Analysts’ Influence on Firms’ Voluntary Disclosures

Kimball Chapman

Washington University in St. Louis

Jeremiah Green*

Pennsylvania State University

December 2015

Abstract

We provide evidence that analysts influence managers’ disclosure decisions. Analysts’ questions

during earnings conference calls prompt guidance in subsequent quarters’ conference calls and

earnings announcements. However, analysts’ ability to mitigate that managers from small, low

earnings, and high earnings volatility companies are less willing to provide guidance is limited to

guidance about capital expenditures and taxes.

Keywords: Information demands, information intermediaries, voluntary disclosure

*Kimball is at Olin Business School at Washington University in St. Louis and his email is

kimballchapman@wustl.edu. Jeremiah is at the Smeal College of Business at the Pennsylvania State University and

his email is green@psu.edu. We are indebted to the following for their helpful feedback: Mark Bradshaw (editor), two

anonymous reviewers, Orie Barron, Dane Christensen, Kai Du, Dan Givoly, Guogin Gong, Vilmos Misangyi, Brian

Miller, Karl Muller, Anywhere Sikochi, Jake Thornock, Luke Watson, and Biqin Xie. We thank Andy Leone for

posting Perl code used in collecting and analyzing data in this paper. This paper benefited from feedback provided by

participants in a workshop at Penn State.

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Introduction

In this paper, we ask whether analysts influence managers’ disclosure decisions. Discussions

with managers and analysts1 suggest that analysts play an important role in shaping voluntary

disclosure policies by requesting important information and by informally guiding managers

towards enhanced disclosure.

To study analysts’ influence on disclosure, we use analyst and manager interactions during

earnings’ conference calls. We find that analysts’ questions during earnings conference calls

prompt managers’ guidance in subsequent quarters in the prepared remarks of conference calls and

in earnings announcements. The effect is economically meaningful. We find that when analysts

request guidance, managers are on average 3.25%2 more likely to add the requested guidance to

the prepared remarks of subsequent conference calls. This increase is economically significant

given that the average unconditional rate of providing guidance is 21%3.

To better understand the implications of analysts’ influence on managers’ guidance, we also

ask whether analysts’ questions contribute to or offset managers’ other disclosure incentives. We

do so with three sets of tests described below. As an example of the importance of these tests, we

find that whether managers provide guidance is positively correlated with earnings and market

1 Following the recommendation of an anonymous reviewer, we spoke with several managers and analysts. All of

them agreed that analysts shape the disclosures that managers make. However, there was no consensus on the extent

of the effect that analysts have on disclosure decisions. Analysts’ anecdotes suggest that at times analysts may directly

provide feedback to managers on what they should disclose. Managers’ anecdotes suggest that at times managers

directly seek or accept feedback from analysts on their disclosures and other times they infer important items that need

to be disclosed from analysts’ requests. 2 The mean of the marginal effects of Analyst ask in Table 4. 3 The mean of the percent of conference calls with forecasts (Table 1 Panel B). To be clear, this is the average

percentage of firm-quarters in our sample in which managers provide a forecast of the six types of information studied

in this paper. Because some firms that provide forecasts do so less frequently than quarterly, this number is not

representative of the overall proportion of firms in our sample that would be considered forecasters in that they provide

some forecast during the year. Panel D of Table 2 provides an analysis of the proportion of firms in our sample that

provide forecasts at least one quarter during a year for the types of information we study.

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capitalization and negatively correlated with earnings volatility. If analysts are also more likely to

ask for guidance from managers at large companies with high earnings and low earnings volatility

then analysts’ influence on disclosure has different implications than if analysts’ questions are

unrelated to these firm characteristics or offset these disclosure incentives.

For the three sets of tests, first, we estimate a model of the determinants of analysts’ questions

using variables that have been used in prior research to explain managers’ guidance behavior. For

the most part, we find that the relations between firm characteristics and analysts’ questions are

similar to the relations between firm characteristics and managers’ guidance (i.e. generally

speaking, managers’ guidance and analysts’ requests for guidance are both positively correlated

with earnings and market capitalization and negatively correlated with earnings volatility). One

exception is that analysts’ requests for cash flow related guidance are negatively correlated with

earnings while managers’ cash flow related guidance is positively correlated (or uncorrelated) with

earnings. Second, we directly test whether analysts’ questions mitigate managers’ disclosure

incentives coming from earnings, size, and volatility. We only find limited evidence that analysts

mitigate managers’ disclosure incentives. Specifically, analysts’ questions mitigate the earnings

relation with tax guidance as well as the size and earnings volatility relations with capital

expenditure guidance. Third, we find that managers’ disclosure incentives extend to their decisions

to respond to requests for guidance. In general, managers from companies with higher earnings,

larger market capitalization, and lower earnings volatility are also more likely to respond to

analysts’ requests by providing guidance in future quarters.

Together, these tests provide context to analysts’ influence on managers’ guidance. Although

there is some limited evidence that analysts challenge managers to provide guidance when they

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might not otherwise do so, this does not appear to generally be the case.4 Outside of the few

exceptions, analysts tend to instigate guidance from managers that may already be predisposed to

provide it.

Our approach focuses on forward-looking information disclosed during the prepared remarks

of earnings conference calls because these disclosures are purely voluntary and are not provided

by all firms (Heitzman, Wasley, and Zimmerman, 2010; Tasker, 1998; Bushee, Matsumoto, and

Miller, 2003). The conference call setting is also used by a large number of firms on a regular basis

to provide information and facilitate manager interactions with external parties. Furthermore,

analysts’ questions during earnings conference calls are publicly observable interactions where

analysts convey their demand for information directly to managers.5 We collect analysts’ questions

about expected capital expenditures (CAPEX), cash flow, EBITDA, earnings per share (EPS),

operating margin, and taxes6 during the question and answer period of earnings conference calls

4 An alternative interpretation is that analysts’ requests for guidance coincide with managers’ willingness to provide

guidance, but that they challenge managers’ disclosures in other ways. In untabulated analysis, we find that the

relations between earnings, market capitalization, and earnings volatility are similar for analysts’ questions that are

about current or past items rather than guidance about future items suggesting that the results from our tests may

generalize to analysts’ requests for other information. 5Although we use analysts’ questions during earnings conference calls as a measure of analysts’ demands for

disclosure from managers, we acknowledge that this is not the only nor even the most important manner in which

analysts’ demands for disclosure are communicated to managers. In several of our discussions with them, managers

and analysts agree that managers learn about additional disclosures that they might need to provide from analysts.

However, the examples these managers and analysts offered us show that analysts also interact with managers outside

of conference calls and other public settings to convey their thoughts on managers’ disclosure policies. This is

consistent with research that highlights the importance of private interactions between managers and analysts (Soltes,

2014; Brown, Call, Clement, and Sharp, 2015). By using analysts’ questions during earnings conference calls, we

assume that the public requests for disclosure are correlated with the private requests. Our discussions with managers

and analysts suggest that this assumption is reasonable. Additionally, if these public requests for disclosure are

correlated with, but are a subset of, the demands from analysts for enhanced disclosure, then we expect our empirical

approach to understate the effect of analysts on managers’ disclosure policies. (We are grateful to an anonymous

referee for pointing out other ways in which analysts interact with managers and for the recommendation to discuss

these topics with managers and analysts to receive their feedback.) 6 We select these six types of information following prior research (Chuk, Matsumoto, and Miller, 2013) and a 2014

survey (suggested by an anonymous reviewer) that suggest these categories are the most common types of guidance

provided by managers (Niri, 2014). We also attempted to include revenue guidance, but in untabulated tests of a

small, random sample of the output from our algorithm, revenue guidance had a much higher error rate than any of

the other types of guidance. In this random sample of 560 observations (80 for each of 7 types of guidance), we find

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and map these topics to managers’ guidance in the prepared remarks of subsequent conference

calls7.

As part of our effort to control for alternative explanations for the inference that analysts

causally influence managers’ voluntary disclosure practices, we use several methodological

choices. Noting the possible concern that a variable that drives analysts’ requests and managers’

guidance is omitted from our model, our first attempt at mitigation is to include other variables

that might drive analysts’ requests and managers’ guidance. The effect of analysts’ requests on

managers’ disclosures is incremental to institutional investor holdings, industry-level disclosure

patterns, firm characteristics, analysts’ prior requests for guidance, managers’ prior guidance, and

other controls for the determinants of managers’ guidance. Because it is possible that even with

these controls our results may be spurious, we also include analysts’ non-forward looking

questions made during the same conference call in which analysts request a forecast to control for

general interest in the topics that are the subject of the requested guidance.

We also attempt to address the omitted variable concern by showing that our results hold for

the sub-sample of firms/quarters in which there has been no guidance provided in the prior year.

The results for this subsample provide comfort that the findings are not driven by previous

disclosure patterns that persist over time. Finally, we use statistical methods to control for possible

concerns about the endogeneity between analysts’ questions and managers’ guidance. We use an

entropy balancing method that helps create balanced samples between company-quarters in which

our algorithm to be successful in more than 80% of cases for forecasts of CAPEX, cash flows, and tax rates; more

than 75% of the cases for EBITDA and operating margin; and more than 70% of cases for earnings per share (EPS).

However, the success rate for revenue forecasts is approximately 55%. 7 For expedience we report tests using guidance provided by managers four quarters after the requests made by

analysts. In untabulated results, we also estimate the model of managers’ guidance using future guidance of one to

eight quarters ahead and find that the results are similar at all horizons.

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analysts requested a forecast (treatment observations) and those in which no forecast was requested

(control observations) (Hainmueller, 2012; McMullin and Schonberger, 2015). Our findings are

robust to this approach.8

Together, our findings provide evidence that analysts influence managers’ disclosures but that

analysts can only partially overcome managers’ guidance incentives. Our findings suggest that

there is a robust, important, and practical channel through which analysts influence corporate

voluntary disclosure practices. Our study, in conjunction with other recent research (e.g., Jung,

2013), advances the understanding in the literature of how information environments develop.

1 Background and hypothesis development

In this section, we provide background information and develop the hypotheses tested in the

paper. Our primary hypothesis is that analysts influence managers’ voluntary disclosure choices.

While developing this paper, we discussed voluntary disclosure choices with a small number of

analysts and managers. The useful insights we gained that were common across our conversations

with analysts and managers can be generalized with the following points. First, managers generally

want to disclose information that will most help external parties value the company and understand

its performance. Second, managers do not always know the content and format of voluntary

8 We select this approach because using propensity score matching we are unable to achieve a covariate balance

between control and treatment firms. The entropy balancing approach effectively forces covariate balance, making

treatment and control firms comparable thereby enhancing the causal interpretation of the results. Additionally, in

untabulated analysis, we replicate our tests of the effect of analysts’ requests on managers’ disclosures using an

instrumental variables approach with measures for the busyness of analysts on the day of the conference call as

exogenous instruments and find similar results. Regarding our instrumental variables approach, we find some evidence

that analysts make fewer requests for information when they participate in more calls on the day of the conference

call, when there are more calls in the same industry, or the analyst issues a larger number of reports. Although we

replicate our main results using this approach, our tests suggest that our instruments are weak (Larcker and Rusticus,

2010).

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disclosures that will be most useful. Third, managers, at times, receive direct input from external

parties such as analysts to inform their disclosure decisions. At other times, managers learn about

the need to make changes in their disclosures by observing the questions they are asked. Lastly,

managers are reluctant to add additional disclosures that will set a costly precedent for future

disclosure.

Consistent with this feedback we received privately, managers sometimes publicly reveal that

they change disclosures in response to feedback or that they are soliciting feedback on their

disclosures. For example, in its conference call for the third quarter of 2008, Developers

Diversified Realty states, “First, at the request of several analysts, we added disclosure to our

quarterly supplement....” In its conference call for the fourth quarter of 2004 Allstate says, “As

many of you know, we’ve been soliciting feedback….”

Consistent with the insight from our discussions with managers and analysts and in support of

managers’ incentive to respond to analysts’ information requests, prior literature documents

several potential benefits of providing informative disclosures. First, high-quality disclosures may

decrease costs of capital (Botosan, 1997; Botosan and Plumlee, 2002; Easley and O’Hara, 2004;

Lambert, Leuz, and Verrecchia, 2007).9 Second, prior research shows that disclosure quality is

positively associated with trading liquidity (Diamond and Verrecchia, 1991; Healy, Hutton, and

Palepu, 1999). Third, prior research documents that high-quality disclosures improve a firm’s

information environment and attract new investors in ways that are aligned with managers’

incentives. Lang and Lundholm (1996) demonstrate that firms with higher levels of voluntary

disclosure often have higher analyst coverage and more accurate and less disperse analyst

9 Subsequent literature questions whether disclosure quality should be associated with stock prices in a large economy

because firm-specific risk is diversifiable (Hughes, Liu, and Liu, 2007).

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forecasts. Healy, Hutton, and Palepu (1999) observe a positive association between disclosure

quality and institutional ownership. Bushee and Miller (2012) and Kirk and Vincent (2014) also

find that investor relations activities increase the quantity of disclosure and increase equity

valuation, analyst and media coverage, institutional ownership, and trading liquidity. A final

incentive managers may have to provide high-quality disclosure is to reduce potential litigation

costs. Field, Lowry, and Shu (2005) suggest that disclosures may reduce the likelihood of future

litigation and Skinner (1997) shows that disclosures reduce litigation settlement costs.

However, increasing or changing disclosures is potentially costly for managers. As modeled by

Einhorn and Ziv (2008), providing new disclosures changes investor expectations about a

manager’s future information endowment such that investors are more likely to infer a negative

signal from future nondisclosure. This gives rise to an implicit commitment to future disclosure

that is costly for managers because future non-disclosure is more likely to result in a negative stock

price reaction. Consistent with this prediction, survey evidence confirms that managers are wary

of providing new disclosures; Graham, Harvey, and Rajgopal (2005) document that 69.6% of

survey respondents acknowledge that avoiding disclosure precedents that may be difficult to

maintain in the future is a significant constraint on current disclosures. In addition to costs arising

from implicit commitments to future disclosures, managers may incur other costs when revising

or adding new disclosures, such as information processing costs of collecting information,

opportunity costs of adding disclosures within space limitations imposed by various disclosure

venues, and potential reputation and litigation costs arising from incorrect predictions of uncertain

future outcomes (Beyer and Dye, 2012; Rogers and Van Buskirk, 2009)10.

10 In relation to conference calls, Hollander, Pronk, and Roelofsen (2010) document various conditions under which

managers provide partial or incomplete responses to questions on conference calls.

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Despite the importance of understanding how information demands are transmitted and

translated into corporate disclosures, extant literature on the determinants of disclosure provides

only limited insight into how information demands shape disclosure choices. The existing evidence

is limited to associations between measures of investor composition or measures of information

asymmetry and firm disclosures. For example, Bushee and Noe (2000) and Healy, Hutton, and

Palepu (1999) document a positive association between measures of disclosure quality and

institutional investor ownership, consistent with managers changing their disclosures in order to

satisfy investor information demands. Other examples include evidence of managers providing

“open” conference calls based on investor composition (Bushee, Matsumoto, and Miller, 2003), a

positive association between disclosure frequency and information asymmetry (Botosan and

Harris, 2000), and increased disclosures when accounting information is less informative or after

high-profile accounting scandals (Chen, DeFond, and Park, 2002; Wasley and Wu, 2006).

Although these studies provide reasonable explanations for a demand effect in firms’ voluntary

disclosures, the mechanisms through which demands for information are conveyed to and

influence disclosure remain untested. An exception to this, Jung (2013), is one of the first to

attempt to find causal evidence of the effects of investor demand on corporate disclosures.11 Jung

(2013) conjectures that investors’ demand is transmitted to managers through repeated interactions

and finds that investors influence corporate disclosure.

11 Jung (2013) does not find evidence that analyst coverage is incremental to institutional investor ownership in

explaining increases in management disclosure. Jung (2013) uses an inventive approach by looking at changes in

disclosures for firms with overlapping institutional investor holdings. He infers that these changes in disclosure

occur through repeated interactions between investors and managers. The primary difference and why we find

evidence of an effect by analysts on management disclosures is that we measure explicit demands from analysts for

specific information. In this way we complement the findings in Jung (2013) by measuring one of these repeated

interactions and their effects on disclosure.

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Although managers may initially placate analysts by immediately answering their questions or

providing answers to their questions in the short-term (Hollander, Pronk, and Roelofsen, 2010),

this is distinct from how analysts’ requests for information inform managers’ voluntary disclosure

practices beyond the short-term. If analysts influence managers’ disclosure practices rather than

merely instigate a response, then the effect of analysts’ questions should be evident beyond

managers’ immediate answers. This leads to the primary hypothesis of the paper (stated in the

alternative form):

H1: Analysts’ requests for information increase the probability that managers disclose the

requested information in future periods.

Our primary hypothesis is that analysts influence the guidance that managers provide. However,

the context in which analysts influence guidance is important because it affects the implications

that can be made from observing their influence. For instance, managers provide more frequent

voluntary disclosures when earnings performance is increasing (Miller, 2002) and disclose good

news quickly while withholding bad news (Graham, Harvey, and Rajgopal, 2005; Kothari, Shu,

and Wysocki, 2009). If analysts ask questions to demand disclosure when managers are otherwise

unwilling to provide it, then analysts’ influence can offset undesirable patterns in managers’

disclosure. If on the other hand, analysts’ questions respond to similar forces as do managers’

guidance, then analysts’ influence may add to or reinforce managers’ disclosure incentives.

Although this context is also important to understand, it suggests that analysts’ influence may not

help drive disclosure towards what is in investors’ best interest. For example, prior research finds

a positive relation between managerial guidance and size (Waymire, 1985; Lev and Penman, 1990;

Lang and Lundholm, 1993) and a negative relation between guidance and volatility (Cox, 1985;

Waymire, 1985) consistent with disclosure costs limiting managerial guidance. If analysts’

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questions are driven by similar forces, then analysts’ influence may only elicit guidance from

managers that are already predisposed to disclosure rather than fundamentally altering the cost-

benefit tradeoffs that managers face in their disclosure decisions.

To understand the context of analysts’ influence on managers’ guidance, we ask whether

analysts’ influence can mitigate other forces that determine managerial guidance. We seek to

answer this question in three ways. We first model the determinants of analysts’ questions.

Although this test is descriptive in nature, it allows us to test whether the forces that determine

managerial guidance have a similar relation with analysts’ questions. If analysts’ questions and

managers’ guidance have similar relations with their modeled determinants then we expect

analysts’ ability to mitigate managers’ disclosure incentives to be low. Second, we directly test

whether analysts’ questions mitigate the influence of other key determinants on managers’

guidance. For example, while size is positively correlated with managers’ guidance, analysts’

questions can mitigate the influence of size by eliciting guidance from small companies. Third,

because analysts’ influence is a product of their questioning behavior as well as managers’

willingness to adjust guidance after observing analysts’ questions, we test for the determinants of

which companies are more likely to respond to analysts’ questions by providing guidance in

subsequent periods.

At first glance it seems obvious that analysts’ influence will mitigate managers’ other guidance

incentives because they will ask questions when managers are otherwise unwilling to provide

guidance in order to acquire important information. Additionally, analysts must request guidance

that they view as helpful in some way, so analysts’ requests should be for guidance when that

guidance is most useful, e.g. for companies where information might otherwise be scarce.

However, analysts’ incentives and the relationship between analysts and managers could offset the

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inclination to request guidance when it is most needed or most useful. For instance, analysts’

incentives to maintain positive relationships with managers can lead them to ask for guidance only

when managers are willing to provide it. This explanation seems feasible given that analysts rely

on managers for information used in their earnings forecasts and recommendations and because

cooperation from managers is required for analysts to achieve the important job function of

providing their institutional clients with access to managers (Brown, Call, Clement, and Sharp,

2015). This can also occur because the number of questions analysts ask during a call is limited.

This is supported by anecdotal evidence that firms limit the number of questions that analysts are

permitted to ask on earnings conference calls and by our conversations with analysts and managers.

The preceding question and discussion leads to the second hypothesis.

H2: Analysts’ influence on disclosure mitigates managers’ other disclosure incentives.

We next discuss the data for our analysis and then turn to the empirical design.

2 Sample selection and description

2.1 Sample construction

The primary objective for this paper is to test analysts’ influence on managers’ disclosure

choices. In order to test for analysts’ influence on disclosure, we need observable measures of

analysts’ interaction with managers and of disclosures that managers voluntarily withhold or

provide. Ideally, in order to adequately test analysts’ influence on disclosure, we also need to use

disclosures that are important from analysts’ perspective. Because one of analysts’ primary

products is their forecast of earnings, an important demand for disclosure will be for forward

looking information that can aid in their forecasts. Therefore, in our analysis, we focus on analysts’

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questions about forward looking information. Using disclosures of forecasts also provides a

desirable research setting for testing the effect of analysts’ information requests on managers’

disclosure choices because these forecasts are forward looking, something not required by

regulation or accounting standards. The use of these forecasts helps distinguish between voluntary

disclosures and seemingly voluntary disclosures that are disclosed for other reasons (e.g., they are

related to material events that make some disclosure mandatory (Heitzman, Wasley, and

Zimmerman, 2010)). These forecasts are also common enough to make them available across a

broad range of firms. Additionally, by identifying specific pieces of information we are able to

match the requests for a particular piece of information with the subsequent disclosure of that same

information. We collect questions and guidance about CAPEX, cash flow, EBITDA, EPS,

operating margin, and taxes during quarterly earnings conference calls. We select these six types

of information following prior research (Chuk, Matsumoto, and Miller, 2013) and a 2014 survey

that suggest these categories are the most common types of guidance provided by managers (Niri,

2014).12

Each of the six types of requests and forecasts are collected from earnings conference calls.

The sample is primarily constructed from transcripts of quarterly earnings conference calls that

occurred between the years 2001 and 2014 which we collect from the CQ FD Disclosure news

service. For each conference call transcript in the sample, we identify the request for a CAPEX,

cash flow, EBITDA, EPS, operating margin, or tax forecast from questions asked by analysts

during the question and answer portion of the conference call. We then identify the disclosure of

this information from the prepared remarks section of the subsequent conference calls. For each

12 We appreciate the suggestion from an anonymous reviewer to look at and benchmark our data against that provided

by this survey.

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firm-quarter observation, we set our disclosure variable (forecast) equal to one if managers provide

forward-looking information for one of these items in the prepared remarks section of the earnings

conference calls. Similarly, we set our analysts’ request variable (analyst ask) equal to one if

analysts request forward-looking information for one of these items in the question and answer

section of the earnings conference calls.

Three steps are required to identify managers’ provision of and analysts’ requests for forecasts.

First, using textual analysis software, we parse each transcript into two sections - the prepared

remarks section and the question and answer section. We assume that every conference call begins

with management’s prepared remarks and we identify the question and answer section as the

portion of the call beginning with the first time an analyst speaks and ending when the call has

concluded.13 We identify the first instance of an analyst speaking as the first time a non-company

participant speaks on the conference call. We identify non-company participants based on whether

a title is included (titles such as CEO, CFO, IR, etc are included for company participants while

no titles are provided for non-company participants). Importantly, the prepared remarks section of

the call is always at the beginning and the question and answer section is always at the end of the

call. Thus, distinguishing between these two sections allows us to observe what information

managers disclose before analysts ask questions.

Second, we parse each section into individual sentences and categorize each sentence as a

forecast sentence if it contains at least one word from a list of commonly used words indicating

13 While there is no reason that the prepared remarks section must always precede the question and answer section,

this appears to be a universal convention in earnings conference calls. We have observed rare examples in which no

questions are asked following the prepared remarks, but to our knowledge prepared remarks always precede open

dialogue with investors and analysts.

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forward-looking language.15 Lastly, from among the sentences that contain forward-looking

words, we conduct another search for words indicating that the sentence contains a CAPEX, cash

flow, EBITDA, EPS, operating margin, or tax forecast.16 Sentences that contain both language

indicative of a forecast and one of the six types of information are categorized as a request

(AnalystAsk=1) when they were spoken by analysts in the question and answer section of the

conference call or as a management forecast (Forecast=1) when they are provided by managers

in the prepared remarks section of the call.17

We also collect management’s provision of forecasts in earnings announcements from the

SEC’s EDGAR website. We identify earnings announcement as exhibits 99 or 99.1 of 8-K filings

tagged as items 9.01 or 2.02 that are filed within plus or minus one day of the earnings

announcement date recorded in Compustat. We use the same methodology to identify forecasts

provided by managers in earnings announcements as was used (and described above) for

transcripts of quarterly conference calls.

We collect accounting, stock market, and analyst variables from the Compustat, CRSP, and

I/B/E/S databases and match these data to our sample of conference calls. Because conference

calls in our sample occur on average 34 calendar days after the close of the fiscal quarter

15 We use the Perl module Lingua::EN::Sentence to parse the transcripts and earnings announcements into individual

sentences. Our list of forward-looking words starts with the list used by Chuk, Matsumoto, and Miller (2013):

anticipate, anticipated, expect, expectation, expected, forecast, guidance, and outlook. To this list we add several

related words and short phrases that we noticed were commonly associated with forecasts in our reading of conference

call transcripts: anticipating, anticipation, forecasted, going to be, projected, projection, revised, should be, trajectory,

and will be. 16 “EBITDA;” “cash flow” or “cash from operations;” and “CAPEX,” “capital expenditures” or “capital expenses”

are used for EBITDA, cash flow, and CAPEX forecasts, respectively. “Operating income” and “operating expense”

are used for operating margin. “Earnings per share,” “EPS,” “net income,” and “net profit” are used to identify EPS

forecasts. “ETR,” “effective tax,” “tax rate,” or “effective rate” are used to identify tax forecasts. 17 Appendix 2 contains examples of analysts’ requests for and managers’ provision of forecasts and our identification

strategy for measuring these items.

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(untabulated), variables are matched to the most recently available firm-quarter (for variables

calculated quarterly) or firm-year (for variables calculated annually) to which the earnings

conference call applies.18 After excluding observations where data are unavailable from

Compustat, CRSP or I/B/E/S, our sample consists of 146,113 firm quarters.

2.2 Descriptive statistics

Table 1 provides sample descriptive statistics. As shown in Panel A, our sample primarily

consists of firms that are large along various dimensions. For example, the mean natural log of

market capitalization is 6.8 or approximately $900 million, the percentage of shares owned by

institutional investors is on average about 50%, and the mean of the log of 1 + the number of

analysts is 1.7 or approximately four analysts. The selection of large companies results from

requiring conference call data. These statistics suggest that our findings may not generalize to

smaller companies or to companies with less institutional ownership or less analyst coverage.

Panel B describes analysts’ requests and managers’ forecasts by type of forecast. As each of

these variables are indicator variables, the mean values presented in Panel B are the percentage of

observations with an indicator equal to one for each variable. The first four variables (Analyst ask,

Analyst ask Non-Fwd Looking, EA forecast and Forecast) indicate whether the relevant item is

provided in the quarter. The following four variables (Industry forecast, Prior Analyst ask, Prior

forecast and Prior forecast EA) indicate whether a forecast or ask occurred in any of the four

quarters from t-4 to t-1. Analysts ask for forward-looking information about these items (Analyst

ask) in approximately 7.5% of the quarterly observations; from 4.3% to 12.5% for the individual

18 This implies that variables derived from conference calls are not measured at precisely the same time as variables

collected from CRSP, Compustat, and I/B/E/S within each firm-quarter. For example, in tests where Forecast(t) is the

dependent variable and various measures of firm characteristics are included as independent variables (also denoted

with time subscript t), the dependent variable is observed on average 34 days after the quarterly control variables are

measured.

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forecast items. The differences between Analyst ask and Prior Analyst ask shows that when defined

to include four quarters a larger number of observations have some request for guidance.

Therefore, although 7.5% of observations have a request for guidance, approximately 20% of the

observations have had a request during at least one of the preceding four quarters. Analyst ask

(Non-Fwd Looking) is defined each quarter as an indicator variable if an analyst asks for non-

forward-looking information about a particular item. For example, if an analyst asks about CAPEX

for the current quarter (as opposed to forward-looking, i.e. future quarters), this variable is coded

as one. The difference in means between Analyst ask and Analyst ask (Non-Fwd Looking) shows

that although analysts do not ask for forward-looking information in a large percentage of the

observations, they do ask for non-forward-looking information a larger percentage of the time

(10.2% - 37.8%). Managers provide forecasts more frequently than analysts ask for them

(Forecast, 9.3% - 45.5%). When looking at the industry level, Industry forecast, 11.4% to 49.2%

of companies within the same industry provide a forecast of these items sometime during the prior

four quarters.

Table 2 describes the provision of forecasts along several dimensions. The number of

observations (N) in each panel in Table 2 reflects the number of observations in our sample by

industry (Panel A), Year (Panel B) and firm size (Panel C). Panel A shows that our sample consists

of firms across a broad range of industries. For most industries, the most frequent type of forecast

is for EPS ranging from 24% for telecom and energy to 72% for utilities. In untabulated analysis,

we find that the distribution of firms in our sample closely resembles the distribution of firms

followed by Compustat during our sample period. For example, for each of the Fama-French 12

industry categorizations, our sample is within +/- 1% of the same percentage of observations

available for all firm quarters available in the Compustat quarterly file between 2001 and 2013

17

with the exception of Business Equipment (21% in our sample, 16% in Compustat), Finance (16%

in our sample, 19% in Compustat), Other (13% in our sample, 18% in Compustat), and

Wholesale/Retail (9% in our sample, 7% in Compustat). Thus with respect to its distribution across

industries, our sample generally reflects firms covered by Compustat.

Panel B presents the frequency of forecasts and the number of observations in our sample by

year. There is an increase over time in CAPEX, EBITDA and tax forecasts. Panel C presents the

frequency of forecasts and the number of observations in our sample by firm size. Except for

CAPEX and EBITDA, forecasts are generally more frequent for larger firms. Panel D provides a

comparison between the frequency of forecasts from the NIRI 2014 survey and the frequency of

forecasts in our sample. To make the comparison meaningful, we compare the forecast data from

2013 in our sample to the NIRI survey because 2013 is most likely to be within the frame of

reference for firms answering the survey questions. Because the NIRI survey asked if companies

provide a forecast and forecasts can come outside of conference calls, earnings announcements

and 10Q filings, we expect that the forecasts measured in our sample may underrepresent the extent

of forecasts collected in the survey. Nevertheless, the frequency of our forecasts relates fairly well

to the NIRI survey. For example, EBITDA is the least frequent forecast in our data and in the NIRI

survey (26% versus 42%) and the most frequent types of forecasts in the survey (CAPEX, EPS,

and tax) are also three of the most frequent types of forecasts in our data (76, 65, and 79% for the

NIRI survey and 43, 66, and 44% for our data).

18

3 Research design and empirical results

3.1 Research design for hypotheses tests

Our first hypothesis states that analysts’ questions lead to managers’ guidance in future

periods. Table 3 provides some descriptive evidence on the relation between our analyst and

guidance measures. This table provides contingency tables with Analyst ask (t) and Forecast (t+1)

separately for observations with Forecast(t) equal to zero and Forecast(t) equal to one. As the

tables are similar for each topic, we focus on the CAPEX tables provided in Panel A. When

Forecast(t) is equal to zero, Analyst ask(t) is equal to one for 19.4 percent of the observations (6.2

plus 13.2). When Forecast(t) is equal to one, Analyst ask(t) is equal to one for 29.5 percent (14.7

plus 14.8) of the observations. This relation between the provision of a forecast and questions

about forecasts during the same conference call shows a positive correlation between Forecast(t)

and Analyst ask(t). When Forecast(t) is equal to one and Analyst ask(t) is equal to one, there is

little difference between the percent of observations where Forecast(t+1) is equal to zero and

where Forecast(t+1) is equal to one (14.7 versus 14.8 percent). However, when Forecast(t) is

equal to zero and Analyst ask(t) is equal to one there is a notable difference between the percent of

observations where Forecast(t+1) is equal to zero and where Forecast(t+1) is equal to one (6.2

versus 13.2 percent). This implies that when a manager has not provided a CAPEX forecast, but

an analyst asks for CAPEX guidance, managers are more likely than not to provide the requested

guidance in quarter t+1 (13.2% of the observations with Forecast(t+1) equal to one versus 6.2%

of the observations with Forecast(t+1) equal to zero. Therefore, although there is some positive

relation between analysts’ questions and managers’ guidance, Table 4 provides descriptive

evidence that analysts causally influence disclosure by requesting guidance when it is not already

19

given thereby prompting guidance in subsequent quarters. With a few exceptions, this effect in the

contingency tables is similar for the other types of forecasts as shown in Panels B-F.

In order to formally test the first hypotheses, we model managers’ guidance as a function of

prior questions and prior guidance and other firm characteristics drawn from prior research on

earnings guidance. The regression model estimates the following equation.

𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑖,𝑡+4 =

𝛽0 + 𝛽1𝐴𝑛𝑎𝑙𝑦𝑠𝑡 𝑎𝑠𝑘𝑖,𝑡 + 𝛽2𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑖,𝑡 + 𝛽3𝐿𝑛𝑀𝑎𝑟𝑘𝑒𝑡 𝐶𝑎𝑝𝑖,𝑡

𝛽4𝑆𝑡𝑑𝐷𝑒𝑣(𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠)𝑖,𝑡 + ∑ 𝛽𝑗

𝐽

𝑗=5

𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑗,𝑖,𝑡 + 𝜀𝑖,𝑡

(1)

Forecastt+4 is an indicator variable equal to one if managers provide a forecast in the prepared

remarks of the earnings conference call in quarter t+4 and i and t index company and quarter.

Analyst ask is an indicator variable equal to one if an analyst asks a question. For analyst questions,

t indicates questions asked during the question and answer portion of the earnings conference call

in quarter t. The equations are repeated for each type of question (CAPEX, cash flow, EBITDA,

EPS, operating margin, or tax). β0 – βj are coefficients to be estimated while ε is an error term.

Earnings are quarterly earnings before extraordinary items scaled by the market value of equity at

fiscal quarter end. Prior research documents a positive relation between firm profitability and

voluntary disclosures (Lang and Lundholm, 1993; Houston, Lev, and Tucker, 2010). Lang and

Lundholm (1993) and Frankel, Johnson, and Skinner (1999) find a positive relationship between

voluntary disclosures and firm size. Thus, we include the log of the market value of equity (Ln

market cap) as a measure of firm size. We control for earnings volatility (StdDevEarnings) because

20

prior literature finds a negative relationship between the provision of forecasts and earnings

volatility (Cox, 1985; Waymire, 1985). We also include other control variables. The construction

of these variables is defined in Appendix 1. We control for managers’ prior provision of forecasts

because the decision to provide a forecast in the future is likely to be influenced by prior disclosure

choices (Prior Forecast). In untabulated analysis, we find positive autocorrelation in managers’

forecasts. We control for analysts’ prior requests for information because prior requests reflect

previous demands for information on the same topic (Prior Analyst ask). We also control for the

amount of forward looking information provided by managers because this is likely to be

correlated with analysts’ decision to request additional information (PM forward looking). We

control for institutional ownership (Inst Ownership) because prior literature suggests information

demands of institutional investors influence managers’ disclosure choices (Bushee and Noe, 2000;

Healy, Hutton, and Palepu, 1999; Jung, 2013). Lang and Lundholm (1996) observe that firms with

more informative disclosure practices tend to have higher levels of analyst coverage. We control

for the effect of analyst coverage on voluntary disclosure choices by including the number of

analysts providing earnings estimates on the firm within the current quarter (Ln num analysts).

Prior research finds that managers are more likely to provide earnings forecasts in less litigious

environments (Baginski, Hassell, and Kimbrough, 2002). We control for litigation risk by

including a dummy variable if the firm has been named in a shareholder lawsuit at any time over

the prior two years (Litigation). Findings from O'Brien and Bhushan (1990) suggest that firm

disclosures are correlated with disclosures of firms within the same industry. We include a measure

of the overall rate with which firms in each industry provide forecasts as the percentage of firms

providing at least one forecast in the prior four quarters during the sample period (Industry

21

forecast) in order to control for the role of industry-level disclosure patterns in determining

managers’ disclosure choices (see also Jung, 2013).

Although Analyst ask is measured prior to Forecast and should therefore, in principal, not be

subject to concerns that the variables are simultaneously determined (i.e., that there is bias in the

estimated coefficient of interest), there may still be a concern that some other unmeasured event

leads to questions being asked by analysts and disclosed by managers even if it is not analysts’

questions that are causing managers’ forecast behavior. We attempt to alleviate this concern, using

various approaches, one of which is described here. Specifically, the inclusion of managers’ prior

provision of forecasts (Prior forecast) should control for previous unobservable factors that may

have caused managers to provide forecasts in the recent past and that may continue to influence

their decision to provide a forecast. Additionally, we control for analysts’ non-forward looking

questions during the same conference call (Analyst ask Non-Fwd Looking). This variable is set

equal to one if the analyst asks a question about one of the six types of information on which we

focus (CAPEX, cash flow, EBITDA, EPS, operating margin, and taxes) but does not use forward-

looking language, implying that the analyst is not requesting a forecast of that type of information

but rather is asking something about that type of information related to the current or previous

quarters. We expect that if there is some correlated variable that drives analysts’ questions and

managers’ forecasts, the effect of this correlated omitted variable would be strongest when

analysts’ interest in that topic is the strongest. Thus, including analysts’ non-forward looking

questions during the same conference call is likely to be a strong control variable that can directly

address this concern. Similarly, the prior provision of forecasts and the prior requests for forecasts

help to rule out that the results are spuriously driven by some omitted forecast or questioning

behavior. In unreported analysis we also include a number of other control variables, not described

22

here for the purpose of simplicity.19 We later return to other efforts to mitigate the concern about

correlated omitted variables.

The primary hypothesis of the paper states that analysts influence managers’ disclosure

decisions. This hypothesis predicts 𝛽1 > 0 in equation (1). The interpretation is that, controlling

for other determinants of managers’ provision of a forecast, analysts’ questions incrementally

make forecasts in the following year more likely. Note that because the control variables included

in the estimation of equation (1) include prior forecasts, the influence of analysts’ questions is

incremental to prior forecasting behavior.

We estimate equation (1) using a logistic regression with industry and year fixed effects. 20

Statistical significance of coefficient estimates use standard errors clustered by firm. We estimate

equation (1) for Forecast measured at quarter t + 4, four quarters after the quarter in which

analysts’ question, Analyst ask, is measured. In untabulated analysis we also repeat the analysis

for Forecasts measured from t + 1 to t + 8 and find similar results.21 By measuring Analyst ask

prior to Forecast we measure the effect of analysts’ questions on managers’ disclosures beyond

the period in which the question is asked. Despite the tests already described, the concern may

remain that the results are driven by some other unmeasured omitted variable. We perform

additional tests that help mitigate this concern.

19 These include an indicator variable equal to one if the firm reported a loss, an indicator for negative unexpected

earnings, lagged quarterly and annual stock returns and prior stock return volatility. 20 In unreported analysis, we also estimate the regression using a linear probability model with firm fixed effects and

find similar results. 21 While our results are similar in all periods through t+8, the magnitude of the effect tends to diminish over time,

consistent with analysts’ information requests having a persistent but not necessarily permanent effect on managers’

disclosure choices. We focus on the results using forecasts from quarter t+4 because it seems like a reasonable tradeoff

between guidance in the near versus long term and because the seasonality in requests for and provision of guidance

suggests some relation that should occur annually in the corresponding quarters.

23

First, we repeat the analysis replacing forecasts from earnings conference calls with forecasts

disclosed in earnings announcements. This test helps to speak to the extent of analysts’ influence.

If analysts’ influence is limited to the context within which their questions are asked then the scope

of their influence is restricted. However, if analysts influence managers in a way that informs

managers about important missing disclosures, their influence should extend beyond the

conference call context.

Second, we restrict the sample to those observations where Forecast(t) is equal to zero. This

test parallels descriptive analysis presented in Table 3. By restricting the sample to observations

where analysts’ questions are asked when there is no guidance provided by managers, we

strengthen the causal interpretation of the results.

Third, we estimate equation (1) with methodologies that help to mitigate concerns about the

endogenous relation between analysts’ questions and managers’ forecasts. We use entropy

balancing as a way to compare control observations with treatment observations where control

observations are made to be as similar to treatment observations as possible. In unreported

analysis, we also estimate equation (1) with a two-stage instrumental variables approach that uses

measures of analysts’ busyness in the first stage to create an instrumental variable for analysts’

questions that is used in the second stage.

As discussed previously, if analysts request guidance when managers are otherwise unwilling

to provide guidance, analysts may be able to mitigate some of the effects of other factors on

managers’ guidance decisions. To test for analysts’ mitigation effect, we perform a few related

tests. First, we estimate a model of the determinants of analysts’ questions using the following

model.

24

𝐴𝑛𝑎𝑙𝑦𝑠𝑡 𝑎𝑠𝑘𝑖,𝑡 =

𝛽0 + 𝛽1𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑖,𝑡 + 𝛽2𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑖,𝑡 + 𝛽3𝐿𝑛𝑀𝑎𝑟𝑘𝑒𝑡 𝐶𝑎𝑝𝑖,𝑡

𝛽4𝑆𝑡𝑑𝐷𝑒𝑣(𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠)𝑖,𝑡 + ∑ 𝛽𝑗

𝐽

𝑗=5

𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑗,𝑖,𝑡 + 𝜀𝑖,𝑡

(2)

This equation uses most of the same variables as those used in the estimation of equation (1).

Estimating the determinants of analysts’ questions in this way allows us to compare the similarities

and differences between the effects of the determinants on managers’ guidance versus analysts’

questions. Even though t indicates the quarter for Analyst ask and Forecast, during the earnings

conference call, Forecast precedes Analyst ask as the question and answer period follows the

prepared remarks. The equation is repeated for each type of question (CAPEX, cash flow,

EBITDA, EPS, operating margin, or tax). β0 – βj are coefficients to be estimated while ε is an error

term.

To more directly test analysts’ mitigation of other forces on managers’ guidance, we interact

Analyst ask in equation (1) with other key determinants of managers’ forecasts (Earnings, Ln

Market cap, StdDev(earnings)). If analysts questions help mitigate the influence of these variables

on managers’ forecasts we expect the interaction term to be the opposite sign of the main effect of

these variables. Thus, if earnings positively influence forecasts, we expect the interaction term

with Analyst ask to be negative to help reduce the influence of earnings on managers’ guidance.22

22 Prior research does provide some insight for when analysts’ requests for guidance and managers’ provision for

guidance might differ. In particular, prior research discusses how managers’ incentives for disclosure vary with

performance. However, this positive association between performance and disclosure need not apply to the disclosures

that analysts require. DeFond and Hung (2002) finds that analysts are more likely to provide cash flow forecasts when

firms are in poor financial health. If managers’ guidance is an important input into analysts’ forecasts (Brown, Call,

Clement, and Sharp, 2015), then we would expect analysts’ demand for cash flow related guidance from managers to

be negatively associated with earnings. This demand for cash flow information when earnings are low stands in

contrast to managers’ greater willingness to provide guidance when earnings are high.

25

However, if managers decide not to change disclosures even when analysts request guidance

then analysts may be unable to influence managers’ guidance when it matters most. To test for the

determinants of when analysts ask questions, but managers do not respond to analysts’ requests,

we model managers’ responsiveness (or lack thereof) to analysts’ questions. To measure

managers’ response to analysts’ requests, we limit the sample to observations in which analysts

make a request for guidance and then repeat the estimation of equation (1). Using this subsample,

we therefore test whether managers provide guidance in future quarters conditional on having

received a request in quarter t.

We next turn to describing the results of our empirical tests.

3.2 Results

Table 4 presents the results from estimating equation (1) on the full data panel. The estimated

coefficient on Analyst ask (t) is significantly positive for all types of forecast. The marginal effects

of Analyst ask range from 0.017 for tax forecasts and 0.053 for EPS forecasts. The mean of these

marginal effects yields the interpretation that when analysts ask for guidance in quarter t, managers

are 3.25% more likely to provide guidance in subsequent quarters after controlling for other

determinants of guidance. These results are consistent with the predictions of the first hypothesis.

Additional results in Table 4 also provide insights into the determinants of managerial guidance

and how findings in prior research generalize to the different types of guidance used in our study.

For four of the six types of forecasts the estimated coefficient on Earnings is positive. The positive

coefficients on earnings are consistent with the evidence in prior research that managers are more

willing to provide disclosures when performance is better. However, for cash flow related guidance

(Capex, CF, and EBITDA) earnings is only significantly positive for Capex. Additionally, larger

26

companies are generally more likely to provide guidance and companies with higher earnings

volatility are generally less likely to provide guidance. Key control variables (Prior forecast, Prior

analyst ask, PM forward looking, Industry forecast, and Analyst ask (Non-Fwd Looking) are

important in explaining managers’ guidance. Inst Ownership is significantly positive for five of

the six types of forecasts consistent with institutional investors’ influence on guidance. Litigation

is largely insignificant in explaining guidance.

Summarizing the results in Table 4, our findings are as predicted by the first hypothesis. We

find that analysts influence managers’ guidance in that requests in quarter t lead to guidance in

subsequent quarters.

To provide assurance that our tests are not simply picking up some peculiar aspect of

conference calls, we test whether analysts’ information requests influence overall firm disclosure

policy by testing whether a request for information on a conference call influences subsequent

disclosure choices across other, non-conference call, disclosure venues. By demonstrating a

spillover effect of analysts’ information requests in subsequent period disclosures in other

disclosure venues, we provide additional evidence that analysts’ information requests are resulting

in changes in managers’ voluntary disclosures.

Anecdotal evidence suggests that changes in one disclosure venue will not necessarily lead to

changes in another because content across disclosure venues is likely to be determined

independently (at least in part). Obvious differences in content across disclosure venues and the

consistency of content within venues supports this view; managers rarely discuss details from

footnotes on earnings conference calls, but footnotes are required and therefore always present in

mandatory filings. Nor is it likely that mandatory filings or earnings announcements constitute a

27

superset of disclosures from which a subset is selected for conference calls (which would imply a

mechanical relationship linking disclosure venues). This is evidenced by significantly higher levels

of forward-looking information in conference calls.23 However, if analysts’ requests are informing

managers about deficiencies in their disclosures, we should observe some evidence that questions

in conference calls also influence disclosures outside of conference calls.

We estimate the probability that a firm provides a forecast during an earnings announcement

(as opposed to a conference call) in quarter t following the same model as in equation (1). Table 5

reports the results from estimating equation (2) using earnings announcements. Analysts’ requests

in conference calls are statistically significant positive predictors of forecasts provided in quarter

t + 4 for earnings announcements (for five of six information types). We interpret these results as

providing evidence that analyst information demands have an effect on disclosure policy because

there is some evidence that managers change disclosures across multiple outlets in response to

information requests.

To provide a stronger test of this causal effect of analysts’ requests on subsequent disclosures,

we repeat our estimation of equation (1) on the subsample of observations where managers have

not provided guidance in the previous four quarters. The results provided in Table 6 show that with

this subsample the results continue to show the influence of analysts’ requests on managers’

guidance, which helps provide assurance that our results are not caused by the co-mingling of

forecasting and non-forecasting firms in our sample.

In addition to the control variables used to address the omitted variable concern and the

additional tests, we also use statistical techniques to mitigate the concern that these results are

23 In untabulated descriptive statistics, we find a lower incidence of forward-looking information in earnings

announcements and an even lower incidence of forward-looking information in 10Qs.

28

spuriously driven by correlated omitted variables that determine both analysts’ questions and

managers’ forecasts. Our first test is to use matching methods where company-quarters without

analysts’ requests are used as a control for company-quarters with a question by analysts. This

matching procedure matches on observable variables, but helps to mitigate questions of

endogeniety. In untabulated tests, we first try to create these matches directly using observed

variables with propensity score matching. The goal of this procedure is to match along the factors

that predict analysts’ requests for information, which we attempt using the control variables

included in equation (2). However, using propensity score matching, we fail to achieve a covariate

balance between the treatment and control observations. Because of this failure, we employ

entropy balancing, which is a methodology that changes the weighting of control observations

such that covariate balance is effectively achieved between treatment and control observations

(Hainmueller, 2012; Hartzmark, 2015; McMullin and Schonberger, 2015). The first step in the

entropy balancing approach is to re-weight control observations (which in our case consists of all

firm/quarters in which analysts did not request a forecast) such that the distribution of control

observations closely resembles that of treatment observations (which are observations in which

analysts did request a forecast). Panel A of Table 7 provides summary information on the effects

of this re-weighting of control observations. For all forecast types, the mean values of weighted

control observations are equal or approximately equal to the mean values of the un-weighted

treatment observations. In the second step of the entropy balancing approach, we simply estimate

equation (1) on this adjusted sample of unweighted treatment and weighted control observations.

Panel B of Table 8 displays the results of this analysis. Consistent with our first hypothesis,

Analyst ask remains significantly positive for all types of forecasts. We interpret these results as

additional evidence that information requested by analysts is disclosed more frequently by

29

managers in future periods. In showing that our main findings hold even after weighting control

observations such that their distributional properties closely resemble those of treatment

observations, this analysis also provides stronger evidence that more frequent future disclosures

are caused by analysts’ requests, rather than an omitted or unobserved correlated factor, which

helps mitigate concerns related to the potential endogenous relation between analysts’ requests for

information and managers’ future disclosures.

Panel B of Table 7 also provides a second analysis through which to interpret the economic

magnitude of our main result because these results are directly comparable to those reported in our

main logistic regressions in Table 4. As in Table 4, coefficient values for Analyst Ask are positive

and significant for all forecast types in Panel B of Table 7. We also note that the magnitude of the

coefficients are at least as large or larger than those presented in Table 4. While we are unable to

formally test whether coefficient estimates for Analyst Ask are statistically significant across these

two analyses, we interpret this result as additional evidence that the effect we document is

economically significant.

In untabulated analysis, we also use an instrumental variables approach to address the

endogeneity concern. This approach uses a first stage model, including measures of analysts’

operating environment as instrumental variables. The partial R-squared value of our instruments

suggests that, although individually statistically significant, our instruments have relatively little

incremental explanatory power in the first stage model. While this problem is not uncommon with

instrumental variable analysis, we acknowledge that it suggests we have weak instruments. The

Wald test successfully rejects the null hypothesis that Analyst ask is exogenous, implying some

degree of endogeneity in our variable of interest. However, as pointed out by Larker and Rusticus

(2010), the Wald result is potentially unreliable in the presence of weak instruments. With these

30

caveats, we find that using an instrumental variable approach yields conclusions similar to those

presented in the tables and text.

Even with the apparent robustness of our results, we note the importance of using caution in

drawing conclusions from them. Despite our control variables, additional tests, fixed effects, and

statistical robustness tests, we may still have been unable to completely remove the effects of some

correlated omitted variables. This merits some caution when interpreting the results.

We next turn to testing the second hypothesis that analysts mitigate the influence of other

factors on managers’ guidance. Table 8 presents results from estimating equation (2) that models

the determinants of analysts’ questions using the same set of determinants used to model

managers’ guidance. This table shows that the model does a reasonably good job of explaining

analysts’ questions with pseudo 𝑅2 values ranging from 10.5% to 27.4%.24 The coefficient

estimate on Forecast is significantly positive for all of the six question types with marginal effects

from 0.4 to 5%. This means that if managers provide guidance during the prepared remarks of an

earnings conference call analysts are 0.4 to 5% more likely to ask about the forecast in the question

and answer portion of the conference call. The marginal effects of Earnings on analysts’ questions

about cash flow related items (CAPEX, CF, and EBITDA) are negative while the marginal effects

for earnings related items (EPS, OperMar, and Tax) are positive. These negative relations between

earnings and cash flow items are consistent with analysts’ demand for cash flow guidance when

performance is poor. In an untabulated test, we use a multinomial logistic regression to test whether

analysts’ requests for cash flow guidance are significantly different from their requests for earnings

24 The number of observations varies slightly across regressions because, when estimating industry fixed effects, a

few industries have no variation across firms in a particular type of forecast. Results are similar when not using

industry fixed effects.

31

related items (specifically EPS). We find that lower earnings make requests for cash flow related

items significantly more likely relative to requests for EPS.

Other results provide additional insight into the determinants of analysts’ requests for

guidance. For five of the six types of requests, the coefficient on Ln Market Cap is significantly

positive. Analysts are more likely to ask for forecasts from large companies. The estimated

coefficient on StdDev(earnings) is significantly negative for four of the six types of requests. We

interpret this finding as evidence that analysts view forecasts to be less useful when earnings are

volatile. A number of the other variables that control for the autocorrelation in analysts’ questions

and in their relation to prior forecasts are significant (Prior forecast, Prior analyst ask, PM forward

looking, Industry forecast, and Analyst ask (Non-Fwd Looking)). The other variables (Inst

Ownership and Litigation) are insignificant or inconsistent across the different types of analysts’

questions.

Comparing the determinants of guidance presented in Table 4 with the determinants of

analysts’ questions in Table 8 shows some similarities and differences. We find that analysts’

requests for cash flow related guidance is stronger for low levels of earnings while managers are

more likely to provide guidance for high levels of earnings. However, we also find that the signs

of several variables are statistically significant in the same direction, implying that the factors that

influence managers’ provision of forecasts also influences analysts’ requests for information in the

same way. For example, firm size (Ln Market Cap) is positively associated with the provision of

forecasts and with analysts’ requests for guidance (for five of six types of information). We find

similar patterns for StDev(earnings) (three of six types of information load in the same direction).

32

Our finding that some factors explaining the provision of forecasts and analysts’ request for

information load in the same direction is potentially surprising because there is a strong null

hypothesis that analysts are more likely to ask questions of managers when they perceive managers

as less likely to otherwise provide the information. However, this does not appear to be the case

except for the different relations between earnings and analysts’ requests for cash flow guidance

and earnings and managers’ cash flow related guidance.

Table 9 presents the results from directly testing whether analysts’ questions help mitigate the

influence of other factors on managers’ guidance, which we conduct by modifying equation (1) by

adding interactions between analyst ask and firm size (Ln market cap), performance (earnings)

and performance volatility (StdDevEarnings). Results in Table 9 provide some indication that

analysts can mitigate the influence of other factors on managers’ guidance; however the mitigation

is not particularly strong. We find a negative association between the interaction of firm

performance and a request for information (Earnings x analyst ask) for two of six forecast types,

which suggests that at least for EPS and tax forecasts, the tendency for high-earnings firms to

provide future forecasts more frequently is partially mitigated when analysts request future

earnings-related information. Similarly, we find a negative association between the interaction of

firm size and a request for information (Ln Market cap x analyst ask) for two of six forecast types,

which suggests that at least for Capex and tax forecasts, the tendency for large firms to provide

future forecasts more frequently is partially mitigated when analysts request future earnings-

related information. Finally, we find a positive association between the interaction of

StdDev(earnings) and Analyst ask for one of six forecast types, which is evidence of a mitigating

33

effect of analysts’ forecasts for Capex information. However, overall we see relatively little

evidence of the mitigating effect of analysts’ questions.25, 26

Finally, even if analysts were to request guidance in ways that could mitigate managers’ other

disclosure incentives (something that seems doubtful or weak from the results presented in Tables

8 and 9), analysts may fail to mitigate managers’ disclosure incentives if managers’ willingness to

respond to analysts’ requests by adjusting future disclosures is determined by the same forces that

drive guidance even in the absence of analysts’ requests. Table 10 presents results from estimating

equation (1) while restricting the sample to observations with a request for guidance in quarter (t).

This regression is therefore an analysis of how likely managers are to provide guidance in quarter

(t+4) when analysts request guidance in quarter (t). The results in Table 10 provide some evidence

that at least some of the same forces that determine managers’ guidance (and analysts’ requests)

also influence whether managers decide to alter disclosure in response to analysts’ requests. The

estimated coefficients on Ln Market Cap are significantly positive for four of the six guidance

types. However, the relations are not as convincing with one or two of the coefficients on Earnings

and StdDev(earnings) being significant.

25 An additional caution is important when interpreting the statistical significance of the results presented in Table 9.

Because we are testing for the significance of the coefficients on 18 interaction terms (6 guidance types x 3 variables

– Earnings, Ln Market Cap, and StdDev(earnings)) and particularly because we do not have predictions for which

coefficients should be significant and which not, there is some concern that at least a small number of the coefficients

are significant by chance alone. This is one of the concerns raised by statisticians when considering situations with

multiple testing. An adjustment following Bonferroni (1936) suggests that the cutoffs used with p-values should be

adjusted. If the desired significance is at the 5% level, the observed statistics should be adjusted (5% / 18) so that the

desired cutoff would be for standard p-values of 0.28%. 26 Ai and Norton (2003) show that the magnitude of the interaction effect in logistic regressions does not equal the

magnitude of the interaction term. Our primary method for analyzing the economic significance of the mitigation

effect of analysts’ requests is by observing the frequency of a significant interaction across our six types of forecasts,

rather than by interpreting the magnitude of the coefficient estimates. However, to address the issue raised by Ai and

Norton (2003), we also use the Stata function “inteff” to confirm that the statistical significance of interaction effects

presented in Table 9 holds across the range of the dependent variable, which we generally find to be the case.

Specifically, earnings X Analyst Ask is significantly negative from 0.5 through 1 for EPS and from 0.1 to 1 for Capex;

Ln Market cap X AnalystAsk is significantly negative for the entire range for Capex and from 0.4 to 1 for Tax;

StdDev(Earnings) X AnalystAsk is significantly positive for the entire range for Capex.

34

Summarizing the results from Tables 8-10 yield the following conclusions. Although analysts

influence the guidance that managers provide, the importance of this influence may be mitigated

by two forces. First, analysts tend to request guidance from managers that are otherwise most

willing to provide guidance. However, there is some limited evidence that analysts are able to

mitigate some of these guidance incentives. Second, managers’ willingness to respond to analysts’

requests is also determined by similar forces to their willingness to provide guidance without the

consideration of analysts’ requests. However, this is not universally the case.

4 Summary and conclusion

In this paper, we explore whether corporate disclosure choices are influenced by analysts’

information demands. We find that analysts’ requests for forward-looking information during

conference calls about CAPEX, cash flow, EBITDA, EPS, operating margin, and taxes make the

subsequent voluntary disclosure of these items more likely. We also find increased future

disclosures in earnings announcements of information requested by analysts on earnings

conference calls. We document previously unexplored determinants of analysts’ questions.

Specifically, we find that analysts request more earnings-related information when earnings is high

and more cash-flow related information when earnings is low. We also show that smaller

companies, companies with higher earnings volatility, and companies with lower earnings are not

only less likely to provide guidance, but also less likely to respond to analysts’ requests by

providing guidance in future quarters. Therefore, the evidence that analysts are able to mitigate

other managerial disclosure incentives is limited. The results are robust to various methodological

choices. Overall, our findings provide evidence that analysts influence managers’ guidance

choices.

35

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38

Appendix 1: Variable definitions

Variable Name Definition

Analyst ask

A binary variable set equal to one if at least one analyst requested a forecast of one of the six

categories of information used in the paper (Capex, Cash flow, EBITDA, EPS, Operating

Margin, or Tax) during the question and answer portion of the earnings conference call.

Analyst ask (Non-Fwd Looking)

A binary variable set equal to one if at least one analyst asked a question not containing a

forward-looking word about one of the six categories of information used in the paper

(Capex, Cash flow, EBITDA, EPS, Operating Margin, or Tax) during the question and

answer portion of the earnings conference call.

EA Forecast A binary variable equal to one if the firm provided a forecast of the item in the earnings

announcement.

Earnings Earnings before extraordinary items scaled by the market value of equity at the end of the

fiscal quarter.

Forecast A binary variable equal to one if the firm provided a forecast of the item in the prepared

remarks of the quarterly earnings conference call.

Industry forecast The percentage of firms providing at least one forecast during the prepared remarks of

conference calls in the prior four quarters for each four-digit SIC code.

Inst Ownership The percentage of traded shares owned by institutional investors as of the quarterly reporting

period occurring most recently before the end of the fiscal quarter.

Litigation A binary variable set equal to one if the firm was involved in litigation as tracked by the

Securities Class Action Clearinghouse over the two most recently completed fiscal years.

Ln Market cap The natural log of the market value of equity at the end of the fiscal quarter.

Ln num analysts The log of one plus the number of analysts providing earnings forecasts for the firm during

the prior quarter.

PM forward looking The number of sentences containing forward-looking words in the prepared remarks section

of the earnings conference call.

PM length The total number of sentences in the prepared remarks section of the earnings conference

call.

Prior Analyst ask A binary variable equal to 1 if Analyst ask equals one in any of the quarters t-4, t-3, t-2, and

t-1.

Prior Forecast A binary variable equal to 1 if Forecast equals one in any of the quarters t-4, t-3, t-2, and t-

1.

Prior Forecast (EA) A binary variable equal to 1 if EA Forecast equals one in any of the quarters t-4, t-3, t-2, and

t-1.

StdDev(earnings) The standard deviation of the firm’s quarterly earnings scaled by total assets over the prior

eight quarters.

39

Appendix 2: Examples of requests for and provision of forecasts

Forward-looking statements are presented in italic text and guidance items are presented in bold

text.

Panel A: Analysts’ Requests for Forecasts

Type Firm Year Qtr Speaker Text

CAPEX UNITED STATES

STEEL CORP 2005 3 Leo Larkin Could you give us CAPEX guidance for '06 and also

DD&A for '06, if that's available? CF COVANCE INC 2011 1 John

Kreger Lastly, Bill, can you just give us an update on your

expectations for cash flow for the rest of the year? EBITDA GEEKNET INC 2008 4 William

Morrison So can you give us some guidance on what to expect

there and maybe some kind of -- possibly some kind of

a range of an incremental margin on EBITDA? EPS KNIGHT

TRANSPORTATION

INC

2004 2 Daniel

Moore EPS guidance, you don't normally give guidance

necessarily, but you do and have in the past commented

as to whether or not you are comfortable with those

expectations. OM DSW INC-OLD 2008 4 John

Zolidis Looking at the items like depreciation which you expect

to be up about $13 million and advertising spend which

you expect to be up about $15 million, just doing a

quick calculation, that together with negative mid single

digit comps, ballpark operating margins we should

expect those to be down at least 200 basis points in

2009 so was wondering if you could just kind of

confirm that's in the ballpark. TAX PRGX GLOBAL INC 2003 3 Daniel

O'Sullivan What kind of effective tax rate can we expect going

forward?

Panel B: Example of managers’ provision of forecasts

Type Firm Year Qtr Speaker Text

CAPEX TRINITY

INDUSTRIES 2009 3 Bill

McWhirter For 2009, we expect non-leasing capital expenditures

of between $50 million and $55 million. CF TIBCO SOFTWARE

INC 2010 1 Sydney

Carey We expect cash flow from operations to range from

$20 million to $25 million. EBITDA GREENFIELD

ONLINE INC 2004 3 Bob Bies We expect adjusted EBITDA for the fourth quarter of

2004 to be in the range of 3.2 to 3.4 million, compared

to 1.4 million in the fourth quarter 2003. EPS BEA SYSTEMS INC 2003 1 Bill M.

Klein At this revenue range, we expect to report similar

operating profits and earnings per share as reported

in Q1. OM BLACKBAUD INC 2011 2 Tim

Williams Non-GAAP operating income is expected to be $76.5

million to $79 million, leading to non-GAAP earnings

per share of $1.06 to $1.09 based on a share count of

approximately 44 million shares. TAX NATIONAL MED

HEALTH CARD SYS 2006 4 Stuart

Diamond We anticipate an effective tax rate of 42.3%.

40

Table 1: Descriptive statistics

Panel A: Summary Statistics – Control Variables

Variable Mean 25% Median 75% n

Earnings (0.008) (0.003) 0.010 0.018 145,946

Inst ownership 0.505 0.069 0.578 0.837 146,113

Litigation 0.048 - - - 146,113

Ln market cap 6.836 5.586 6.769 8.006 146,113

Ln num analysts 1.694 1.000 1.609 2.197 146,113

PM forward looking 13.430 6.000 11.000 19.000 145,978

StdDev(earnings) 0.026 0.005 0.010 0.026 146,017

This table provides summary statistics for key variables used in the analysis. Variable definitions are provided in

Appendix 1. Ln indicates the natural log function.

Panel B: Summary Statistics – By Forecast Type

Mean Values: Capex CF Ebitda EPS

Oper

Mar Tax

Analyst ask 8.3% 7.4% 4.3% 12.5% 5.4% 6.8%

Analyst ask (Non-Fwd Looking) 19.1% 18.4% 8.2% 37.8% 13.1% 10.2%

EA forecast 3.7% 4.1% 2.8% 18.5% 3.5% 4.5%

Forecast 19.2% 19.5% 9.3% 45.5% 13.8% 19.8%

Industry forecast 24.9% 26.7% 11.4% 49.2% 19.8% 25.2%

Prior Analyst ask 21.7% 20.2% 10.3% 31.6% 15.5% 19.5%

Prior forecast 31.9% 33.6% 14.5% 61.4% 25.4% 32.2%

Prior forecast (EA) 10.3% 12.0% 7.1% 41.2% 10.6% 12.4%

This table provides summary statistics for key variables used in the analysis. Variable definitions are provided in Appendix 1. Variables preceded by Prior are

measured over the four quarters from t-4 to t-1. Thus, Prior analyst ask is an indicator variable equal to 1 if at least one analyst asked about the item during any of

the quarters t-4, t-3, t-2, and t-1.

Table 2: Provision of forecasts

Panel A: Frequency of forecasts and the number of observations by industry

Industry Capex CF Ebitda EPS

Oper

Mar Tax N

Business Equipment 15% 17% 8% 49% 20% 26% 31,671

Chemicals 24% 21% 12% 52% 15% 28% 3,571

Consumer Durables 23% 25% 11% 49% 17% 23% 3,321

Consumer Non-Durables 27% 20% 8% 57% 20% 27% 6,366

Energy 33% 24% 11% 24% 8% 17% 7,081

Finance 4% 20% 5% 43% 6% 10% 23,087

Healthcare 9% 13% 5% 37% 10% 16% 15,995

Manufacturing 28% 22% 8% 50% 18% 28% 12,977

Other 22% 20% 15% 42% 13% 19% 18,923

Telecom 39% 31% 28% 24% 10% 7% 5,471

Utilities 28% 23% 9% 72% 4% 8% 4,189

Wholesale/retail 31% 19% 8% 57% 16% 24% 13,461

Total 146,113

Panels A-C provide the percentage of observations with forecasts of each type and the total number of observations

by Fama / French 12-industry, year, and firm size (Capex= capital expenditures; CF= cash flow; Ebitda = earnings

before interest, taxes, depreciation, and amortization; EPS = net income or earnings per share; OperMar = operating

margin or operating profit; Tax= tax rate). Forecasts are those provided by management during the prepared remarks

section of the quarterly earnings conference call.

43

Panel B: Frequency of forecasts and the number of observations by year

Year Capex CF Ebitda EPS

Oper

Mar Tax N

2001 11% 16% 8% 54% 16% 14% 209

2002 14% 20% 8% 51% 12% 13% 6,816

2003 14% 19% 6% 49% 11% 14% 10,703

2004 15% 17% 6% 50% 14% 17% 12,368

2005 17% 18% 7% 51% 14% 19% 12,896

2006 19% 17% 7% 50% 15% 21% 13,146

2007 19% 18% 8% 47% 14% 22% 13,527

2008 21% 21% 9% 44% 13% 20% 13,773

2009 21% 21% 9% 40% 13% 19% 12,323

2010 20% 20% 10% 42% 14% 21% 12,045

2011 21% 20% 12% 42% 15% 21% 13,400

2012 23% 21% 13% 43% 15% 24% 12,634

2013 23% 22% 15% 41% 15% 24% 11,751

2014 22% 19% 12% 48% 20% 29% 522

Total 146,113

Panel C: Frequency of forecasts and the number of observations by firm size

Capex CF Ebitda EPS

Oper

Mar Tax N

Mega-cap (over $25 B) 20% 23% 5% 58% 21% 29% 6,697

Large-cap ($5 B - $25 B) 25% 28% 8% 59% 20% 28% 19,125

Mid-cap ($1 B - $4.9 B) 24% 24% 10% 55% 17% 25% 42,699

Small-cap ($150 M - $999 M) 17% 16% 10% 42% 12% 18% 54,707

Micro-cap (under $150 M) 10% 11% 8% 22% 6% 6% 22,885

Total

146,113

44

Panel D: Comparison to NIRI Survey Results

NIRI Survey

Our Results

(Year 2013)

CAPEX 76% 43%

CF 50% 44%

EBITDA 42% 26%

EPS 65% 66%

OM 57% 32%

TAX 79% 44%

This table compares the number of firms providing at least one forecast by each of the six types used in this study with

the number of firms that self-report providing these types of forecasts on a 2014 survey conducted by the National

Investor Relations Institute (NIRI, 2014). The percentages reported under “Our Results” are based on the sub-sample

of firms for which data is available for all four quarters in the year 2013 which is the most comparable sub-sample in

our data (because it uses the nearest year with a large sample size to the year in which the responses of the NIRI survey

were likely provided).

Table 3: Frequency of requests and provision of forecasts split on

whether there is a forecast in period t (percentages)

Panel A: Capex

Forecast(t)=0 Analyst

Ask(t)=0

Analyst

Ask(t)=1 Forecast(t)=1

Analyst

Ask(t)=0

Analyst

Ask(t)=1

Forecast(t+1)=0 93.8 6.2 Forecast(t+1)=0 85.3 14.7

Forecast(t+1)=1 86.8 13.2 Forecast(t+1)=1 85.2 14.8

Panel B: Cash Flow

Forecast(t)=0 Analyst

Ask(t)=0

Analyst

Ask(t)=1 Forecast(t)=1

Analyst

Ask(t)=0

Analyst

Ask(t)=1

Forecast(t+1)=0 94.8 5.2 Forecast(t+1)=0 87.3 12.7

Forecast(t+1)=1 89.3 10.7 Forecast(t+1)=1 84.1 15.9

Panel C: EBITDA

Forecast(t)=0 Analyst

Ask(t)=0

Analyst

Ask(t)=1 Forecast(t)=1

Analyst

Ask(t)=0

Analyst

Ask(t)=1

Forecast(t+1)=0 97.9 2.1 Forecast(t+1)=0 81.0 19.0

Forecast(t+1)=1 87.8 12.2 Forecast(t+1)=1 74.7 25.3

Panel D: EPS

Forecast(t)=0 Analyst

Ask(t)=0

Analyst

Ask(t)=1 Forecast(t)=1

Analyst

Ask(t)=0

Analyst

Ask(t)=1

Forecast(t+1)=0 94.1 5.9 Forecast(t+1)=0 84.5 15.5

Forecast(t+1)=1 87.1 12.9 Forecast(t+1)=1 79.7 20.3

Panel E: Operating Margin

Forecast(t)=0 Analyst

Ask(t)=0

Analyst

Ask(t)=1 Forecast(t)=1

Analyst

Ask(t)=0

Analyst

Ask(t)=1

Forecast(t+1)=0 96.2 3.8 Forecast(t+1)=0 88.6 11.4

Forecast(t+1)=1 90.9 9.1 Forecast(t+1)=1 84.1 15.9

Panel F: Tax

Forecast(t)=0 Analyst

Ask(t)=0

Analyst

Ask(t)=1 Forecast(t)=1

Analyst

Ask(t)=0

Analyst

Ask(t)=1

Forecast(t+1)=0 94.3 5.7 Forecast(t+1)=0 90.2 9.8

Forecast(t+1)=1 88.9 11.1 Forecast(t+1)=1 90.5 9.5

Table 4: Management forecasts

Dependent Variable: Forecast (t+4)

Capex CF Ebitda EPS OperMar Tax

Analyst ask 0.025*** 0.040*** 0.032*** 0.053*** 0.028*** 0.017***

(8.44) (12.89) (15.23) (15.62) (10.00) (5.26)

Earnings 0.090*** 0.006 0.003 0.325*** 0.053*** 0.313***

(5.45) (0.41) (0.32) (12.67) (3.18) (9.71)

Ln Market cap 0.007*** 0.016*** -0.002*** 0.026*** 0.014*** 0.013***

(5.51) (12.71) (-2.92) (15.18) (11.60) (9.58)

StdDev(earnings) -0.098** -0.039 0.063*** -0.400*** 0.038 -0.257***

(-2.40) (-0.91) (2.65) (-7.21) (1.15) (-5.09)

Prior Forecast 0.171*** 0.136*** 0.103*** 0.215*** 0.119*** 0.169***

(42.52) (37.25) (33.48) (47.75) (35.57) (46.50)

Prior Analyst ask 0.023*** 0.034*** 0.026*** 0.049*** 0.033*** 0.008***

(7.80) (11.77) (11.99) (14.08) (12.98) (2.80)

PM forward looking 0.002*** 0.003*** 0.001*** 0.006*** 0.002*** 0.003***

(16.70) (24.38) (15.41) (29.21) (18.92) (18.35)

Inst Ownership 0.020*** 0.028*** -0.004 0.076*** 0.034*** 0.061***

(3.31) (4.99) (-0.94) (10.42) (6.76) (10.49)

Ln num analysts 0.003 -0.007** -0.003 0.019*** -0.000 0.020***

(1.00) (-2.37) (-1.47) (4.84) (-0.04) (6.31)

Litigation -0.007 0.001 -0.004 -0.023*** -0.002 -0.013**

(-1.10) (0.14) (-0.79) (-2.74) (-0.30) (-2.13)

Industry forecast 0.040*** 0.042*** 0.023*** 0.027** 0.025** 0.017

(3.78) (4.14) (2.79) (2.30) (2.33) (1.62)

Analyst ask (Non-Fwd Looking) 0.019*** 0.023*** 0.029*** 0.014*** 0.024*** 0.019***

(7.84) (9.83) (15.06) (5.04) (10.49) (6.59)

Observations 143,960 145,157 144,903 145,202 145,202 144,981

Pseudo R-squared 21.7% 19.7% 29.1% 25.2% 20.1% 23.9%

This table presents the results of a logit regressions that model the determinants of managers provision of six forecast

types (Capex = capital expenditures; CF = cash flow; Ebitda = earnings before interest, taxes, depreciation, and

amortization; EPS = net income or earnings per share; OperMar = operating margin or operating profit; Tax = tax

rate). Variable definitions are provided in Appendix 1. Continuous variables are winsorized at the 1% and 99% levels.

Ln indicates the natural log function. Variables preceded by Prior are measured over the four quarters from t-4 to t.

Thus, Prior analyst ask is an indicator variable equal to 1 if at least one analyst asked about the item during any of the

quarters t-4, t-3, t-2, and t-1. Fixed effects are taken by year and industry. Standard errors are clustered by firm. *, **,

and *** represent significance at 10%, 5%, and 1%, respectively (two-tailed). Marginal effects are reported in place

of coefficient values. Z-values are presented beneath the marginal effects in parentheses.

47

Table 5: Forecasts in subsequent earnings announcements

Dependent Variable: EA forecast (t+4)

Capex CF Ebitda EPS OperMar Tax

Analyst ask 0.005** 0.011*** 0.008*** 0.022*** 0.008*** 0.003

(2.50) (5.29) (4.51) (6.92) (3.50) (1.39)

Earnings 0.027** 0.029** 0.013 0.331*** 0.025* 0.106***

(2.27) (2.41) (1.63) (10.60) (1.84) (3.84)

Ln Market cap 0.005*** 0.006*** -0.001* 0.021*** 0.005*** 0.006***

(4.03) (4.86) (-1.67) (10.59) (4.91) (5.56)

StdDev(earnings) -0.028 -0.044 0.043** -0.259*** 0.019 -0.007

(-0.97) (-1.48) (2.43) (-3.69) (0.71) (-0.19)

Prior Forecast 0.020*** 0.027*** 0.032*** 0.081*** 0.027*** 0.015***

(7.58) (9.52) (11.71) (14.47) (11.31) (5.10)

Prior Analyst ask 0.007*** 0.011*** 0.011*** 0.023*** 0.008*** 0.001

(3.31) (5.98) (5.97) (6.81) (3.90) (0.58)

PM forward looking 0.000*** 0.001*** 0.000*** 0.003*** 0.000*** 0.001***

(4.24) (6.73) (5.83) (13.80) (4.87) (5.23)

Inst Ownership 0.000 0.003 -0.002 0.033*** 0.011*** 0.016***

(0.08) (0.71) (-0.62) (4.17) (2.79) (3.17)

Ln num analysts -0.008*** -0.009*** -0.004* -0.021*** -0.005** -0.000

(-3.15) (-3.00) (-1.86) (-4.86) (-2.21) (-0.16)

Litigation -0.001 -0.001 -0.004 -0.021** -0.010** -0.007

(-0.28) (-0.13) (-1.09) (-2.30) (-2.10) (-1.31)

Industry forecast 0.014* 0.028*** 0.001 0.009 0.002 0.009

(1.93) (4.12) (0.17) (0.70) (0.27) (1.07)

Analyst ask (Non-Fwd Looking) 0.009*** 0.008*** 0.016*** 0.018*** 0.010*** 0.010***

(5.23) (5.25) (9.89) (6.42) (5.71) (4.84)

Prior Forecast (EA) 0.087*** 0.075*** 0.047*** 0.223*** 0.069*** 0.103***

(27.18) (23.65) (18.20) (50.50) (22.00) (27.65)

Observations 93,043 96,772 95,794 97,576 97,425 96,966

Pseudo R-squared 26.3% 25.5% 33.4% 28.0% 21.7% 23.7%

This table models the determinants of a forecast in the quarterly earnings announcement in period (t+4). Variable

definitions are provided in Appendix 1. Continuous variables are winsorized at the 1% and 99% levels. Ln indicates

the natural log function. Variables preceded by Prior are measured over the four quarters from t-4 to t. Thus, Prior

analyst ask is an indicator variable equal to 1 if at least one analyst asked about the item during any of the quarters t-

4, t-3, t-2, and t-1. Fixed effects are taken by year and industry. Standard errors are clustered by firm. *, **, and ***

represent significance at 10%, 5%, and 1%, respectively (two-tailed). Marginal effects are reported in place of

coefficient values. Z-values are presented beneath the marginal effects in parentheses.

48

Table 6: Subsequent forecasts when no forecast before analysts’ request

Dependent Variable: Forecast (t+4)

Capex CF Ebitda EPS OperMar Tax

Analyst ask 0.014*** 0.022*** 0.018*** 0.026*** 0.011*** 0.016***

(5.83) (6.99) (9.63) (4.77) (3.71) (6.18)

Earnings 0.033*** 0.006 -0.014*** 0.149*** 0.032*** 0.217***

(2.61) (0.48) (-2.84) (6.91) (2.92) (10.60)

Ln Market cap 0.006*** 0.007*** -0.001*** 0.009*** 0.005*** 0.007***

(10.90) (10.70) (-3.61) (8.31) (9.68) (12.63)

StdDev(earnings) -0.011 0.011 0.021** -0.096*** 0.039** -0.115***

(-0.57) (0.54) (2.17) (-3.01) (2.35) (-5.12)

Prior Forecast

Prior Analyst ask 0.013*** 0.019*** 0.013*** 0.020*** 0.018*** 0.010***

(7.12) (8.40) (8.76) (5.21) (9.75) (5.31)

PM forward looking 0.001*** 0.001*** 0.000*** 0.003*** 0.001*** 0.001***

(16.99) (17.16) (8.87) (16.60) (15.39) (15.43)

Inst Ownership 0.014*** 0.013*** -0.006*** 0.031*** 0.017*** 0.033***

(5.64) (4.84) (-4.05) (6.75) (7.69) (13.39)

Ln num analysts -0.003* -0.002 -0.002*** 0.004* 0.003*** 0.009***

(-1.86) (-1.51) (-2.64) (1.69) (2.62) (6.14)

Litigation 0.003 0.006 -0.001 0.006 -0.000 0.003

(0.82) (1.48) (-0.40) (0.88) (-0.11) (1.05)

Industry forecast 0.024*** 0.025*** 0.033*** 0.005 0.015** 0.023***

(4.28) (3.81) (8.03) (0.48) (2.42) (3.68)

Analyst ask (Non-Fwd Looking) 0.017*** 0.014*** 0.017*** 0.017*** 0.017*** 0.018***

(9.62) (6.48) (12.56) (5.71) (8.94) (8.25)

Observations 91,783 89,274 120,693 47,566 102,814 92,715

Pseudo R-squared 11.4% 6.5% 8.2% 9.5% 7.6% 11.4%

This table models the determinants of a forecast in period (t+4) for the set of observations where there was no prior

forecast (Prior forecast = 0). Variable definitions are provided in Appendix 1. Continuous variables are winsorized at

the 1% and 99% levels. Ln indicates the natural log function. Variables preceded by Prior are measured over the four

quarters from t-4 to t. Thus, Prior analyst ask is an indicator variable equal to 1 if at least one analyst asked about the

item during any of the quarters t-4, t-3, t-2, and t-1. Fixed effects are taken by year and industry. Standard errors are

clustered by firm. *, **, and *** represent significance at 10%, 5%, and 1%, respectively (two-tailed). Marginal effects

are reported in place of coefficient values. Z-values are presented beneath the marginal effects in parentheses.

Table 7: Forecasts with entropy balancing

Panel A: Post-balancing descriptive statistics

Variable Means

Capex CF Ebitda EPS OperMar Tax

Treat Cont. Treat Cont. Treat Cont. Treat Cont. Treat Cont. Treat Cont.

Earnings -0.006 -0.006 -0.011 -0.011 -0.017 -0.017 0.004 0.004 0.003 0.003 0.007 0.007

Ln Market cap 7.300 7.300 7.149 7.149 6.846 6.846 7.472 7.471 7.293 7.293 7.186 7.186

StdDev(earnings) 0.021 0.021 0.022 0.022 0.026 0.026 0.016 0.016 0.021 0.021 0.018 0.018

Prior Forecast 0.517 0.517 0.541 0.541 0.582 0.582 0.820 0.819 0.504 0.504 0.445 0.445

Prior Analyst ask 0.472 0.472 0.404 0.404 0.504 0.504 0.540 0.540 0.394 0.394 0.392 0.392

PM forward looking 13.766 13.765 15.719 15.718 14.716 14.714 16.061 16.059 15.972 15.970 13.786 13.785

Inst Ownership 0.520 0.520 0.558 0.558 0.452 0.452 0.585 0.585 0.596 0.596 0.585 0.584

Ln num analysts 1.781 1.780 1.796 1.796 1.644 1.644 1.907 1.907 1.924 1.924 1.834 1.833

Litigation 0.042 0.042 0.054 0.054 0.043 0.043 0.058 0.058 0.047 0.047 0.042 0.042

Industry forecast 0.341 0.341 0.329 0.329 0.239 0.239 0.550 0.550 0.262 0.262 0.286 0.286

Analyst ask (Non-Fwd Looking) 0.486 0.485 0.492 0.492 0.481 0.481 0.613 0.613 0.445 0.445 0.360 0.360

This table reports the mean values of determinants of a request for a forecast after entropy balancing. Under entropy balancing, control observations are weighted

such that first and second moments (mean and variance) of control observations (those in which there was no request for a forecast) closely approximate the first

and second moments of the treatment observations (those in which there was a request for a forecast). Variable definitions are provided in Appendix 1. Continuous

variables are winsorized at the 1% and 99% levels.

Panel B: Regression analysis

Dependent Variable: Forecast (t+4)

Capex CF Ebitda EPS OperMar Tax

Analyst ask 0.033*** 0.054*** 0.085*** 0.053*** 0.043*** 0.017***

(7.63) (12.12) (12.59) (14.27) (8.31) (3.81)

Earnings 0.139*** 0.026 0.039 0.292*** 0.007 0.121***

(5.01) (0.97) (1.00) (9.19) (0.16) (3.32)

Ln Market cap 0.004** 0.026*** -0.001 0.029*** 0.022*** 0.012***

(2.18) (14.86) (-0.45) (20.29) (10.83) (7.09)

StdDev(earnings) 0.042 -0.080 0.191** -0.549*** 0.093 -0.221***

(0.65) (-1.30) (2.20) (-7.93) (1.22) (-3.47)

Prior Forecast 0.227*** 0.191*** 0.246*** 0.245*** 0.208*** 0.228***

(44.77) (36.27) (31.07) (45.92) (34.77) (40.74)

Prior Analyst ask 0.024*** 0.051*** 0.049*** 0.043*** 0.053*** 0.004

(5.10) (10.35) (6.45) (10.78) (8.88) (0.85)

PM forward looking 0.003*** 0.005*** 0.005*** 0.006*** 0.004*** 0.004***

(14.01) (21.52) (15.27) (33.88) (15.34) (15.53)

Inst Ownership 0.007 0.039*** 0.002 0.082*** 0.048*** 0.060***

(0.94) (5.58) (0.23) (13.67) (5.92) (8.33)

Ln num analysts 0.022*** -0.018*** -0.002 0.031*** 0.001 0.027***

(5.48) (-4.22) (-0.35) (8.43) (0.29) (6.18)

Litigation 0.018 -0.005 -0.012 -0.030*** -0.004 0.014

(1.57) (-0.49) (-0.72) (-3.59) (-0.31) (1.17)

Industry forecast 0.007 0.025* 0.010 0.008 0.019 -0.031*

(0.51) (1.67) (0.39) (0.65) (0.92) (-1.86)

Analyst ask (Non-Fwd Looking) 0.022*** 0.030*** 0.057*** 0.008** 0.028*** 0.019***

(4.58) (6.24) (7.64) (2.10) (4.91) (3.66)

Observations 142,209 142,209 142,209 142,209 142,209 142,209

Pseudo R-squared 5.5% 11.3% 10.8% 11.7% 9.6% 7.8%

This table models the determinants of a forecast in period (t+4) using an entropy-balanced sample. Variable definitions

are provided in Appendix 1. Continuous variables are winsorized at the 1% and 99% levels. Ln indicates the natural

log function. Variables preceded by Prior are measured over the four quarters from t-4 to t. Thus, Prior analyst ask is

an indicator variable equal to 1 if at least one analyst asked about the item during any of the quarters t-4, t-3, t-2, and

t-1. Fixed effects are taken by year and industry. Standard errors are clustered by firm. *, **, and *** represent

significance at 10%, 5%, and 1%, respectively (two-tailed). Marginal effects are reported in place of coefficient values.

T-values are presented beneath the marginal effects in parentheses.

51

Table 8: Determinants of analysts’ guidance requests

Dependent Variable: Ask(t)

Capex CF Ebitda EPS OperMar Tax

Forecast 0.021*** 0.029*** 0.035*** 0.050*** 0.027*** 0.004**

(11.69) (16.89) (24.22) (22.21) (17.04) (2.24)

Earnings -0.037*** -0.048*** -0.017*** 0.059*** 0.051*** 0.143***

(-3.86) (-5.81) (-2.90) (3.36) (4.16) (7.13)

Ln Market cap 0.007*** 0.002*** 0.001 0.008*** 0.004*** 0.003***

(10.99) (4.22) (1.39) (10.04) (7.59) (5.65)

StdDev(earnings) -0.095*** -0.028 0.010 -0.329*** -0.045** -0.143***

(-3.95) (-1.46) (0.79) (-9.37) (-2.48) (-5.83)

Prior Forecast 0.009*** 0.018*** 0.020*** 0.036*** 0.015*** 0.008***

(4.89) (9.90) (11.69) (13.70) (9.24) (4.13)

Prior Analyst ask 0.048*** 0.038*** 0.031*** 0.052*** 0.027*** 0.038***

(24.69) (21.11) (19.45) (24.05) (16.93) (22.72)

PM forward looking -0.000*** 0.000*** 0.000 0.001*** -0.000 -0.000***

(-3.59) (4.64) (0.06) (6.29) (-0.03) (-4.12)

Inst Ownership -0.010*** -0.000 -0.011*** 0.001 -0.001 0.009***

(-3.44) (-0.04) (-5.84) (0.28) (-0.33) (3.57)

Ln num analysts -0.008*** 0.001 -0.003** 0.005** 0.004*** 0.001

(-5.07) (0.66) (-2.53) (2.40) (2.96) (0.62)

Litigation 0.001 0.006** -0.000 0.006 -0.004* -0.008***

(0.19) (1.96) (-0.17) (1.54) (-1.65) (-2.66)

Industry forecast 0.029*** 0.023*** 0.021*** 0.036*** 0.016*** 0.009*

(5.73) (4.95) (5.31) (5.93) (3.40) (1.72)

Analyst ask (Non-Fwd Looking) 0.079*** 0.085*** 0.052*** 0.077*** 0.061*** 0.087***

(44.58) (54.42) (39.56) (37.44) (42.41) (53.75)

Observations 145,723 145,723 145,637 145,728 145,678 145,502

Pseudo R-squared 13.7% 11.9% 27.4% 11.3% 14.9% 10.5%

This table presents the results of a logit regressions that model the determinants of analysts’ requests for six forecast

types (Capex = capital expenditures; CF = cash flow; Ebitda = earnings before interest, taxes, depreciation, and

amortization; EPS = net income or earnings per share; OperMar = operating margin or operating profit; Tax = tax

rate). Variable definitions are provided in Appendix 1. Continuous variables are winsorized at the 1% and 99% levels.

Ln indicates the natural log function. Variables preceded by Prior are measured over the four quarters from t-4 to t.

Thus, Prior analyst ask is an indicator variable equal to 1 if at least one analyst asked about the item during any of the

quarters t-4, t-3, t-2, and t-1. Fixed effects are taken by year and industry. Standard errors are clustered by firm. *, **,

and *** represent significance at 10%, 5%, and 1%, respectively (two-tailed). Marginal effects are reported in place

of coefficient values. Z-values are presented beneath the marginal effects in parentheses.

52

Table 9: Analysts’ mitigation of guidance incentives

Dependent Variable: Forecast (t+4)

Capex CF Ebitda EPS OperMar Tax

Analyst ask 0.060*** 0.045*** 0.021** 0.050*** 0.035** 0.053***

(4.22) (2.84) (2.01) (2.88) (2.30) (3.15)

Earnings 0.091*** -0.000 -0.004 0.339*** 0.052*** 0.332***

(5.10) (-0.01) (-0.42) (12.37) (3.03) (9.71)

Earnings x Analyst ask 0.001 0.043 0.042** -0.106* 0.008 -0.245***

(0.04) (1.22) (1.99) (-1.70) (0.14) (-3.30)

Ln Market cap 0.008*** 0.016*** -0.002*** 0.026*** 0.014*** 0.013***

(5.96) (12.73) (-3.12) (14.94) (11.63) (9.76)

Ln Market cap x Analyst ask -0.006*** -0.001 0.002 0.000 -0.001 -0.005**

(-2.95) (-0.32) (1.05) (0.07) (-0.54) (-2.24)

StdDev(earnings) -0.126*** -0.037 0.057** -0.407*** 0.035 -0.264***

(-3.00) (-0.88) (2.37) (-7.24) (1.02) (-5.05)

StdDev(earnings) x Analyst ask 0.278*** -0.015 0.039 0.110 0.054 0.134

(3.31) (-0.17) (0.75) (0.92) (0.66) (1.18)

Prior Forecast 0.171*** 0.136*** 0.103*** 0.215*** 0.119*** 0.169***

(42.53) (37.25) (33.48) (47.74) (35.57) (46.44)

Prior Analyst ask 0.023*** 0.034*** 0.026*** 0.049*** 0.033*** 0.008***

(7.81) (11.77) (11.98) (14.10) (12.99) (2.78)

PM forward looking 0.002*** 0.003*** 0.001*** 0.006*** 0.002*** 0.003***

(16.65) (24.38) (15.43) (29.20) (18.90) (18.34)

Inst Ownership 0.019*** 0.028*** -0.004 0.076*** 0.034*** 0.061***

(3.23) (4.99) (-0.90) (10.40) (6.75) (10.44)

Ln num analysts 0.003 -0.007** -0.003 0.019*** -0.000 0.019***

(0.92) (-2.37) (-1.43) (4.85) (-0.04) (6.29)

Litigation -0.007 0.001 -0.004 -0.023*** -0.002 -0.013**

(-1.09) (0.14) (-0.80) (-2.75) (-0.30) (-2.13)

Industry forecast 0.040*** 0.042*** 0.023*** 0.027** 0.025** 0.017

(3.76) (4.15) (2.77) (2.30) (2.34) (1.61)

Analyst ask (Non-Fwd Looking) 0.019*** 0.023*** 0.029*** 0.014*** 0.024*** 0.019***

(7.82) (9.83) (15.08) (5.03) (10.48) (6.63)

Observations 143,960 145,157 144,903 145,202 145,202 144,981

Pseudo R-squared 20.50% 17.90% 29.60% 21.80% 18.50% 22.20%

This table presents the results of a logit regressions that model the determinants of managers provision of six forecast

types (Capex = capital expenditures; CF = cash flow; Ebitda = earnings before interest, taxes, depreciation, and

amortization; EPS = net income or earnings per share; OperMar = operating margin or operating profit; Tax = tax

rate). Variable definitions are provided in Appendix 1. Continuous variables are winsorized at the 1% and 99% levels.

Ln indicates the natural log function. Variables preceded by Prior are measured over the four quarters from t-4 to t.

Thus, Prior analyst ask is an indicator variable equal to 1 if at least one analyst asked about the item during any of the

quarters t-4, t-3, t-2, and t-1. Fixed effects are taken by year and industry. Standard errors are clustered by firm. *, **,

53

and *** represent significance at 10%, 5%, and 1%, respectively (two-tailed). Marginal effects are reported in place

of coefficient values. Z-values are presented beneath the marginal effects in parentheses.

54

Table 10: Managers’ response to analysts’ requests

Dependent Variable: Forecast (t+4)

Capex CF Ebitda EPS OperMar Tax

Earnings 0.140** 0.029 0.077 0.269*** 0.096 0.139

(2.47) (0.57) (1.28) (4.11) (0.93) (1.39)

Ln Market cap 0.005 0.029*** 0.002 0.028*** 0.023*** 0.012***

(1.56) (7.56) (0.31) (8.66) (5.06) (3.16)

StdDev(earnings) 0.139 -0.105 0.291* -0.450*** 0.088 -0.283*

(1.06) (-0.75) (1.79) (-3.15) (0.55) (-1.77)

Prior Forecast 0.213*** 0.190*** 0.231*** 0.213*** 0.207*** 0.191***

(21.77) (18.30) (15.72) (19.97) (18.05) (19.52)

Prior Analyst ask 0.023*** 0.052*** 0.012 0.022*** 0.036*** 0.003

(2.77) (5.99) (0.96) (2.98) (3.58) (0.37)

PM forward looking 0.003*** 0.005*** 0.006*** 0.006*** 0.004*** 0.003***

(7.76) (10.87) (9.17) (15.17) (8.49) (8.23)

Inst Ownership 0.007 0.044*** -0.022 0.070*** 0.061*** 0.068***

(0.43) (2.77) (-1.03) (5.32) (3.13) (4.41)

Ln num analysts 0.016** -0.023** -0.004 0.029*** 0.005 0.024***

(1.97) (-2.55) (-0.32) (3.89) (0.45) (2.81)

Litigation 0.043** -0.010 -0.011 -0.033** 0.007 0.043**

(2.05) (-0.53) (-0.31) (-2.08) (0.31) (2.10)

Industry forecast 0.005 0.015 0.046 0.002 0.065 -0.019

(0.18) (0.50) (0.98) (0.09) (1.57) (-0.62)

Analyst ask (Non-Fwd Looking) 0.014* 0.022*** 0.019 -0.004 0.012 0.007

(1.68) (2.66) (1.58) (-0.56) (1.32) (0.89)

Observations 12,086 10,715 6,163 18,118 7,842 9,866

Pseudo R-squared 14.5% 18.4% 17.9% 19.8% 16.6% 15.1%

This table models the determinants of when managers’ provide forecasts in quarter t+4 after analysts make a request

in quarter t. The dependent variable is a binary variable set equal to one if managers provide forecast and the sample

is restricted to observations in which analysts make a request. Variable definitions are provided in Appendix 1.

Continuous variables are winsorized at the 1% and 99% levels. Ln indicates the natural log function. Variables

preceded by Prior are measured over the four quarters from t-4 to t. Thus, Prior analyst ask is an indicator variable

equal to 1 if at least one analyst asked about the item during any of the quarters t-4, t-3, t-2, and t-1. Fixed effects are

taken by year and industry. Standard errors are clustered by firm. *, **, and *** represent significance at 10%, 5%,

and 1%, respectively (two-tailed). Marginal effects are reported in place of coefficient values. Z-values are presented

beneath the marginal effects in parentheses.