisions is shaped by their historical views

of and experiences in their firms (Prahalad and Bettis, 1986; Leonard-Barton, 1992; Kaplan and

Tripsas, 2008) and in their industries (Porac, Thomas, and Baden-Fuller, 1989; Benner and Tripsas,

2012). This has two possible effects on the quality of decision-making. One is that accumulated

experience built around existing schemas can enable managers to leverage their knowledge and

capabilities in the current context, improving decision-making (Nelson and Winter, 1982; Leonard-

Barton, 1992; Teece et al., 1994). The other is that “cognitive inertia” surrounding existing schemas,

defined as “the inability of strategists to revise their mental models... sufficiently quickly to adapt

successfully to the changing environment” (Hodgkinson, 1997), can limit managers’ responsiveness

to current conditions, eroding the quality of decision-making (Huff, Huff, and Thomas, 1992; Reger

and Palmer, 1996; Tripsas and Gavetti, 2000; Gavetti, Levinthal, and Rivkin, 2005).

These insights apply quite naturally to analysts, in that their original schemas about the firms

they do and do not cover may exert a great deal of influence on their current coverage decisions

and hence, the accuracy of their research. The two opposing effects of this stickiness on the quality

of decision-making are likely to be at play for analysts, too. On the one hand, analysts may

accumulate experience covering a firm and connections to its management over time, improving

the accuracy of their research (Mikhail, Walther, and Willis, 2003). On the other hand, however,

analysts may be slow to update their perceptions of firms, even if the true nature of those firms’

operations has substantively diverged from analysts’ original schemas about them. Consistent with

this view, Tripsas (2009) shows that analysts initially classified “Linco” as a photography firm

when it went public, and that this coverage persisted even after Linco had taken steps towards

remaking itself into a memory firm. Similarly, in the face of a technological discontinuity, analysts

are more attentive to and enthusiastic about strategies that “extend and preserve” firms’ existing

technologies than they are to strategies that respond to that technological shift (Benner, 2010).

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When might decision-makers like analysts be able to surmount the negative effects of cognitive

inertia on the quality of their decision-making? Existing research suggests that this should only

occur when challenges to decision-makers’ existing schemas are sufficiently widespread or substan-

tial (Hodgkinson, 1997); for example, Benner (2010) finds evidence that analysts begin responding

to technological discontinuities only when the true direction of technical change has become well-

established. The remainder of this section of the paper develops two core arguments around this

insight by considering how legacy spinoffs might challenge analysts’ existing schemas about the di-

vesting firms. First, all spinoffs should induce some shift in the composition of analysts covering the

divesting firms by improving analysts’ economic incentives to cover those firms. However, legacy

spinoffs would be expected to amplify these effects by removing the specific business unit—the

legacy business—that may be the source of analysts’ original schemas about the divesting firms,

enabling them to overcome cognitive inertia in their coverage decisions. Second, these distinctive

shifts in the composition of analyst coverage following legacy spinoffs should have unique implica-

tions for the quality of analysts’ research about the divesting firms as well.

Corporate Spinoffs and the Composition of Analyst Coverage

Spinoffs and Changes in Analysts’ Coverage Decisions

All spinoffs, whether legacy or non-legacy, would be expected to induce some shift in the com-

position of analysts covering the divesting firms, by increasing the economic benefits and reducing

the economic costs of analysts terminating and initiating coverage of those companies.

With regard to terminations of coverage, analysts suffer career penalties, such as losing their

jobs or having to move to lower-reputation banks, when they produce inaccurate forecasts (Mikhail,

Willis, and Walther, 1999; Hong, Kubik, and Solomon, 2000; Rao, Greve, and Davis, 2001; Hong

and Kubik, 2003). Accordingly, the economic benefits of analysts terminating coverage of a firm

they currently cover derive from a reduction in the career risks associated with the production of

inaccurate forecasts. Spinoffs frequently alter the composition of the industries in which diversified

firms participate, in that the spun-off business units are often unrelated to their firms’ primary

areas of operation (Daley et al., 1997; Desai and Jain, 1999; Krishnaswami and Subramaniam,

1999). As a firm’s industry changes due to a spinoff, the career risks associated with analysts

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covering a firm they are unspecialized to follow are therefore likely to increase. Thus, the economic

benefits of analysts terminating coverage should rise when a firm undertakes a spinoff.

Terminations of coverage also reduce trading volume by directing investor attention away from

a firm’s stock, and they send a negative signal about that firm’s prospects, which could jeopardize

client relationships or access to management (McNichols and O’Brien, 1997; Mola, Rau, and Kho-

rana, 2013). Because analyst compensation is linked to trading volume and investment banking

revenues (Groysberg, Healy, and Maber, 2011), the costs that banks incur from terminations of

coverage are passed on to analysts. With this being said, the economic costs of terminating cover-

age should decrease when a firm undertakes a spinoff. Trading volumes increase after these deals

(Vijh, 1994; Gilson et al., 2001; Abarbanell, Bushee, and Raedy, 2003), offsetting the declines that

normally occur when analysts terminate coverage. Spinoffs also constitute a plausible reason for a

termination of coverage, lowering the risk of jeopardizing client or management relationships.

In terms of initiations of coverage, analysts are rewarded for maximizing the accuracy of the

forecasts they produce (Bhushan, 1989; Litov, Moreton, and Zenger, 2012), in that their compensa-

tion is determined by two factors—professional recognition in venues like Institutional Investor and

the Wall Street Journal (Stickel, 1992; Fang and Yasuda, 2009), and trading volume and invest-

ment banking revenues (Groysberg et al., 2011)—both of which are driven by analysts producing

accurate research on which investors can reliably base their decision-making (Gilson et al., 2001).

Thus, the economic benefits of analysts initiating coverage of a firm they do not yet cover derive

from the financial gains that they enjoy by solidifying their standing as experts and by generating

more trading volume and investment banking revenues through their research. There are two rea-

sons why the economic benefits of analysts initiating coverage are likely to increase when a firm

undertakes a spinoff. First, the fact that spinoffs create separate entities with distinct economic

characteristics should result in increased trading volumes as investors become attracted to the “pure

play” stocks of the parent and spinoff firms (Vijh, 1994; Gilson et al., 2001), and as institutions

reallocate their investments to suit their preferences (Abarbanell et al., 2003). Second, spinoffs

may also be correlated with an increased demand for investment banking services. These deals

may reduce the ability of diversified firms to function as internal capital markets (Gertner, Powers,

and Scharfstein, 2002), requiring investment banks to fund them externally (Krishnaswami and

Subramaniam, 1999). Additionally, spinoffs are often part of larger restructuring efforts involving

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other transactions like mergers and acquisitions, alliances, and divestitures (Chang, 1996; Capron,

Dussauge, and Mitchell, 1998; Capron et al., 2001), which require the services of investment banks.

With this being said, however, analysts bear “start-up costs” (Mikhail, Walther, and Willis,

1997) of learning to cover a new firm. These start-up costs are lower when analysts are specialized

to cover a particular firm, since they have expertise covering similar companies (Bhushan, 1989).

When a firm undertakes a spinoff, the start-up costs of initiating coverage are likely to decrease:

the analysts who initiate coverage in these circumstances are those whose areas of specialization

match the firm’s primary industry following the completion of that deal, since it is less costly for

analysts to learn how to produce accurate research about firms they are specialized to cover.

In sum, the foregoing discussion has addressed how all spinoffs might improve analysts’ economic

incentives to change their coverage decisions. By increasing the economic benefits and reducing the

economic costs of such changes, spinoffs should impel some analysts who are covering the divesting

firms to stop covering them, and some analysts who are not covering the divesting firms to start

covering them. Importantly, however, this discussion has not answered the question of how spinoffs

might induce analysts to surmount the cognitive inertia that characterizes their coverage decisions.

Theoretically, if a spinoff were to prompt analysts to reevaluate their original schemas about the

divesting firm, the likelihood that analysts would change their coverage decisions would be even

higher for that deal. The natural question is whether certain spinoffs might be more likely than

others to induce analysts to overcome cognitive inertia in their coverage decisions.

Legacy Spinoffs and Changes in Analysts’ Coverage Decisions

Legacy spinoffs, in which a firm spins off its original line of business, would be expected to

do just this. As its founding operation, a firm’s legacy business has been present throughout the

company’s entire history. The fact that analysts specialize by industry means that the (mis-)match

between analysts’ areas of specialization and a firm’s legacy industry makes that legacy business

a key determinant of analysts’ original schemas about the divesting firm, and hence, their initial

coverage decisions for it. Specifically, certain analysts may have begun covering a firm when its

legacy business constituted its core operation, matching those analysts’ industry specializations at

that time. These analysts would be expected to continue covering that firm based on their initial

decision to do so (as in the case of Linco (Tripsas, 2009)), even if the composition of the firm’s

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businesses evolved away from their areas of specialization. Other analysts may not have started

covering a firm because its legacy business was too far removed from their areas of specialization.

These analysts still might not cover that firm at present because of their initial decision not to do so,

even if the composition of the firm’s businesses evolved to be closer to their areas of specialization.

A legacy spinoff formally separates a diversified firm’s current operations from its historical

antecedents, thereby breaking the connection between analysts’ initial and current coverage deci-

sions for the divesting firms. As a result, a legacy spinoff would therefore be expected to induce

analysts to overcome cognitive inertia in their coverage decisions by removing the specific business

that may be determining their original schemas about that firm (Hodgkinson, 1997; Benner, 2010).

George Millington Jr., the head of Gould Inc.’s legacy battery division (which the firm spun off in

1984), expressed exactly this sentiment: “The spinoff was psychologically important. Whenever we

met with anyone from Wall Street, the response was: ‘Oh yeah, Gould, the old battery maker’ ”

(Waldstein, 1987). Non-legacy spinoffs, which remove units other than a firm’s legacy business, are

unlikely to have the same effect, since these spinoffs do not affect the firm’s historical antecedents.

Thus, all spinoffs increase analysts’ economic incentives to terminate coverage of the divesting

firms. Legacy spinoffs uniquely amplify these effects by inducing certain analysts—those who

started covering firms due to their legacy businesses, but who should not be doing so because

they are no longer appropriately specialized—to surmount the cognitive inertia in their coverage

decisions and stop covering those companies. Non-legacy spinoffs are not expected to have the

same impact, since those deals do not affect the divesting firms’ legacy businesses.

Hypothesis 1a. Analysts are more likely to terminate coverage of firms that undertake

legacy spinoffs than of firms that undertake non-legacy spinoffs.

Similarly, all spinoffs increase analysts’ economic incentives to initiate coverage of the divesting

firms. Legacy spinoffs in particular magnify these effects by inducing certain analysts—those who

are not covering firms due to their legacy businesses, but who should be doing so because they are

appropriately specialized—to surmount the cognitive inertia in their coverage decisions and start

covering those firms. Non-legacy spinoffs are not expected to have the same effect, since those deals

do not influence the divesting firms’ legacy businesses.

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Hypothesis 1b. Analysts are more likely to initiate coverage of firms that undertake

legacy spinoffs than of firms that undertake non-legacy spinoffs.

Corporate Spinoffs and the Quality of Analyst Coverage

The above discussion suggests that analysts will be more likely to terminate and initiate coverage

of firms that undertake legacy rather than non-legacy spinoffs. These differences in the likelihood of

analysts changing their coverage decisions are expected to have distinct implications for the quality

of analysts’ research, as measured by their forecast accuracy, about these two types of firms.

Terminations of Coverage and Forecast Accuracy

Two groups of analysts cover a firm before it undertakes a spinoff: the analysts who will

terminate coverage after that deal’s announcement, and the analysts who will continue coverage.

For firms that undertake legacy spinoffs, the pre-spinoff forecasts produced by analysts who

terminate coverage should be less accurate than those produced by analysts who continue coverage.

As per the earlier discussion, a legacy spinoff is expected to induce certain analysts to surmount

cognitive inertia in their coverage decisions by removing the specific business unit that may be

generating these analysts’ original schemas about the divesting firm in the first place. The particular

analysts for whom this is likely to be the case are those who are covering the divesting firm because

they specialize in its legacy industry, even if the legacy business has come to comprise a small share

of the divesting firm’s operations. By comparison, the analysts for whom a legacy spinoff does not

induce terminations of coverage (i.e., the analysts who continue coverage) are those whose coverage

decisions are unconstrained by any cognitive inertia deriving from the firm’s legacy business. The

analysts for whom this is likely to be the case are those who are covering the divesting firm because

they specialize in an area that is close to the firm’s primary industry. Because forecast accuracy

declines in the distance between an analyst’s area of specialization and a firm’s main industry

(Zuckerman, 1999; Gilson et al., 2001), the terminating analysts should therefore be producing less

accurate pre-spinoff forecasts than the continuing analysts.

These arguments should not hold for firms that undertake non-legacy spinoffs. The analysts

for whom a non-legacy spinoff induces terminations of coverage are those who specialize in the

industry in which the divested non-legacy business operates. To the extent that this spun-off

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business is not the primary focus of the divesting firm’s operations, these terminating analysts are

likely to be producing inaccurate pre-spinoff forecasts about that firm. Given the earlier theorizing

about cognitive inertia in analysts’ coverage decisions, a non-legacy spinoff should fail to induce

terminations of coverage by the analysts who specialize in the divesting firm’s legacy industry, since

these analysts may still be constrained by their initial decisions to cover that firm. To the extent

that the divesting firm’s legacy business has come to constitute a small share of its operations, these

continuing analysts are also likely to be producing inaccurate pre-spinoff forecasts about that firm.

A priori, however, it is not possible to determine whether the forecasts produced by the continuing

analysts will be more or less inaccurate than those produced by the terminating analysts.

Thus, while legacy spinoffs have a clear implication for the relative accuracy of the pre-spinoff

forecasts produced by analysts who terminate versus continue their coverage of the divesting firms,

the same is not true for non-legacy spinoffs. These points imply:

H2a. The difference in the pre-spinoff forecast accuracy of analysts who terminate

versus continue coverage of firms that undertake legacy spinoffs is larger than that of

firms that undertake non-legacy spinoffs.

Initiations of Coverage and Forecast Accuracy

Two groups of analysts will cover a firm after it undertakes a spinoff: the analysts who newly

initiate coverage, and the analysts who chose to continue (rather than terminate) coverage.

For non-legacy spinoffs, it is unclear whether the forecasts produced by the initiating analysts

will be more or less accurate than those produced by the continuing analysts. On the one hand,

analysts generally do not produce much research about the segments in diversified firms (Feldman

et al., 2014), and they find it costly to change the models they use to value companies (Zuckerman

and Rao, 2004). Both of these factors should make it more difficult for continuing analysts to

produce accurate research about a firm that has undertaken a non-legacy spinoff: they may not

have a sufficient understanding of that firm’s remaining operations, and they may not be able or

willing to update their valuation models in response to the deal. Initiating analysts should not be as

constrained by these limitations, suggesting that their forecasts might be more accurate than those

of the continuing analysts. On the other hand, continuing analysts may accumulate experience

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covering a firm and connections to its management over time, both of which are associated with

the production of accurate research (Mikhail et al., 2003). Because initiating analysts lack these

types of expertise, their forecasts might be less accurate than those of the continuing analysts.

While it is unclear, for non-legacy spinoffs, whether the initiating analysts’ forecasts will be

more or less accurate than the continuing analysts’ forecasts, there are two reasons why the same

is not true for legacy spinoffs. First, the psychological magnitude of the changes implied by legacy

spinoffs suggests that analysts who continue covering these firms might be even more constrained

by their pre-spinoff valuation models or their failure to produce segment-level research about these

firms (Zuckerman and Rao, 2004; Feldman et al., 2014). Second, while continuing analysts might

normally benefit from their accumulated experience covering a firm that undertakes a spinoff, the

historical significance of a firm getting rid of a business for which it has been known since its

inception, along with the organizational shifts that often accompany such a change (Feldman,

2014), should erode the value of the experience or connections that continuing analysts normally

enjoy. Both of these effects should put the initiating analysts at an advantage to the continuing

analysts. Moreover, the analysts for whom a legacy spinoff induces initiations of coverage are likely

to be those who were cognitively constrained against covering that firm due to its legacy business,

even though they specialize in the divesting firm’s post-spinoff industry. Thus, when these analysts

initiate coverage, they should produce accurate research about that firm.

Legacy spinoffs therefore have two complementary effects: (1) they induce well-specialized an-

alysts, who would otherwise be constrained by cognitive inertia, to initiate coverage; and (2) they

erode the benefits of experience that analysts who continue covering the divesting firms would

otherwise enjoy. This suggests that analysts who initiate coverage of firms that undertake legacy

spinoffs should produce more accurate post-spinoff forecasts than analysts who continue coverage.

By comparison, the relative accuracy of the forecasts produced by analysts who initiate versus

continue coverage of a firm that undertakes a non-legacy spinoff is unclear. These points imply:

H2b. The difference in the post-spinoff forecast accuracy of analysts who initiate versus

continue coverage of firms that undertake legacy spinoffs is larger than that of firms

that undertake non-legacy spinoffs.

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Non-Random Selection

Given the divergent implications that legacy spinoffs are predicted to have for analysts’ cov-

erage decisions, the intentionality underlying managers’ decisions to undertake these spinoffs is

theoretically interesting and has implications for the empirical approach that must be employed

to accurately test the above arguments. The foregoing discussion suggests that analysts observe

the type of spinoff a diversified firm undertakes and change their coverage decisions accordingly.

This seems to be a reasonably accurate characterization of analysts’ behavior, in that there is evi-

dence that analysts change their coverage decisions in response to “concrete information” that it is

necessary to do so (Beunza and Garud, 2007: 32). For example, Jensen (2004) finds that analyst

coverage is positively associated with alliance announcements, which he characterizes as conveying

information to analysts about the quality of a firm’s capabilities, hence prompting greater coverage.

Similarly, adverse earnings surprises, which communicate to analysts that a firm’s future prospects

are weak, are negatively correlated with analyst coverage (Mikhail, Walther, and Willis, 2004).

However, there are many reasons why managers might decide to undertake a spinoff: exiting

unrelated (Daley et al., 1997, Desai and Jain, 1999) or underperforming businesses (Hayward and

Shimizu, 2006; Shimizu, 2007), improving the efficiency of resource allocation or the fit between a

firm’s strategy and the industry environment (Capron et al., 2001; Helfat and Eisenhardt, 2004),

and even removing businesses whose continued presence could be clouding external perceptions

(Zuckerman, 2000; Gilson et al., 2001; Bergh et al., 2008). Since managers sometimes try to

shape analysts’ perceptions (Westphal and Clement, 2008; Westphal and Graebner, 2010), this

final motivation could be a key driver of the legacy spinoff decision.

Whether analysts react to the strategies they observe managers undertaking, or managers ac-

tively try to shape analysts’ perceptions by undertaking certain strategies (or these causal pathways

occur in different circumstances), the above discussion suggests that it is important to account for

the possibility that the motivations that drive managers’ decisions to undertake legacy versus non-

legacy spinoffs may themselves be correlated with the potentially different outcomes of these two

types of transactions. More specifically, if managers choose to undertake legacy spinoffs because

their firms are not attaining the appropriate analyst coverage, any differences in the quality of

analyst coverage these firms attain could simply be attributable to these ex ante differences rather

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than to the type of spinoff. As will be discussed, this means that it is important to control for the

effects of non-random selection in the decision to undertake a legacy (versus non-legacy) spinoff on

the outcomes firms experience when they implement one or the other type of transaction.

Methods

Sample and Data

The sample of companies analyzed in this paper is the same as the one used in Feldman et al.

(2014). As described in that paper, a random sample of 62 spinoffs was chosen from the universe

of the 350 spinoffs completed between 1985 and 2001.

The benefit of using Feldman et al.’s (2014) sample of spinoffs is that it faciliates the construction

of a dataset whose temporal structure is uniquely well-suited to testing the above hypotheses. More

specifically, as illustrated in Figure 1, there are three relevant blocks of time to consider within

the “lifecycle” of a corporate spinoff: the period of time prior to the announcement of the spinoff

(Period 1), the period between the announcement and effective dates of the spinoff (Period 2),

and the period following the effective date of the spinoff (Period 3). The Feldman et al. (2014)

dataset consists of detailed information hand-collected from the universe of analyst reports written

about the divesting companies in the sample during Period 2, meaning that it includes identifying

information for every analyst who covered these companies during this block of time. From there,

I gathered information on every analyst covering the same companies during Period 1 (the year

prior to the spinoff announcements) and Period 3 (the year following the spinoff completions). This

makes it possible to determine, out of the entire population of analysts covering these firms, which

analysts continued covering these firms throughout the entire spinoff lifecycle, as well as which

analysts initiated or terminated coverage at various points therein. Figure 1 presents a graphical

depiction of all the potential ways in which analyst coverage of a company that undertakes a spinoff,

legacy or not, might change over these three periods of time.

———— Figure 1 here ————

In this figure, a group of analysts, “A”, is the initial set of analysts who cover a company in the

year prior to the announcement of its spinoff, in Period 1. Following the spinoff’s announcement,

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in Period 2, a subset of these individuals continues covering the firm (“A1”), and a different subset

terminates coverage (“A2”). Additionally, a new group of analysts, “B,” initiates coverage of

the firm during Period 2. After the spinoff’s completion, a subset of the analysts who continued

coverage from Period 1 into Period 2 still continues covering the firm into Period 3 (“A1a”), while a

different subset terminates coverage (“A1b”). Among the analysts who initiated coverage in Period

2 (“B”), one subset continues coverage into Period 3 (“B1”) and one subset terminates coverage

(“B2”). Finally, a new group of analysts, “C,” initiates coverage of the firms during Period 3.

The temporal structure of this dataset therefore facilitates an exploration of how the compo-

sition and quality of analyst coverage changes when firms undertake spinoffs. However, the above

hypotheses predict that the nature of these changes will differ depending on whether a firm spins

off a legacy or a non-legacy business. As such, I identified the legacy businesses (defined as the

original business in which a firm operated at the time of its founding (Feldman, 2014)) of each

of the 52 divesting firms in Feldman et al.’s (2014) sample using the International Directory of

Company Histories and corporate annual reports. I then hand-matched this information on firms’

legacy businesses to the spinoff data to identify the legacy spinoffs. For example, Whitman Cor-

poration’s legacy business was identified as the Illinois Central Railroad, so the spinoff of that unit

was classified as a legacy spinoff. Of the 62 spinoffs in the sample, 11 were legacy spinoffs.

For each analyst report, the unit of analysis in this study, I collected earnings forecast data

from the Institutional Brokers’ Estimate Service (I/B/E/S). I also gathered information about the

characteristics of the analysts and their investment banks from I/B/E/S, Capital IQ, Institutional

Investor Magazine, Professor Jay Ritter’s website, and Professor Boris Groysberg’s dataset on

I/B/E/S’s universe of analysts. I collected financial data, most importantly the actual earnings

realized by the companies (for comparison to the forecasts the analysts made) and their end-of-year

stock prices, from Compustat and the Center for Research in Security Prices (CRSP).

Variables

Dependent Variables

Hypotheses 1a and 1b make predictions about the propensities of analysts to terminate or

initiate coverage of firms following legacy and non-legacy spinoffs, relative to their propensities to

13

continue their coverage unchanged. As such, the dependent variables in the regressions testing

these predictions are Analyst Terminates Coverage and Analyst Initiates Coverage, respectively.

Analyst Terminates Coverage takes the value one for all reports written in Period 1 by analysts

who terminate coverage of a firm once it announces a spinoff (the subset of A analysts who become

A2 analysts), and zero for reports written in Period 1 by analysts who continue covering a firm

after it announces a spinoff (the subset of A analysts who become A1 analysts). Analyst Initiates

Coverage takes the value one for all reports written in Period 2 by analysts who initiate coverage

of a firm after it announces a spinoff (a B analyst), and zero for reports written by analysts who

continue their coverage of that firm from Period 1 into Period 2 (an A1 analyst).

Hypotheses 2a and 2b make predictions about the accuracy of earnings forecasts produced about

firms that undertake legacy and non-legacy spinoffs. The dependent variable in regressions testing

these hypotheses is EPS Forecast Error: the absolute value of the difference between forecasted and

actual earnings-per-share (EPS), scaled by the end-of-year stock price in which the EPS figure was

realized (Agrawal, Chadha, and Chen, 2006). Higher EPS Forecast Errors indicate that an analyst

is less accurate in his forecast, as the gap between forecasted and actual earnings is larger.

Key Independent Variable

Legacy Spinoff is an indicator variable that takes the value one if a firm about which an analyst

writes a report undertakes a legacy spinoff, and zero if the firm undertakes a non-legacy spinoff.

Methodologies

The empirical work in this paper must account for two methodological issues. First, the key

independent variable, Legacy Spinoff, is measured at the deal-level, whereas the dependent variables

used to test the hypotheses are all measured at the analyst-level. This makes it necessary to control

for the fact that the same value of this deal-level variable is repeated in each analyst-specific

observation to which it pertains. Second, as has been mentioned, a manager’s decision to spin off

his firm’s legacy or non-legacy business is not random, and may be driven by characteristics that

are themselves correlated with the outcome variable in Hypotheses 2a and 2b, EPS Forecast Error.

Thus, it is also necessary to account for the effects of non-random selection.

14

To test Hypotheses 1a and 1b, I use logistic regressions with robust standard errors clustered

by deal to account for the repetition of observations in the independent variable, Legacy Spinoff.

To test Hypotheses 2a and 2b, I use three types of empirical models to deal with the above-

described methodological issues: regressions with standard errors clustered by deal to control for the

repetition of deal-specific observations, and both treatment effects and coarsened exact matching

models to account for non-random selection.

In the treatment effects models, the first-stage regression (estimated at the deal-level) takes

Legacy Spinoff as its dependent variable, thereby predicting the likelihood that a firm will un-

dertake a legacy spinoff (the treated group) rather than a non-legacy spinoff (the control group).

The second-stage regression (estimated at the analyst-level) takes EPS Forecast Error as its de-

pendent variable and the predicted values of Legacy Spinoff from the first-stage regression as its

key independent variable. As such, this second-stage regression measures the relationship between

spinoff type (legacy or non-legacy) and forecast accuracy, controlling for non-random selection in

a manager’s decision to undertake one or the other type of spinoff in the first place.

Treatment effects models require the use of at least one instrumental variable to properly identify

the system of equations. These variables must be correlated with the endogenous variable of

interest (here, Legacy Spinoff), but orthogonal to the unobserved errors in the outcome variable in

the second-stage regression (here, EPS Forecast Error). I propose and employ two instruments to

identify this model: Lagged # M&A Deals in Legacy Industry and Sales Growth in Legacy Industry.

These two variables appear to satisfy both of the requirements for appropriate instruments.

Lagged # M&A Deals in Legacy Industry is the total number of mergers and acquisitions

undertaken by U.S.-based, single-business firms operating in the industries (represented by their

3-digit SIC codes) in which each firm’s legacy business operated, measured in the year prior to their

spinoffs. Managers should be more likely to undertake a legacy spinoff the higher the valuation

they expect the divested legacy business to attain after its spinoff, which would benefit their firm’s

shareholders because spinoffs are effected through a distribution of shares to existing investors.

Sales Growth in Legacy Industry is calculated as the sales growth rate of all single-business

firms operating in the industries (again represented by their 3-digit SIC codes) in which each firm’s

legacy business operated. Firms should be more likely to divest their legacy businesses when the

growth opportunities in these units’ industries are worse, and vice versa (Feldman, 2014).

15

With regard to the exclusion restriction, the errors in Lagged # M&A Deals in Legacy Industry

and Sales Growth in Legacy Industry are not expected to be systematically correlated with the

errors in the divesting firm’s EPS Forecast Error. Post-spinoff, a firm’s legacy business is no longer

part of its portfolio, so the conditions in the legacy industry are unlikely to affect analysts’ forecast

accuracy about the divesting firm. Even if industry conditions do influence post-spinoff forecast

accuracy (for example, via the quantity of resources that are redeployed from the legacy business

to the divesting firm’s remaining operations after the spinoff), this relationship should not be

very strong, given that a firm’s legacy business is typically much smaller than the divesting firm’s

remaining operations (Feldman, 2014). For similar reasons, while the conditions of the industry

in which the legacy business operates may have some influence on analysts’ pre-spinoff forecast

accuracy (since the legacy business is still part of the divesting firm at that time), that relationship

is also likely to be weak given the relatively small size of the firm’s legacy business.

As a robustness check, I also estimate the relationship between legacy spinoffs and forecast ac-

curacy using coarsened exact matching models (“CEM models”). CEM models are similar to treat-

ment effects models in that both are two-stage models in which the first-stage regression predicts

the likelihood that a firm will undertake a legacy rather than a non-legacy spinoff; the second-stage

regression then estimates the relationship between legacy spinoffs and forecast accuracy, using the

predicted values of the first-stage regression’s dependent variable as the key independent variable.

There are two major differences between CEM and treatment effects models. First, the first-stage

propensity regression is estimated using “coarsened” values of the independent variables. Second,

CEM models do not rely on instrumental variables for identification. As such, the two instruments,

Lagged # M&A Deals in Legacy Industry and Sales Growth in Legacy Industry, appear as inde-

pendent variables in both the first- and second-stage regressions. If I find support for Hypotheses

2a and 2b using these CEM models, it will suggest that the results of the treatment effects models

are not being driven by my choice of instruments (i.e., by the failure of the exclusion restriction).

Control Variables

Primary Industry Sales Growth is defined as the sales growth rate of all single-business firms

operating in a divesting firm’s primary industry (measured by its 3-digit SIC code), reflecting the

industry opportunities outside of its legacy business. Legacy Age is a firm’s age, since its legacy

16

business has been part of the firm since its inception. Firm Coverage Mismatch is the sales-weighted

segment-level coverage mismatch scores (Zuckerman, 1999, 2000). Segment-level coverage mismatch

scores are defined as one minus the ratio of the number of analysts specializing in an industry to

the maximum number of industry specialists covering any of the firms in that industry. Higher

values of Firm Coverage Mismatch indicate that a firm is covered by fewer specialists.

Excess Value is the natural log of the ratio of a firm’s total capital to the imputed value of the

sum of that company’s segments as stand-alone firms (Berger and Ofek, 1995). Accordingly, higher

Excess Value indicates that less value is being destroyed by diversification, since the company as a

whole is worth more than the imputed sum of its parts. ln(Total Assets) is the natural log of the

total assets of each divesting company. Leverage is the ratio of a firm’s debt to its total value, and

Capex/PPE is capital expenditures divided by net property, plant, and equipment.

Analyst Experience Covering Firm is the number of quarters an analyst has been covering

a company. Overall Analyst Experience is the number of quarters an individual has worked as

an analyst. Analyst Tenure with I-Bank is the number of quarters an analyst has been working

at a particular investment bank. Ranked Analyst takes the value one if an analyst is ranked in

Institutional Investor Magazine’s “All-America Research Team” rankings, and zero if not (Stickel,

1992). Ranked Bank takes the value one if an investment bank is listed in the Carter and Manaster

(1990) rankings of investment banks’ underwriting activity, and zero if not.

Results

Analysts’ Coverage Decisions

Table 1 presents the results of logistic regressions testing Hypotheses 1a and 1b. The positive

and significant coefficient on Legacy Spinoff in Regression (1) reveals that analysts are 14.2% more

likely to terminate coverage of firms that undertake legacy than non-legacy spinoffs, supporting

Hypothesis 1a. The positive and significant coefficient on Legacy Spinoff in Regression (2) reveals

that analysts are 12.4% more likely to initiate coverage of firms that undertake legacy rather than

non-legacy spinoffs, supporting Hypothesis 1b.

———— Table 1 here ————

17

Earnings Forecast Accuracy

Analysts Terminating Coverage

Tables 2 and 3 present the results of models testing Hypothesis 2a. Regressions (1) and (2) in

Table 2 are simultaneously-estimated, testing whether the pre-spinoff forecast accuracy of analysts

who terminate versus continue coverage of a firm differs depending on whether it undertakes a legacy

or a non-legacy spinoff. In Regression (1), the positive and significant coefficient on Legacy Spinoff

reveals that the analysts who terminate coverage of firms that undertake legacy spinoffs produce

less accurate pre-spinoff forecasts than analysts who stop covering firms that undertake non-legacy

spinoffs. In Regression (2), the coefficient on Legacy Spinoff is not significant, indicating that

the pre-spinoff forecast accuracy of analysts who continue their coverage does not vary by spinoff

type. A Wald test of the equality of these two coefficients is rejected at 5%. This means that the

difference between the forecast accuracy of analysts who terminate versus continue coverage of a

firm is larger when that firm undertakes a legacy rather than a non-legacy spinoff.

Regression (3) tests whether there are overall differences in the pre-spinoff forecast accuracy of

analysts who terminate versus continue coverage of firms that undertake any spinoff. The positive

and significant coefficient on Analyst Terminates Coverage indicates that analysts who terminate

coverage produce less accurate pre-spinoff forecasts than analysts who continue coverage. Regres-

sion (4) extends this result by testing whether this average effect varies with the type of spinoff un-

dertaken. The positive and significant coefficient on Analyst Terminates Coverage×Legacy Spinoff

indicates that the forecasts of analysts who terminate (rather than continue) coverage of firms that

undertake legacy spinoffs are significantly less accurate than their non-legacy peers.

The results of treatment effects and CEM models testing Hypothesis 2a appear in Table 3.

The dependent variable in Regression (1), the first-stage propensity regression of the treatment

effects model, is Legacy Spinoff. As predicted, the coefficient on the Lagged # M&A Deals in

Legacy Industry is positive and significant, suggesting that a firm is more likely to spin off its

legacy business the more M&A activity occurred in that unit’s industry. The coefficient on Legacy

Industry Sales Growth is negative and significant, implying that a firm is more likely to spin off its

legacy business the slower-growing is that unit’s industry. The results of the first-stage propensity

regression of the CEM model are virtually identical to those presented in Regression (1).

18

Regressions (2) and (3) present the results of the two simultaneously-estimated second-stage

regressions of the treatment effects model. In Regression (2), the positive and significant coefficient

on Legacy Spinoff reveals that analysts who terminate coverage of firms that undertake legacy

spinoffs produce less accurate pre-spinoff forecasts about these firms than they do about firms

that undertake non-legacy spinoffs. In Regression (3), however, the coefficient on Legacy Spinoff is

not significant, meaning that there is no difference between the forecast accuracy of analysts who

continue their coverage of firms that undertake legacy and non-legacy spinoffs. A Wald test of the

equality of these two coefficients is rejected at 10%.


regressions of the CEM model. Two noteworthy points emerge from these regressions. First,

the fact that the coefficients on Lagged # M&A Deals in Legacy Industry and Sales Growth in

Legacy Industry are not significant supports the intuition that these instrumental variables satisfy

the exclusion restriction in the treatment effects models. Second, and even more importantly,

the positive and significant coefficient on Legacy Spinoff in Regression (4) indicates that analysts

who terminate coverage of firms that undertake legacy spinoffs produced less accurate pre-spinoff

forecasts about those firms than about their non-legacy peers. In Regression (5), however, the

coefficient on Legacy Spinoff is not significant, suggesting that there is no difference between the

forecast accuracy of analysts who continue covering firms that undertake legacy and non-legacy

spinoffs. A Wald test of the equality of these coefficients is rejected at 5%.

Taken together, these findings provide further evidence in support of Hypothesis 2a, indicating

that the gap between the pre-spinoff forecast accuracy of analysts who terminate versus continue

coverage of firms that undertake legacy spinoffs is greater than it is for firms that undertake non-

legacy spinoffs, controlling for the effects of non-random selection.

———— Tables 2 and 3 here ————

Analysts Initiating Coverage

Tables 4 and 5 present the results of models testing Hypothesis 2b. Regressions (1) and (2) in

Table 4 are simultaneously-estimated, testing whether the post-spinoff forecast accuracy of analysts

who initiate versus continue coverage of a firm differs depending on whether it undertakes a legacy

19

or a non-legacy spinoff. In Regression (1), the negative and significant coefficient on Legacy Spinoff

reveals that analysts who initiate coverage of firms that undertake legacy spinoffs produce more

accurate forecasts than analysts who start covering firms that undertake non-legacy spinoffs. By

contrast, in Regression (2), the positive and significant coefficient on Legacy Spinoff indicates that

the forecasts produced by analysts who continue covering firms that undertake legacy spinoffs are

less accurate than those produced by analysts who continue covering firms that undertake non-

legacy spinoffs. A Wald test of the equality of these two coefficients is rejected at 5%. This means

that the difference between the forecast accuracy of analysts who initiate versus continue coverage

of a firm is larger when that firm undertook a legacy rather than a non-legacy spinoff.

Regression (3) tests whether there are overall differences in the post-spinoff forecast accuracy of

analysts who initiate versus continue coverage of firms that undertake any spinoff. The null coeffi-

cient on Analyst Initiates Coverage indicates that on average, there is no difference in the forecast

accuracy of analysts who initiate versus continue coverage of firms that undertake any spinoff. Re-

gression (4) extends this result by testing whether this null average effect differs between legacy and

non-legacy spinoffs. While the coefficient on Analyst Initiates Coverage remains insignificant, the

coefficient on Analyst Initiates Coverage×Legacy Spinoff is negative and highly significant. This

finding reveals that the forecasts of analysts who initiate (rather than continue) coverage of firms

that undertake legacy spinoffs are more accurate than their non-legacy counterparts.

The results of treatment effects and CEM models testing Hypothesis 2b appear in Table 5.

Regression (1), the first-stage propensity model, is identical to Regression (1) in Table 3.


regressions of the treatment effects model. In Regression (2), the negative and significant coefficient

on Legacy Spinoff suggests that analysts who initiate coverage of firms that undertake legacy

spinoffs produce more accurate earnings forecasts than their non-legacy counterparts. In Regression

(3), the positive and significant coefficient on Legacy Spinoff indicates that analysts who continue

covering firms that undertake legacy spinoffs produce less accurate earnings forecasts than their

non-legacy peers. A Wald test of the equality of these coefficients is rejected at 5%.


regressions of the CEM model. The negative and significant coefficient on Legacy Spinoff in Re-

gression (4) suggests that analysts who initiate coverage of firms that undertake legacy spinoffs

20

produce more accurate forecasts than their non-legacy peers. However, the positive and significant

coefficient on Legacy Spinoff in Regression (5) indicates that analysts who continue covering firms

that undertake legacy spinoffs produce less accurate forecasts than about firms that undertake

non-legacy spinoffs. A Wald test of the equality of these two coefficients is rejected at 1%.

These findings further support Hypothesis 2b, revealing that the gap between the post-spinoff

forecast accuracy of analysts who initiate versus continue coverage is greater when firms undertake

legacy rather than non-legacy spinoffs, controlling for non-random selection in the spinoff decision.

———— Tables 4 and 5 here ————

Post-Hoc Analyses

The mechanism theorized to be driving the results presented thus far is that legacy spinoffs may

induce analysts to surmount cognitive inertia, emanating from their original schemas deriving from

firms’ legacy businesses, in their coverage decisions. To shed light on this mechanism, I conduct

three sets of post-hoc analyses comparing the accuracy of the forecasts the analysts in my sample

produce about (a) the firms in my sample, to (b) all of the other firms these analysts follow. Results

appear in Table 6.


Regressions (1) and (2) consider the analysts who terminate coverage of the firms in my sample.

In Regression (1), the dependent variable is the likelihood that an analyst who terminates coverage

of one of the parent firms in my sample initiates coverage of its divested spinoff firm. The positive

and significant coefficient on Legacy Spinoff reveals that these terminating analysts are more likely

to go on to initiate coverage of the divested spinoff firms when the spinoff is legacy rather than

non-legacy. This finding supports the intuition that the analysts who terminate coverage of the

parent firms in my sample are specialized in the legacy industries of those companies.

Continuing in this vein, the dependent variable in Regression (2) is the forecast errors produced

by all of the analysts who are covering the spinoff firms that the parent companies in my sample

divest. The negative and significant coefficient on Analyst Terminates Coverage of Parent reveals

that the analysts who terminate coverage of one of the parent firms in my sample produce more

21

accurate forecasts about its spinoff firm than do all of the other analysts covering that spinoff firm.

The negative and significant coefficient on Analyst Terminates Coverage of Parent×Legacy Spinoff

reveals that this effect is pronounced when the spinoff firm is created by a legacy rather than a

non-legacy spinoff. This finding supports the idea that the cognitive inertia in analysts’ coverage

decisions derives from the parent firm’s legacy business, since the terminating analysts from my

sample produce more accurate forecasts about legacy spinoff firms (whose antecedent business units

determined these analysts’ initial coverage decisions) than they do about their non-legacy peers.

Regressions (3) and (4) consider the analysts who continue their coverage of the firms in my

sample, while Regressions (5) and (6) consider the analysts who initiate coverage of these firms.

The dependent variable in all four of these regressions is the forecast errors that analysts produce

about the parent firms in my sample and all of the other firms these same analysts cover. In

Regression (3), the positive and significant coefficient on Firm Undertook Spinoff indicates that the

continuing analysts produce less accurate forecasts about the firms in my sample than about all of

the other firms they follow. In Regression (5), however, the negative and significant coefficient on

Firm Undertook Spinoff reveals that the initiating analysts produce more accurate forecasts about

the firms in my sample than about all of the other firms they follow.

Together, these findings reinforce the intuition developed in the Theory section about the advan-

tages and disadvantages faced by continuing versus initiating analysts. For analysts who continue

covering a firm that undertakes a spinoff, the constraint imposed by their existing models for cover-

ing that firm outweighs the benefit of any accumulated experience, since they produce less accurate

forecasts about the firms in my sample than about any of the other firms they follow. For analysts

who initiate coverage of a firm that undertakes a spinoff, however, the benefit of starting to follow a

firm that they are specialized to cover outweighs their lack of experience, since they produce more

accurate forecasts about the firms in my sample than about any of the other firms they follow.

Regressions (4) and (6) disaggregate these average effects according to whether a spinoff is legacy

or non-legacy. In Regression (4), the positive and significant coefficients on Firm Undertook Legacy

Spinoff and Firm Undertook Non-Legacy Spinoff indicate that the continuing analysts produce less

accurate forecasts about the firms in my sample—whether their spinoffs are legacy or non-legacy—

than about the other firms they follow. Analogously, in Regression (6), the negative and significant

coefficients on Firm Undertook Legacy Spinoff and Firm Undertook Non-Legacy Spinoff indicate

22

that the initiating analysts produce more accurate forecasts about the firms in my sample—whether

their spinoffs are legacy or non-legacy—than about the other firms they follow.

In both regressions, Wald tests reveal that the magnitudes of the coefficients on Firm Undertook

Legacy Spinoff significantly exceed those of Firm Undertook Non-Legacy Spinoff. This finding

reinforces, in two ways, the idea that analysts’ original schemas and hence, the cognitive inertia in

their coverage decisions emanates from a firm’s legacy business. First, the constraints imposed by

the continuing analysts’ existing models for covering a firm are more binding when they derive from

its legacy (rather than a non-legacy) business. Second, the gains generated by specialized analysts

initiating coverage are greater when these analysts’ coverage decisions are formally decoupled from

the legacy (rather than a non-legacy) business. Thus, out of all of the firms that they cover,

continuing analysts produce the least accurate forecasts about firms that undertake legacy spinoffs,

while initiating analysts produce the most accurate forecasts about those same firms.

Overall Effects

This paper has documented that changes in the composition and quality of analyst coverage are

more pronounced when firms undertake legacy rather than non-legacy spinoffs. However, existing

research has established two empirical regularities that are consistent with these results. First,

forecast accuracy improves following spinoffs, as existing analysts find it easier to cover the divesting

firms (Bhushan, 1989; Gilson et al., 2001). Second, stock market performance improves when firms

undertake spinoffs (Daley et al., 1997; Desai and Jain, 1999; Bergh et al., 2008). Accordingly, it

is instructive to consider whether the unique shifts in analyst coverage that occur following legacy

spinoffs translate into overall improvements in forecast accuracy and stock market performance for

these firms in particular, above and beyond the improvements implied by existing explanations.

Table 7 tests whether the overall improvements in forecast accuracy and stock market perfor-

mance are more pronounced for legacy than non-legacy spinoffs. The dependent variables are EPS

Forecast Error in Regressions (1)-(3) and Compounded Annual Returns in Regressions (4)-(6). The

key independent variables are Post-Spinoff and Post-Legacy Spinoff. Post-Spinoff measures how

forecast accuracy and stock market performance change between the pre- and post-spinoff time

periods for all spinoffs, while Post-Legacy Spinoff represents the same change for legacy spinoffs.

In Regression (1), the coefficient on Post-Spinoff is negative and significant, suggesting that

23

forecast accuracy is higher post-spinoff than it was pre-spinoff. In Regression (2), the coefficient on

Post-Legacy Spinoff is also negative and significant, meaning that this effect holds for legacy spinoffs

in particular. While both the coefficients on Post-Spinoff and Post-Legacy Spinoff are negative in

Regression (3), only the coefficient on Post-Legacy Spinoff is significant. This finding indicates that

improvements in forecast accuracy are concentrated among firms that undertake legacy spinoffs.

I next investigate the relationship between legacy spinoffs and stock market performance. In

Regression (4), the coefficient on Post-Spinoff is positive, though it is not significant. The coefficient

on Post-Legacy Spinoff is positive and significant in Regression (5), and the same pattern repeats in

Regression (6). Together, these findings reveal that the improvements in stock market performance

that have been attributed to spinoffs are, in fact, concentrated among legacy spinoffs, introducing

an important caveat to the studies that have documented this empirical regularity regarding the

performance effects of spinoffs (Daley et al., 1997; Desai and Jain, 1999; Bergh et al., 2008).


Discussion and Conclusion

This paper has investigated how spinoffs improve the quality of analysts’ research. While the

literature attributes these improvements to spinoffs enabling existing analysts to better cover these

firms (Bhushan, 1989; Zuckerman, 2000; Gilson et al., 2001), the core conceptual insight to emerge

from this study is that spinoffs may offer members of the analyst community the opportunity to

revisit and improve their coverage decisions, contributing to the gains the literature has identified.

To explore this issue, I distinguish between legacy spinoffs, which involve a firm’s original line

of business, and their non-legacy peers. While all spinoffs reduce the economic constraints analysts

face against changing their coverage decisions, only legacy spinoffs induce analysts to surmount

the cognitive inertia in their coverage decisions by removing the specific businesses that may be

driving those decisions in the first place. Consistent with these arguments, I find that analysts

are more likely to terminate and initiate coverage of firms that undertake legacy rather than non-

legacy spinoffs. Analysts who terminate coverage of firms that undertake legacy spinoffs produce

less accurate pre-spinoff forecasts than analysts who continue coverage, and analysts who initiate

coverage produce more accurate post-spinoff forecasts than analysts who continue coverage. These

24

differences are concentrated among firms that undertake legacy rather than non-legacy spinoffs.

The results in this paper exhibit an important duality, in that the same transaction—a legacy

spinoff—prompts analysts who were not terminating coverage but should have been, as well as

analysts who were not initiating coverage but should have been, to change their initial coverage

decisions. These changes should be and are associated with a significant reduction in the average

forecast errors of firms that undertake legacy spinoffs. These same firms also enjoy unique improve-

ments in their stock market performance, over and above the gains enjoyed by their non-legacy

counterparts. Thus, these findings suggest that the effect of analysts revisiting and potentially

changing their coverage decisions is at least as important as the resolution of miscategorization by

existing analysts in explaining the economic benefits of spinoffs for shareholders.

These conceptual insights contribute to management theory in several important ways.

First, this study advances a novel explanation for the capital market inefficiencies that are

often experienced by diversified firms. The efficient markets hypothesis holds that markets are

informationally efficient, meaning that prices reflect all available information about the true value

of an asset (Fama, 1976). However, diversified firms are often undervalued relative to fundamentals,

that is, to the sum of the stand-alone values of all of their business segments (Berger and Ofek,

1995). These firms can experience severe problems as a result: elevated capital costs, distorted

managerial incentives, and a general mismatch between strategy choices and desired investor types.

One explanation that has been advanced for this discrepancy is that diversified firms, which operate

in multiple industrial categories, are often covered by analysts who specialize in one industry

(Zuckerman, 1999; Gilson et al., 2001). In this conceptualization, the undervaluation experienced

by diversified firms is an “illegitimacy discount” (Zuckerman, 1999), whereby these firms are viewed

as illegitimate by shareholders due to their failure to attain reviews by specialized analysts.

Distinct from the foregoing explanation, this study suggests that the undervaluation experienced

by diversified firms might instead be linked to inertial decision-making among analysts (Tripsas,

2009; Benner, 2010), who, as market intermediaries, drive investor behavior, and hence, share prices

(Benner, 2007). This explanation augments, rather than negates, the above-described categoriza-

tion story: analysts still specialize by industry, and it is precisely the fact that their initial coverage

decisions are based on the (mis-)match between their areas of specialization and the firm’s legacy

business that creates cognitive inertia in their coverage decisions. This cognitive inertia would be

25

expected to perpetuate the undervaluation of diversified firms, until legacy spinoffs alleviate the

problem by removing the particular business unit that is creating the inefficiency in the first place.

This intuition is reinforced by the finding that firms that undertake legacy spinoffs enjoy stock

market gains in excess of their non-legacy counterparts, and is consistent with studies showing

that discrepancies between stock market performance and economic fundamentals slowly resolve

themselves as new information is revealed (Benner and Ranganathan, 2013; Feldman, 2014).

On the topic of cognitive inertia, this study also indicates that organizational change may be

challenging for both internal and external constituents. Existing research has primarily focused

on the problems that internal organization members face in initiating and implementing major

corporate shifts. Their ability to do this is limited by routines, the “regular and predictable

behavioral patterns of firms” (Nelson and Winter, 1982: 14) that are the product of the accumulated

knowledge and experiences of organization members (Leonard-Barton, 1992; Teece et al., 1994).

The implementation of a major corporate change (like a spinoff) requires organization members to

undertake the slow and difficult process of modifying the routines they have accumulated over time

to guide their decision-making, making such strategic shifts internally challenging.

Analogously, this study suggests that external constituents like analysts may also find organiza-

tional change difficult. In steady state, analysts rarely change their coverage decisions (McNichols

and O’Brien, 1997; Rao et al., 2001; Mola et al., 2013), and this paper has explored how both eco-

nomic constraints and cognitive inertia might drive this phenomenon. Even though spinoffs relax

the economic constraints that limit analysts from changing their coverage decisions, these external

constituents may still be held back by their original schemas about the divesting firms from fully

responding to these strategic shifts. It is only when a firm undertakes a legacy spinoff—the most

gut-wrenching of all possible spinoffs, getting rid of the original business with which that firm has

been identified throughout its entire corporate history—that analysts are able to surmount the

cognitive inertia in their coverage decisions. Thus, the idea that inertia among external (rather

than internal) constituents might be organizationally costly is a novel contribution of this study.

In combination with existing insights about the stickiness of routines, moreover, this point further

implies that the successful implementation of organizational change requires managers to surmount

inertia among both their internal and external constituents. While managers may or may not un-

dertake legacy spinoffs (or even spinoffs in general) with the explicit intention of shaping analysts’

26

impressions of their firms, these strategies might therefore serve as a mechanism that managers can

actively use to accomplish this goal (Westphal and Clement, 2008; Westphal and Graebner, 2010).

Still further, this paper advances the important insight that both internal and external orga-

nizational constituents may experience a tradeoff between the benefits of experience and the costs

of cognitive inertia in their decision-making. For analysts, the constraints imposed by cognitive

inertia in their coverage decisions appear to predominate, as evidenced by two findings. First, con-

tinuing analysts, who should enjoy the greatest gains from their experience covering firms, in fact

produce less accurate research about firms that undertake spinoffs relative to the other firms that

they follow. This effect is especially strong when spinoffs render those analysts’ existing decision-

making processes least useful, as is the case for legacy spinoffs. Second, initiating analysts, who

should be the most constrained by their lack of experience, in fact produce more accurate research

about firms that undertake spinoffs than they do about the other firms that they follow. This effect

is especially strong when spinoffs enable those analysts to overcome the cognitive inertia in their

existing decision-making processes, as is also the case for legacy spinoffs. An interesting direction

for future research might be to consider how this tradeoff between experience and cognitive inertia

plays out among different decision-makers and in alternate empirical contexts.

Finally, this study reinforces the point that a company’s history can significantly affect its

corporate strategy. Not only do firms’ legacy businesses shape managers’ perceptions (Prahalad

and Bettis, 1986), as well as firms’ core competences (Leonard-Barton, 1992) and hence, their

diversification strategies (Teece et al., 1994; Feldman, 2014), but these units also exert a major

influence on how the members of the financial community perceive these companies, as revealed by

the work in this paper. These ideas suggest that a deeper understanding of the role that companies’

historical antecedents play in shaping their current strategy and performance may be necessary.

In summary, this paper has considered how spinoffs improve the quality of analysts’ research

about diversified firms, theorizing and finding evidence that legacy spinoffs induce analysts to

revisit and improve their earlier coverage decisions. Analysts are more likely to terminate and

initiate coverage of firms that undertake legacy rather than non-legacy spinoffs, resulting in unique

improvements in the quality of their overall forecast accuracy and those firms’ stock market perfor-

mance. These findings reveal that the effect of analysts revisiting their earlier coverage decisions

contributes significantly to the economic benefits of spinoffs for shareholders.

27

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31

Fig

ure

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Tem

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com

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32

Tab

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Pro

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sto

term

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Dep

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(2)

Analy

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tes

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1.3

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Indust

rySale

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(1.2

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(0.3

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Pri

mary

Indust

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Leg

acy

Age

0.0

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05)

(0.0

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age

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(0.1

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ant

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0.1

33

Table 2: Pre-spinoff forecast errors among analysts who terminate versus continue coverage

DV: EPS Forecast Error (1) (2) (3) (4)

Legacy Spinoff 0.009** 0.001(0.005) (0.003)

Analyst Terminates Coverage 0.001** -0.002(0.000) (0.002)

Analyst Terminates Coverage×Legacy Spinoff 0.001**(0.000)

Primary Industry Sales Growth 0.014 0.003 0.001 0.001(0.010) (0.003) (0.011) (0.011)

Legacy Age -0.000* -0.000 0.002 0.002(0.000) (0.000) (0.003) (0.003)

Firm Coverage Mismatch 0.000 0.007** 0.023* 0.023*(0.006) (0.003) (0.012) (0.012)

Excess Value 0.020** 0.001 -0.019 -0.019(0.008) (0.005) (0.060) (0.060)

ln(Total Assets) -0.000 0.001 -0.046* -0.046*(0.004) (0.001) (0.023) (0.023)

Leverage 0.049*** 0.008 0.119** 0.119**(0.015) (0.008) (0.051) (0.051)

Capex/PPE 0.097** 0.011 0.088 0.087(0.036) (0.017) (0.070) (0.070)

Analyst Experience Covering Firm -0.754*** -0.751*** -0.610*** -0.663**(0.297) (0.280) (0.280) (0.303)

Overall Analyst Experience -0.169** -0.400 -0.342 -0.369(0.073) (0.629) (0.425) (0.428)

Analyst Tenure with I-Bank -0.316*** -0.166 -0.381 -0.365(0.110) (0.784) (0.331) (0.304)

Ranked Analyst -0.171** -0.659 -0.070 -0.097(0.070) (0.404) (0.120) (0.135)

Ranked Bank -0.417*** -0.058 -0.309 -0.298(0.180) (0.103) (0.505) (0.478)

Constant -0.031 -0.009 0.294 0.295(0.031) (0.011) (0.179) (0.179)

Deal Fixed Effects No No Yes YesYear Fixed Effects Yes Yes Yes YesR2 0.826 0.513 0.464 0.464Observations 736 1,534 2,270 2,270Period 1 1 1 1Analysts Term (A2) Cont (A1) All (A2 & A1) All (A2 & A1)

Robust standard errors clustered by deal in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

34

Table 3: Pre-spinoff forecast errors among analysts who terminate versus continue coverage, treat-ment effects and coarsened exact matching models

Model Type Treatment Effects Coarsened Exact MatchingDependent Variable (1) LS (2) EPS FE (3) EPS FE (4) EPS FE (5) EPS FE

Legacy Spinoff 0.026*** -0.002 0.011*** 0.000(0.006) (0.005) (0.004) (0.001)

Lagged # M&A Deals in Legacy Industry 0.013** 0.000 0.000(0.005) (0.000) (0.000)

Legacy Industry Sales Growth -1.589** 0.007 -0.000(0.636) (0.021) (0.002)

Primary Industry Sales Growth 1.889** 0.006 0.003 0.013* 0.003(0.855) (0.007) (0.003) (0.008) (0.003)

Legacy Age 0.067* 0.000*** 0.000*** 0.000*** 0.000**(0.036) (0.000) (0.000) (0.000) (0.000)

Firm Coverage Mismatch 1.318 0.004 0.007*** 0.007 0.007***(1.303) (0.003) (0.001) (0.005) (0.001)

Excess Value 0.652 0.023*** 0.002 0.009 0.001(3.181) (0.006) (0.003) (0.006) (0.003)

ln(Total Assets) -0.569 0.001 0.001 0.002 0.001*(0.459) (0.002) (0.001) (0.002) (0.001)

Leverage 1.622* 0.055*** 0.010** 0.071*** 0.009(0.852) (0.007) (0.004) (0.015) (0.007)

Capex/PPE 0.961 0.147*** 0.011 0.129*** 0.011(0.903) (0.023) (0.008) (0.027) (0.007)

Analyst Experience Covering Firm -0.924 -0.753** -0.865 -0.764***(0.796) (0.360) (0.641) (0.256)

Overall Analyst Experience -0.152* -0.380 -0.114*** -0.395(0.079) (0.371) (0.043) (0.480)

Analyst Tenure with I-Bank -0.265*** -0.169 -0.239*** -0.159(0.082) (0.337) (0.074) (0.592)

Ranked Analyst -0.199*** -0.884 -0.120* -0.658(0.071) (0.722) (0.065) (0.934)

Ranked Bank -0.313* -0.061 -0.505*** 0.055(0.181) (0.108) (0.155) (0.089)

Constant -3.099** -0.041** -0.009 -0.032** -0.009*(1.493) (0.021) (0.006) (0.017) (0.005)

Inverse Mills Ratio -0.016*** -0.002**(0.004) (0.001)

Year Fixed Effects No Yes Yes Yes YesObservations 61 736 1,534 703 508Unit of Analysis Spinoff Analyst Analyst Analyst AnalystPeriod - 1 1 1 1Analysts - Term (A2) Cont (A1) Term (A2) Cont (A1)

Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

35

Table 4: Post-spinoff forecast errors among analysts who initiate versus continue coverage

DV: EPS Forecast Error (1) (2) (3) (4)

Legacy Spinoff -0.049*** 0.035***(0.011) (0.009)

Analyst Initiates Coverage 0.000 0.000(0.001) (0.001)

Analyst Initiates Coverage×Legacy Spinoff -0.006***(0.002)

Primary Industry Sales Growth 0.114*** 0.003 0.074*** 0.074***(0.015) (0.030) (0.002) (0.002)

Legacy Age 0.001*** -0.000** 0.001** 0.001**(0.000) (0.000) (0.000) (0.000)

Firm Coverage Mismatch 0.019* 0.021** 0.016*** 0.016***(0.011) (0.010) (0.001) (0.001)

Excess Value 0.018*** -0.012 -0.002 -0.002(0.004) (0.007) (0.005) (0.005)

ln(Total Assets) -0.030*** -0.029*** -0.001 -0.001(0.003) (0.007) (0.003) (0.003)

Leverage 0.063*** 0.054 0.053* 0.052*(0.017) (0.053) (0.031) (0.029)

Capex/PPE 0.290*** 0.268*** 0.028 0.033(0.045) (0.084) (0.055) (0.057)

Analyst Experience Covering Firm -0.774*** -0.297*** -0.584*** -0.529***(0.225) (0.113) (0.207) (0.196)

Overall Analyst Experience -0.155*** -0.396*** -0.357** -0.294***(0.053) (0.010) (0.160) (0.130)

Analyst Tenure with I-Bank -0.109 -0.056 -0.281 -0.120(0.101) (0.794) (0.367) (0.376)

Ranked Analyst -0.878 -0.604 -0.303 -0.298(0.843) (0.601) (0.262) (0.288)

Ranked Bank -0.078 -0.183 -0.022 -0.198(0.522) (0.807) (0.367) (0.159)

Constant 0.038*** -0.026 0.051* 0.052**(0.012) (0.040) (0.025) (0.025)

Deal Fixed Effects No No Yes YesYear Fixed Effects Yes Yes Yes YesR2 0.716 0.796 0.624 0.633Observations 1,330 1,534 2,864 2,864Period 2 2 2 2Analysts Init (B) Cont (A1) All (B & A1) All (B & A1)


*** p<0.01, ** p<0.05, * p<0.1

36

Table 5: Post-spinoff forecast errors among analysts who initiate versus continue coverage, treat-ment effects and coarsened exact matching models

Model Type Treatment Effects Coarsened Exact MatchingDependent Variable (1) LS (2) EPS FE (3) EPS FE (4) EPS FE (5) EPS FE

Legacy Spinoff -0.050*** 0.070*** -0.053*** 0.036***(0.008) (0.010) (0.009) (0.005)

Lagged # M&A Deals in Legacy Industry 0.013** -0.000 0.000(0.005) (0.000) (0.000)

Legacy Industry Sales Growth -1.589** -0.027 0.004(0.636) (0.022) (0.008)

Primary Industry Sales Growth 1.889** 0.114*** 0.715 0.132*** -0.015(0.855) (0.014) (0.838) (0.016) (0.019)

Legacy Age 0.067* 0.001*** -0.000*** 0.001*** -0.000***(0.036) (0.000) (0.000) (0.000) (0.000)

Firm Coverage Mismatch 1.318 0.019** 0.017*** 0.021* 0.016***(1.303) (0.007) (0.003) (0.012) (0.004)

Excess Value 0.652 0.018*** -0.012*** 0.018*** -0.014***(3.181) (0.003) (0.002) (0.005) (0.003)

ln(Total Assets) -0.569 -0.030*** -0.030*** -0.028*** -0.031***(0.459) (0.002) (0.002) (0.004) (0.003)

Leverage 1.622* 0.063*** 0.060*** 0.067*** 0.073**(0.852) (0.015) (0.021) (0.016) (0.034)

Capex/PPE 0.961 0.290*** 0.293*** 0.313*** 0.311***(0.903) (0.039) (0.032) (0.031) (0.056)

Analyst Experience Covering Firm -0.816*** -0.286*** -0.851*** -0.278***(0.171) (0.077) (0.140) (0.099)

Overall Analyst Experience -0.141* -0.229*** -0.147** -0.246***(0.093) (0.079) (0.057) (0.093)

Analyst Tenure with I-Bank -0.113 -0.419 -0.250 -0.615(0.123) (0.594) (0.281) (0.638)

Ranked Analyst -0.090 -0.268 -0.051 -0.296(0.110) (0.543) (0.086) (0.494)

Ranked Bank -0.111 -0.005 -0.093 -0.069(0.428) (0.204) (0.218) (0.182)

Constant -3.099** 0.003 -0.217*** -0.043** -0.011(1.493) (0.020) (0.016) (0.019) (0.024)

Inverse Mills Ratio -0.019** -0.022***(0.009) (0.005)

Year Fixed Effects No Yes Yes Yes YesObservations 61 1,330 1,534 754 558Unit of Analysis Spinoff Analyst Analyst Analyst AnalystPeriod - 2 2 2 2Analysts - Init (B) Cont (A1) Init (B) Cont (A1)

Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

37

Tab

le6:

Pos

t-h

oc

anal

yse

s

Dep

enden

tV

ari

able

(1)

Pr(

Init

Cvg

of

Spin

)(2

)Spin

EP

SF

E(3

)E

PS

FE

(4)

EP

SF

E(5

)E

PS

FE

(6)

EP

SF

E

Leg

acy

Spin

off

(vs.

Non-L

egacy

Spin

)5.4

87**

(2.5

56)

Analy

stT

erm

Cvg

of

Par

-0.0

11*

(0.0

05)

Analy

stT

erm

Cvg

of

Par×

LS

(vs.

NL

S)

-0.0

14**

(0.0

05)

Fir

mU

nder

took

Spin

off

(vs.

No

Spin

)0.0

07***

-0.0

09***

(0.0

02)

(0.0

04)

Fir

mU

nder

took

LS

(vs.

No

Spin

)0.0

09***

-0.0

15**

(0.0

03)

(0.0

06)

Fir

mU

nder

took

NL

S(v

s.N

oSpin

)0.0

02***

-0.0

09***

(0.0

01)

(0.0

02)

Contr

ols

Yes

Yes

Yes

Yes

Yes

Yes

Fir

mF

ixed

Eff

ects

No

Yes

Yes

Yes

Yes

Yes

Yea

rF

ixed

Eff

ects

Yes

Yes

Yes

Yes

Yes

Yes

R2

-0.1

39

0.0

98

0.0

98

0.1

27

0.1

28

Obse

rvati

ons

736

3,8

35

5,8

81

5,8

81

4,0

84

4,0

84

Analy

sts

Ter

m(A

2)

Cov

erin

gSpin

off

sC

ont

(A1)

Cont

(A1)

Init

(B)

Init

(B)

Fir

ms

Cov

ered

Pare

nts

Spin

off

sP

are

nts

&IB

ES

Univ

erse

Pare

nts

&IB

ES

Univ

erse

Rob

ust

stan

dard

erro

rscl

ust

ered

by

firm

inp

are

nth

eses

.

***

p<

0.0

1,

**

p<

0.0

5,

*p<

0.1

38

Table 7: Changes in forecast errors and stock market performance following spinoffs

Dependent Variable EPS Forecast Errors Compounded Annual ReturnsRegression (1) (2) (3) (4) (5) (6)

Post-Spinoff -0.001*** -0.001 0.078 0.120(0.000) (0.001) (0.155) (0.163)

Post-Legacy Spinoff -0.003** -0.004** 0.350** 0.368**(0.001) (0.002) (0.174) (0.187)

Primary Industry Sales Growth 0.001** 0.001 0.001 0.079 0.055 0.051(0.001) (0.001) (0.001) (0.088) (0.085) (0.087)

Legacy Age 0.004*** 0.002*** 0.002*** 0.039 0.012 0.046(0.001) (0.001) (0.001) (0.046) (0.017) (0.048)

Firm Coverage Mismatch 0.006* 0.005 0.006 0.014 0.041 0.059(0.003) (0.005) (0.005) (0.435) (0.421) (0.427)

Excess Value -0.017*** -0.001 -0.002 0.224 0.227 0.230(0.004) (0.010) (0.011) (0.161) (0.165) (0.164)

ln(Total Assets) -0.016*** -0.016*** -0.016*** 0.363 0.374 0.354(0.004) (0.005) (0.005) (0.419) (0.409) (0.394)

Leverage 0.049*** 0.047*** 0.048*** -1.299* -1.224* -1.215*(0.008) (0.013) (0.013) (0.692) (0.636) (0.620)

Capex/PPE 0.033* 0.034** 0.034** -1.937 -2.215* -2.158*(0.019) (0.015) (0.016) (1.245) (1.301) (1.271)

Analyst Experience Covering Firm -0.224** -0.194 -0.198(0.109) (0.145) (0.145)

Overall Analyst Experience -0.154 -0.098 -0.088(0.103) (0.148) (0.149)

Analyst Tenure with I-Bank -0.193 -0.058 -0.038(0.144) (0.129) (0.132)

Ranked Analyst -0.307 -0.563 -0.663(1.024) (0.957) (0.960)

Ranked Bank 0.595* 0.581* 0.591*(0.325) (0.314) (0.316)

Constant 0.031 0.009 0.063 -4.839 -3.135 -5.317(0.086) (0.047) (0.072) (5.276) (3.330) (5.272)

Deal Fixed Effects Yes Yes Yes Yes Yes YesYear Fixed Effects Yes Yes Yes Yes Yes YesR2 0.388 0.302 0.404 0.477 0.498 0.502Observations 5,452 5,452 5,452 225 225 225Unit of Analysis Analysts Analysts Analysts Firms Firms Firms


*** p<0.01, ** p<0.05, * p<0.1

39

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