Scripted Earnings Conference Calls as a Signal of Future Firm Performance
Joshua Lee
Olin Business School
Washington University in St. Louis
St. Louis, MO 63130-6431
joshlee@wustl.edu
January 2014
Abstract:
I examine whether market participants infer negative information about future firm performance
from managers’ scripted responses to questions received during earnings conference calls. I argue
that firms script their Q&A session responses prior to periods of poor performance to avoid the
inadvertent disclosure of information that can be used to build a lawsuit against the firm. Using a
unique measure of conference call Q&A scripting, I provide evidence that scripted Q&A is
negatively associated with future earnings and future cash flows, suggesting that, on average, firms
script their Q&A when future performance is poor. I also find a negative market reaction to scripted
Q&A and downward revisions in analysts’ forecasts following scripted Q&A, suggesting that
investors interpret scripted Q&A as a negative signal of future firm performance. I also find that
firms are less likely to guide future earnings when Q&A is scripted and that analysts’ forecasts are
less accurate following scripted Q&A, suggesting that firms provide less information to market
participants when Q&A is scripted.
I thank Richard Frankel my dissertation committee chair for his guidance and mentorship. I also thank Gauri Bhat,
Andrew Call, Ted Christensen, John Donovan, Bryan Graden, Jared Jennings, Chad Larson, Xiumin Martin, Lorien
Stice-Lawrence, and Jake Thornock for their helpful comments. In addition, I thank workshop participants at
Washington University in St. Louis and the Accounting Research Symposium at Brigham Young University.
1. Introduction
Numerous books and articles describe techniques for predicting firm fundamentals using
quantitative information found in firm disclosures. Recent academic studies find that qualitative
disclosures also inform the market about future firm performance.
For example, market
participants gain useful information for predicting future performance by analyzing the “tone”
(i.e., net optimistic language) of news stories (Tetlock et al. 2008), annual reports (Loughran and
McDonald 2011), earnings press releases (Davis, Piger, and Sedor 2012), and earnings conference
calls (Davis, Ge, Matsumoto, and Zhang 2012; Price et al. 2012). These studies argue that
disclosure tone provides a signal of managers’ or others’ perceptions of firm fundamentals. This
paper examines whether market participants use an alternative qualitative signal from quarterly
earnings conference calls to predict firm fundamentals. Specifically, I test whether market
participants infer negative information about future firm performance when firms script responses
to questions received during the question and answer (Q&A) session of the earnings conference
call.
The Q&A session of the conference call is a unique setting in which managers and investors
interact in two-way communication. While this interactive communication increases the flow of
information to the market (Tasker 1998; Frankel et al. 1999; Bowen et al. 2002; Bushee et al. 2003;
Matsumoto et al. 2011), research examines the potential disadvantages to the firm of allowing
analysts and investors to ask questions in an open forum. For example, Hollander et al. (2010)
suggest that the impromptu format of the Q&A session enables investors to prompt managers to
reveal information they do not yet wish to reveal.1 The potential for this type of “inadvertent
1
Matsumoto et al. (2011) corroborate this argument. They find greater information content for the Q&A session of
the conference call relative to both the presentation session and the accompanying press release, suggesting that “some
disclosures would perhaps not have been made were it not for questioning by analysts.”
1
disclosure” provides incentives for firms to prepare scripted responses to anticipated questions in
advance of the conference call.
I argue that firms are likely to script their Q&A when future performance is poor to avoid
inadvertently providing information that can be used in litigation against the firm. Research
suggests that when future performance is poor, litigation risk is high, and disclosure is costly. For
example, Cutler et al. (2013) find evidence that firms with greater disclosure during the litigation
class period are more likely to receive significant settlements against the firm. In addition, Rogers
and Van Buskirk (2009) find that firms reduce disclosure following class action lawsuits,
consistent with significant costs of disclosure in the litigation process. I similarly argue that firms
holding regular earnings conference calls are likely to avoid the costs of providing information
that can be used to build a case against the firm by preparing more careful and scripted responses
to anticipated questions in advance of the conference call. If so, Q&A scripting can serve as a
signal of negative future firm performance. However, if firms are more forthcoming with
disclosure to prevent litigation as other research suggests (Skinner 1994; Kasznik and Lev 1995),
scripting is unlikely to precede negative future events and would not provide a signal to market
participants.
Firms may also script responses to anticipated questions for reasons unrelated to future
firm performance. For example, firms may script their responses to avoid inadvertently revealing
proprietary information about the firm’s products. Alternatively, managers who are less confident
in their ability to respond to analysts’ questions in real time may use scripted responses to avoid
the reputational costs of providing a “botched” answer to an analyst’s question. Hence, market
participants will interpret Q&A scripting negatively only if they assign a higher probability that
firms script for expected performance reasons rather than to avoid proprietary or reputational costs.
2
I develop a measure of scripting based on linguistics research in computational stylistics
that compares the stylistic properties of texts to determine authorship. I specifically examine the
difference in the speaking style of the CEO during the presentation and Q&A sessions of the call
and argue that CEOs who change their speaking style during the Q&A session are less likely to be
relying on a script to respond to analysts’ questions. The implicit assumption in this measure is
that the presentation session of the call is scripted and only partially prepared by the CEO. 2 Indeed,
the investor relations team typically drafts the script and the CEO makes edits as necessary. Thus,
the difference in the CEO’s speaking style between the presentation session and the Q&A session
identifies whether the CEO is using his/her unique style to answer questions or is relying on a
script prepared by other individuals at the firm.
Using a sample of 30,773 quarterly earnings conference call transcripts for 2,384 firms
over the period from 2002 to 2011, I test the association between my measure of scripting and
measures of future firm accounting performance and the market’s response to the conference call.
I find a negative association between Q&A scripting and both return on assets and operating cash
flows in the four quarters subsequent to the conference call. These results are robust to using
measures of unexpected future earnings and suggest that firms script their Q&A responses when
they possess negative information about future firm performance. I also provide evidence that
firms script their Q&A immediately prior to receiving a class action lawsuit consistent with firms
scripting prior to bad news events.
2
Discussions with a former member of the internal investor relations team at Morgan Stanley verify that the
presentation session of the call is scripted and is prepared by the investor relations team. Responses to expected
questions are also often scripted. The member of the investor relations team estimated that the team is able to
anticipate roughly eighty percent of the questions and draft prepared responses prior to the call. He also verified that
the executives always read from the prepared script for the presentation session but often go off script during the Q&A
session. However, for certain questions (such as a question received about level 3 fair value measurements) the
executives respond from the prepared script “word for word.”
3
I next test the market reaction to scripted conference calls to identify (1) whether investors
are able to discern the level of scripting done by management when answering questions and (2)
whether investors view scripted responses as a negative signal of future firm performance.
Controlling for the news in the earnings announcement, I find that firms that script their Q&A have
significantly lower size and book-to-market adjusted returns on the day of the conference call. In
addition, to more precisely control for news in the current period earnings surprise, I use TAQ data
for a sub-sample of calls for which I have actual start times and find a negative association between
scripting and abnormal returns following the call. This suggests that the negative association
between scripting and the abnormal return on the day of the call is not solely due to the negative
association between scripting and the current period earnings surprise, but that scripting also serves
as a signal of future firm performance. In additional analysis, I find that sell-side equity analysts
make downward revisions to their earnings forecasts in the 30 days following scripted conference
calls, corroborating the market return tests and suggesting that analysts incorporate the negative
implications of scripted Q&A into their forecasts.
Finally, the negative market reaction to scripted Q&A is consistent with two explanations:
1) investors interpret scripted responses as a negative signal of future performance or 2) managers
use scripted responses to provide additional information about the negative expected performance.
I attempt to identify the most likely explanation in two ways. First, I directly test whether firms
provide additional information about future earnings when conference calls are scripted. I find
evidence that firms are less likely to issue earnings guidance on the day of the conference call
when their Q&A is scripted suggesting that firms provide less, not more, information. Second, if
firms provide additional information during scripted conference calls, analysts are likely to have a
richer information set allowing them to make more accurate forecasts of future earnings. I find,
4
however, that analyst forecast revisions following scripted conference calls are less, not more,
accurate. Hence, firms are unlikely to use scripted conference calls as a means of providing
additional information to the market. Rather, the negative market reaction to scripted calls is
consistent with scripted calls providing a signal of future firm performance.
This paper contributes to the literature that examines the linguistic features of firm
disclosures to extract information about the firm.3 Prior research finds that disclosure tone and
vocal cues in conference call speech are informative about firms’ future performance (Davis, Ge,
Matsumoto, and Zhang 2012; Price et al. 2012; Mayew and Venkatachalam 2012). Other research
finds that deceptive speech during conference calls predicts accounting misstatements (Larcker
and Zakolyukina 2012). I add to this literature by examining an alternative conference call feature
– scripting of the Q&A session – and find that my measure of scripting is correlated with future
accounting performance, the market reaction at the time of the call, managers’ guidance decisions,
and analyst forecast properties following the call. This paper also contributes to the literature that
examines whether conference calls provide material information to conference call participants.
Prior research finds significant trading activity at the time of the call (Frankel et al. 1999; Bushee
et al. 2003; Bushee et al. 2004, Lansford et al. 2009), improvements in analyst forecast accuracy
following the call (Bowen et al. 2002), more timely incorporation of earnings news into prices for
firms initiating conference calls (Kimbrough 2005), and a reduction in information asymmetry for
firms holding regular quarterly calls (Brown et al. 2004). This paper finds firms provide less
information to market participants when conference calls are scripted.
3
Examples include Li (2008) and Lehavy et al. (2011) who examine the readability of financial reports, Brown and
Tucker (2011) who examine firms’ year-over-year MD&A modifications, Li (2010) who examines forward looking
statements in MD&A disclosures, and Tetlock et al. (2008), Loughran and McDonald (2011), Davis and TamaSweet (2012), Rogers et al. (2011), Davis, Piger, and Sedor (2012), and Blau et al. (2012) who examine disclosure
tone in other settings.
5
The rest of the paper is organized as follows. Section 2 discusses the prior literature and
develops the hypotheses. Section 3 outlines the empirical models used to test each hypothesis.
Section 4 describes the sample selection process and summary statistics. Section 5 discusses the
results. Section 6 examines whether firms provide more or less information during scripted calls.
Section 7 provides sensitivity tests and additional analyses. Section 8 concludes the paper.
2. Background and Hypothesis Development
Quarterly earnings conference calls have become an important form of voluntary
disclosure. In 2002, approximately 17 percent of Compustat firms held at least one earnings
conference call during the year and by 2011, the percentage increased to 36 percent (see Figure 1).
For firms with analyst following, the percentages are much higher reaching 69% by 2011. 4 In
addition, firms that begin holding quarterly calls are likely to continue holding calls in the future.
Hence, by implicitly committing to hold quarterly earnings conference calls, firms commit to a
high level of transparency with the capital market.
Conference calls generally involve two sessions: a presentation session in which
management discusses results of operations for the quarter and a question and answer (Q&A)
session in which analysts and investors ask questions of management. The conference call Q&A
session is a unique voluntary disclosure setting in which managers and investors interact in twoway communication. Other forms of voluntary disclosure (e.g., press releases) are more one-sided.
By allowing investors to ask questions, firms allow for the possibility that managers inadvertently
reveal information the firm would have otherwise chosen to keep private. If disclosure of certain
4
These percentages include all firms on Compustat regardless of whether they have unique Factiva identifiers. If I
restrict the focus to Compustat firms with Factiva identifiers, the percentages are much higher – 77 percent in 2011
(85 percent for firms with analyst following). Thus, it is possible that these statistics understate the true number of
firms holding conference calls since Factiva may not cover all firms on Compustat.
6
information is costly, firms can script their responses to anticipated questions as a means of
providing more careful disclosure to outsiders.
Firms typically employ an investor relations team to prepare a script for the presentation
session with management providing edits and comments as necessary. The final script, therefore,
is more likely to reflect the style of the investor relations team that prepared it, rather than the
executive who eventually reads it over the call. The Q&A session, on the other hand, is more open
and is considered a less scripted portion of the call (see, e.g., Matsumoto et al. 2011). However,
if managers can anticipate or even prompt participants to submit questions prior to the call, investor
relations teams can prepare scripted responses to these questions. Indeed, investor relations
consultants often encourage firms to prepare for questions prior to conference calls. For example,
Westwicke Partners, an investor relations firm, in a recent blog entitled “Best Practices of Earnings
Conference Call Preparation” provide the following guidance: “Compile the questions you expect
to hear during the call Q&A…Survey your sell-side analysts beforehand to learn what they are
likely to ask.”5
I argue that firms are most likely to script responses to anticipated questions when future
firm performance is poor. Firms with poor expected performance are subject to greater litigation
risk and are likely more careful about the disclosures they make to external market participants
since disclosures are often cited in class action lawsuits. For example, Cutler et al. (2013) find
evidence that greater disclosure during the litigation class period results in a higher likelihood of
significant settlements against the firm. Rogers and Van Buskirk (2009) also find evidence that
firms reduce disclosure following class action lawsuits suggesting that disclosure is costly during
litigation. Hence, firms are likely to use more careful and scripted disclosure when future
5
http://westwickepartners.com/2013/01/best-practices-for-earnings-call-preparation/
7
performance is poor to avoid the possibility of inadvertently revealing information that can be used
to build a case against the firm. My first hypothesis is stated in the alternative form as follows:
H1: Firms prepare scripted responses to anticipated questions for the conference call
Q&A session when future firm performance is poor.
My first hypothesis is less likely to hold if firms improve disclosure to prevent litigation as
some research suggests. For example, Skinner (1994) and Kasznik and Lev (1995) find that firms
are more likely to issue earnings guidance prior to periods of large negative earnings surprises
relative to periods of large positive earnings surprises to avoid large negative market reactions at
the earnings announcement date. In addition, Baginski et al. (2002) find that U.S. firms are more
likely to issue earnings forecasts during periods of earnings declines relative to Canadian firms
that operate in an environment where securities laws and judicial interpretations create a lower
threat of litigation. In addition, my first hypothesis is less likely to hold if expected litigation costs
are small or if firms believe scripting is unsuccessful in preventing significant settlements.
Whether conference call Q&A scripting is negatively associated with future firm performance is,
therefore, an empirical question.
My second hypothesis examines investors’ response to scripted earnings conference calls
as a joint test of (1) whether investors discern the level of scripting and (2) whether investors’
interpret scripted responses as a signal that managers possess negative information about future
firm performance. Prior research suggests investors glean useful information from conference
calls. For example, investors respond to managers’ conference call tone (Davis, Ge, Matsumoto,
and Zhang 2012; Price et al. 2012) and to positive and negative affective states in vocal cues from
conference call speech (Mayew and Venkatachalam 2012). Other research suggests investors
respond negatively when managers refuse to answer specific questions during the Q&A session
8
(Hollander et al. 2010). If firms script their calls prior to periods of negative firm performance,
and investors are able to discern whether managers are responding to questions from a script, I
expect a negative market response to these calls. My second hypothesis is stated as follows:
H2: Investors interpret scripted Q&A responses negatively.
3. Research Design
3.1 Conference call Q&A scripting measure
The empirical challenge of this paper is identifying cross-sectional variation in the extent
of conference call Q&A scripting. I develop my scripting measure using a computational stylistics
method developed in the linguistics literature to identify the authors of documents with unknown
or disputed authorship (see, e.g., Stramatatos 2009).
The most well-known “authorship
attribution” studies use linguistic methods to ascertain who wrote twelve of the Federalist Papers
in which both Alexander Hamilton and James Madison claim authorship (Mosteller and Wallace
1963; Koppel, Schler, and Argamon 2009). Prior research suggests that the most effective method
for authorship attribution is the comparison of a set of function words between two documents
(Burrows, 1987; Stramatatos 2009; Mosteller 2010). Function words are those with primarily
grammatical functions and include articles (e.g., a, an, the), conjunctions (e.g., and, or, so),
pronouns (e.g., I, me, we), prepositions (e.g., of, on, in), and auxiliary verbs (e.g., is, do, can).6
Mosteller (2010) suggests that function words are the best stylistic discriminators between two
authors because they are unrelated to the topic discussed, and they reflect minor or even
unconscious preferences of the author. Thus, an author’s use of function words uniquely identifies
6
See Appendix A for a complete list of function words used in this study.
9
his/her style. Using this approach, studies overwhelmingly identify James Madison as the author
of the twelve disputed Federalist Papers (Mosteller and Wallace 1963).7
Using this method, I examine the extent of scripting of the Q&A session of the conference
call by comparing the use of function words by the CEO during the presentation session to the use
of function words by the CEO during the Q&A session.8 I assume the presentation session of the
call is a scripted outline of the performance of the firm during the quarter. Conversations with an
investor relations consultant and a member of the internal investor relations team at Morgan
Stanley confirm this assumption. The set of function words during this session of the call thus
serves as a baseline for which I can compare the set of function words during the Q&A session of
the call. A CEO is less likely to be relying on scripted responses to conference call questions if
the use of function words during the Q&A session is less similar to the use of function words
during the presentation session of the call. In other words, if the CEO’s speaking style changes
from the presentation session to the Q&A session, he/she is less likely to be using a script to
respond to analysts’ and investors’ questions.
For each conference call, I first identify the presentation and Q&A sessions of the call by
searching for key words such as “question” and “Q&A” within 2 lines of other key words such as
“take” or “open up.”9 I then identify the chief executive officer using the titles provided during
the call and obtain the portions of the call in which the executive is speaking.10 Next, I create two
7
Other methods used in prior work include comparing sentence lengths, word lengths, or uses of frequent words
between two documents. However, these methods are shown to be poor indicators of authorship (see Mosteller 2010).
For this reason, I use the most accepted approach of comparing function words between two documents.
8
The results of all tests remained qualitatively and quantitatively similar if I use the spokesman executive to compute
the scripting measure where the spokesman is defined as the CEO or CFO who speaks for the longest portion of the
conference call. See Section 6.1 for additional detail.
9
During the introduction of the call, the executives often provide an outline for the call and state they will be opening
up the call for questions later on in the call. To ensure I obtain the key words when the Q&A session truly begins
rather than a reference to it later in the call, I require the Q&A session to start at least 10% into the call.
10
In many instances, the conference call speaker is identified using an abbreviated version of the executive’s name.
For example, the executive might be referred to as David when introduced but then Dave later in the call. I manually
correct these differences to ensure I obtain the full text of the call for each executive.
10
vectors of the counts of the function words spoken by the CEO in each session of the call: 𝑣𝑄𝐴 and
𝑣𝑃𝑅𝐸𝑆 , respectively, where QA represents the Q&A session and PRES represents the presentation
session. I then compute my measure of scripting as the cosine similarity between the two vectors
using the following formula:
𝒗
∙𝒗
𝑺𝑪𝑹𝑰𝑷𝑻 = 𝒄𝒐𝒔(𝜽) = ‖𝒗 𝑸𝑨‖‖𝒗𝑷𝑹𝑬𝑺 ‖
𝑸𝑨
𝑷𝑹𝑬𝑺
(1)
where θ is the angle between 𝑣𝑄𝐴 and 𝑣𝑃𝑅𝐸𝑆 , (∙) is the dot product operator, and ‖𝑣𝑖 ‖ is the length
of vector 𝑣𝑖 (i is equal to QA and PRES). The cosine similarity measure captures the uncentered
correlation between two vectors and provides an estimate of the similarity in the use of function
words by the executive during the presentation and Q&A sessions of the conference call.11 Its
values range between 0 and 1 where greater values indicate greater similarity. For ease in
economic interpretation in the multivariate analyses, I rank the SCRIPT measure into deciles from
0 to 9 and divide by 9 (RSCRIPT).12 I also require at least 200 words to be spoken by the CEO in
both the presentation session and the Q&A session of the call to reduce measurement error.
I verify the construct validity of the cosine similarity measure in identifying the speaking
style of the CEO by computing the cosine similarity measure between the vector of function word
counts spoken by CEO j during the Q&A (presentation) session for firm i in quarter t to the vector
of the combined conference call Q&A (presentation) sessions given by CEO j for firm i during all
other quarters. I then compute the cosine similarity between the CEO j Q&A (presentation)
function word count vector in quarter t to nine randomly selected combined word count vectors
for CEOs of other firms across the sample period. I then rank the actual CEO vector relative to
Brown and Tucker (2011) use the cosine similarity measure to compare firms’ MD&A disclosures over time.
Their word count vectors include all unique words in the disclosure to compare content, whereas I use only the
counts of function words to compare speaking style.
12
The results remain qualitatively unchanged if I use the unranked cosine similarity measure.
11
11
the nine randomly selected CEO vectors, where values of 1 (10) indicate the actual CEO vector is
the most (least) similar relative to the nine randomly-selected CEO vectors. Figure 2 presents the
cumulative percentage of firms in each ranking. If the ranking were random, the percentage of
firms in each ranking would be 10 percent. When comparing the Q&A session during the quarter
to the Q&A sessions of other quarters (Q&A to Q&A), the results indicate that 79.7 percent of the
similarity scores are highest for the actual CEO relative to the nine randomly selected CEOs. The
similarity score for the actual CEO is one of the top three highest for 93.3 percent of the
observations suggesting that the similarity score does a good job of identifying the speaking style
of the CEO. Similarly, when comparing the presentation session during the quarter to the
presentation sessions of other quarters (PRES to PRES), the results indicate that 80.5 percent of
the similarity scores are highest for the actual CEO relative to the nine randomly selected CEOs
suggesting that those who script the presentation session (e.g., the investor relations team) have
uniquely identifiable styles.13
I then compute the cosine similarity between the presentation session vector for CEO j of
firm i in quarter t and 1) the Q&A session vector for CEO j of firm i in quarter t and 2) nine
randomly-selected Q&A session vectors for CEOs of other firms. I then rank the similarity score
for the actual CEO vector relative to the randomly-selected CEO vectors. Figure 2 plots the
13
I further verify the accuracy of the cosine similarity measure in the most common setting used in the linguistics
literature: The Federalist papers. I compute the cosine similarity between the vector of word counts for each Federalist
paper and the vectors of word counts for the three known authors of the Federalist papers: John Jay, James Madison,
and Alexander Hamilton. I assign an author to each paper based on the highest similarity score for each paper relative
to the vectors of word counts for all other papers written by the three authors. For all five papers written by John Jay,
the similarity score correctly identifies John Jay as the author. For the 51 papers known to have been written by
Alexander Hamilton, the similarity score correctly identifies 48 as written by Hamilton and incorrectly identifies 3 as
written by Madison. For the 14 papers known to have been written by James Madison, the similarity score correctly
identifies 12 as written by Madison and incorrectly identifies 2 as written by Hamilton. For the 12 disputed papers, I
find 10 of the similarity scores are highest for James Madison and 2 of the similarity scores are highest for Alexander
Hamilton. These results are fairly consistent with prior research and provide additional evidence that the similarity
score using the list of function words employed in this study provides an accurate measure for detecting subtle
differences in style between two texts.
12
cumulative percentage of conference calls in each ranking (PRES to Q&A). I find that only 21.4
percent of the similarity scores are highest for the Q&A session of the actual CEO compared to
the nine randomly-selected Q&A sessions of other CEOs. This suggests two important points.
First, CEOs have unique styles relative to the investor relations teams that prepare the presentation
sessions of the calls. If not, the percentage of firms with rankings closer to 1 would have been
closer to 100 percent. Second, the percentage of firms with a ranking of 1 is greater than what
would be expected if the rankings were random (21 percent relative to 10 percent) suggesting that
some firms script their Q&A.
3.2 Test of hypothesis one
I test the association between Q&A scripting and firms’ future accounting performance
(Hypothesis 1) by estimating the following model similar to Core, et al. (1999), Bowen et al.
(2008), and Davis, Piger, and Sedor (2012):
FUT PERFi,t =
α0 + α1 RSCRIPTi,t + α2 PERFi,t + α3 EARN SURPi,t + α4 ln(MVEi,t) +
α5 INSTOWNi,t + α6 ln(ANAL FOLLi,t) + α7 TURNOVERi,t + α8 EARN
VOLi,t + α9 RET VOLi,t + α10 ln(AGEi,t) + α11 GUIDANCEi,t + α12 GUID
SURPi,t + α13 TONEi,t + α14 ln(CEO WC PRESi,t) + α15 ln(CEO WC
QAi,t) + YEARQTR + INDUSTRY + εi,t.
(2)
The dependent variable, FUT PERFi,t, is the average accounting performance of firm i over the
four quarters following quarter t. I examine two measures of future accounting performance: FUT
ROAi,t and FUT CFOi,t, where FUT ROAi,t (FUT CFOi,t) is the average income before
extraordinary items (operating cash flow) divided by lagged total assets for firm i over the four
quarters following quarter t. The independent variable of interest is the RSCRIPTi,t variable which
is the conference call Q&A scripting measure defined in section 3.1. I expect a negative
13
association between FUT PERFi,t and RSCRIPTi,t if firms script Q&A responses when they
possess negative information about future firm performance.
I include several additional firm-specific variables to control for factors likely associated
with Q&A scripting and future firm performance. I first include the current value of PERFi,t to
control for persistence in the performance measures (Barber and Lyon 1996). PERFi,t is measured
as return on assets (ROAi,t) or cash flow from operations scaled by lagged total assets (CFOi,t) for
firm i in quarter t when FUT ROAi,t and FUT CFOi,t are the dependent variables, respectively.
Next, I include the natural logarithm of market value of equity (ln(MVEi,t)) to control for firm size
and expect larger firms have higher future accounting performance (Core et al. 1999).
I also
include the earnings surprise for firm i in quarter t (EARN SURPi,t) defined as IBES actual EPS
less analysts’ median consensus forecast prior to the conference call date divided by share price at
the end of the quarter and expect a negative coefficient consistent with Davis, Piger and Sedor
(2012).
I also include the standard deviation of earnings in the previous 16 quarters (EARN VOLi,t)
and the standard deviation of monthly stock returns over the previous twelve months (RET VOLi,t)
to control for firm risk. Consistent with prior research, I expect a negative association between
future performance and risk (Minton et al. 2002; Bowen et al. 2008; Core et al. 1999; Davis, Piger,
and Sedor 2012). I also control for the firm’s life cycle stage by including firm age (ln(AGEi,t))
defined as the natural logarithm of the number of years since the firm first appeared on Compustat
as of quarter t. I expect younger firms have lower future performance. I control for additional
factors potentially associated with my scripting measure: the percentage of institutional ownership
of the firm (INST OWNi,t), the natural logarithm of the number of analysts following the firm
during the quarter (ln(ANAL FOLLi,t)), and the stock turnover for the firm during the quarter
14
(TURNOVERi,t) defined as trading volume divided by the number of shares outstanding. I also
include an indicator variable equal to 1 if the firm provides earnings guidance for the next quarter’s
EPS on the conference call date and 0 otherwise (GUIDANCEi,t) to control for manager’s provision
of quantitative information during the call. I use guidance data from both First Call and IBES to
reduce issues associated with the completeness of the datasets (Chuk et al. 2012). I also control
for the direction of the surprise in the earnings guidance according to First Call and IBES by
defining a variable equal to 1 if the guidance qualifies as a positive earnings surprise, equal to 0 if
the guidance does not qualify as a surprise, and equal to -1 if the guidance qualifies as a negative
surprise (GUID SURPi,t). When the firm does not provide earnings guidance, GUID SURPi,t is set
equal to 0.
I next include several conference call specific variables. I first include conference call tone
(TONEi,t) defined as the number of positive words less the number of negative words in the call
divided by the total number of words in the call. The positive and negative word dictionaries are
obtained from Loughran and McDonald (2011). I expect a positive association between tone and
future performance if net optimistic language indicates positive information about future
performance. I next include the natural logarithm of the number of words spoken by the CEO
during the presentation session (ln(CEO WC PRESi,t)) and during the Q&A session of the call
(ln(CEO WC QAi,t)) to control for potential measurement error in the scripting measure if larger
word counts provide a more precise measurement of the differences in function words between the
presentation and Q&A sessions of the conference call.
Finally, I include year-quarter and industry (two-digit SIC code) indicator variables to
control for differences in Q&A scripting over time and across industries. I also cluster the standard
15
errors by firm due to likely serial correlation in the dependent and independent variables (Petersen,
2009).
3.3 Test of hypothesis two
To test whether scripted conference calls are associated with a negative stock market
reaction at the conference call date (Hypothesis 2), I estimate the following model similar to
Mayew and Venkatachalam (2012):
CC CARi,t =
δ0 + δ1 RSCRIPTi,t + δ2 EARN SURPi,t + δ3 ROAi,t + δ4 ln(MVEi,t) + δ5
BTMi,t + δ6 MOMi,t + δ7 RET VOLi,t + δ8 GUIDANCEi,t + δ9 GUID SURPi,t
+ δ10 TONEi,t + δ11 ln(CEO WC PRESi,t) + δ12 ln(CEO WC QAi,t) +
(3)
YEARQTR + INDUSTRY + εi,t.
The dependent variable is the size and book-to-market adjusted cumulative abnormal stock return
over the window [0,1] surrounding the earnings conference call date (CC CARi,t). The stock is
matched to one of the 25 size-BTM Fama French portfolios based on the market capitalization of
the firm at the end of June and the book value of equity of the last fiscal year end in the prior
calendar year divided by the market value of equity at the end of December of the prior year. 14
The independent variable of interest is the RSCRIPTi,t measure defined in section 3.1. I expect a
negative association between RSCRIPTi,t and CC CARi,t if investors interpret Q&A scripting as a
negative signal of future firm performance (Hypothesis 2).
I control for size, growth, and risk, which have been shown to be related to market returns
(Collins and Kothari 1989). I use the natural logarithm of market value of equity (ln(MVEi,t) as
the proxy for size, book-to-market (BTMi,t) as the proxy for growth, and return volatility (RET
VOLi,t) as the proxy for risk. In addition, I control for return momentum (MOMi,t) defined as the
The breakpoints and 25 portfolio returns are obtained from
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
14
Kenneth
French’s
website
at
16
cumulative abnormal return over the [-127, -2] window prior to the conference call date. I also
include the current period earnings surprise (EARN SURPi,t) and the current period return on assets
(ROAi,t) to control for the market reaction to current period earnings and expect a positive
coefficient on these variables. I also include the earnings guidance variables GUIDANCEi,t and
GUID SURPi,t to control for quantitative information provided by the firm about future
performance. I also include the conference call specific variables TONEi,t, ln(CEO WC PRESi,t),
and ln(CEO WC QAi,t) to control for alternative linguistic features of the conference call and for
potential measurement error in the scripting measure. Consistent with prior research, I expect a
positive association between conference call tone and the market reaction to the call. Finally, I
include year-quarter and industry (two-digit SIC code) indicator variables and cluster the standard
errors by firm.
4. Sample selection and data
I obtain a sample of earnings conference calls by first matching all non-financial firms on
Compustat with non-missing total assets between 2002 and 2011 to their corresponding unique
Factiva identifiers using the company name provided by Compustat.15 For the 11,702 unique
Compustat firms, I find Factiva identifiers for 5,099 firms. Using each firm’s unique identifier, I
then search Factiva’s FD Wire for earnings conference calls made between 2002 to 2011 and find
56,822 total calls for 3,475 unique firms.16 I remove 15,384 calls in which the CEO speaks less
than 200 words in either the presentation or Q&A session of the call.
Requiring financial
statement data from Compustat, IBES, and CRSP further reduces the sample by 5,142 calls, 1,370
15
In cases where the match is ambiguous, I check whether the city and state of the matched firm in Factiva matches
the city and state of the firm in Compustat.
16
Factiva contains different types of conference calls such as those discussing mergers and acquisitions. I focus only
on earnings-related conference calls. I filter out non-earnings related conference calls by requiring the term “earnings”
to be in the title of the call. I also require the conference call be made within 2 days of the earnings announcement.
17
calls, and 3,813 calls, respectively. The final sample consists of 30,773 earnings conference calls
for 2,384 unique firms with sufficient data to estimate the main empirical analyses.
Table 1 presents the means of the variables used in the empirical analysis for the full sample
and also for each quintile of the SCRIPTi,t measure. The final column in the table reports the test
statistic testing the difference between the fifth and first quintile. The mean of the scripting
measure (SCRIPTi,t) is 0.797 in the bottom quintile and 0.934 in the top quintile. The mean of
future return on assets (FUT ROAi,t) is 0.010 in the bottom scripting quintile and 0.005 in the top
quintile and the mean of future operating cash flows (FUT CFOi,t) is 0.015 in the bottom quintile
and 0.014 in the top quintile and the differences are statistically significant at the one percent level
providing preliminary evidence of a negative association between Q&A scripting and future
performance (Hypothesis 1).
Table 1 also reports a significant difference in the cumulative abnormal return at the
conference call date (CC CARi,t) between the top and bottom quintiles of the scripting measure (0.001 compared to 0.005) providing preliminary evidence that investors interpret scripting as a
signal that mangers possess negative information about future firm performance (Hypothesis 2).
The cumulative abnormal return in the 252 trading days following the conference call (FUT CARi,t)
shows no difference between the top and bottom quintiles, suggesting that investors understand
the implications of Q&A scripting and there is no drift. I also find analyst forecast revisions
following the conference call (FREVi,t+1) are more negative in the top quintile of the scripting
measure relative to the bottom quintile (-0.193 compared to -0.148). I also find that 19.9% of
firms in the top quintile of the scripting measure provide guidance for next quarter’s EPS
(GUIDANCEi,t) compared to 22.8% in the bottom. I do not, however, find a difference in analyst
forecast accuracy (ACCURACYi,t+1) between the top and bottom quintiles.
18
I also report the means of the control variables used in the empirical analysis. The
significant differences between the top and bottom quintiles for these variables underscore the
importance of including these variables in the empirical analysis to control for alternative
explanations. I specifically find that firms in the top quintile are larger with greater analyst
following and institutional ownership, have lower current period market and accounting
performance, have lower book-to-market ratios, have been listed on Compustat for less time, and
provide more negative forecasts of future EPS. I also find that firms with CEOs who speak more
words during both the presentation and Q&A sessions have more scripted conference calls which
can be attributable to two forces. First, when the firm does not wish to inadvertently disclose
information to outsiders, it may script longer presentations and responses to analysts’ questions to
allow for less time for multiple questions to be asked. Second, higher word counts allow a more
precise measurement of the scripting variable potentially creating a bias in the measure. Hence, I
include these two measures in each regression specification to control for this possibility.17
5. Results
5.1. Results for hypothesis one
Table 2 presents the results of estimating Equation 2. In Column 1 (2) the dependent
variable is FUT ROAi,t (FUT CFOi,t). The coefficient on RSCRIPTi,t is -0.003 in Column (1) and
-0.003 in Column (2) and both are significant at the one percent level. The coefficient estimates
suggest that relative to firms in the bottom decile of the scripting measure, firms in the top decile
have a 45 percent lower return on assets in the four quarters following the conference call (-
17
To further rule out the possibility that measurement error in the scripting measure is affecting my results, I reestimate the scripting measure holding the number of words constant across firms. I continue to find a highly positive
correlation between this alternative scripting measure and the total number of words spoken by the CEO during both
the presentation and Q&A sessions of the call, suggesting measurement error is not driving the large positive
association between call length and my scripting measure. The results of my empirical analyses are also robust to
using this alternative measure. See Section 6.1 for more detail.
19
0.003/0.0066 = -0.45) and 21 percent lower operating cash flows in the four quarters following the
conference call (-0.003/0.0141 = -0.21). These results suggest that firms script Q&A when future
accounting performance is poor and are consistent with my first hypothesis.
The control variables indicate that larger firms with more institutional ownership, lower
return volatility, and lower analyst following have higher future earnings and cash flows. I find
positive coefficients on the current period performance measures consistent with persistence in
performance. I also find that younger firms with higher stock turnover and lower earnings
volatility have higher future cash flows but that these variables are insignificant in the future
earnings regression. In addition, future earnings and cash flows are higher when firms provide
guidance and when the guidance is more positive. I also find that conference call tone loads
positively in both future performance regressions, suggesting that managers use positive tone when
future performance is high. I do not find a relation between future performance and the number
of words spoken by the CEO during the Q&A session, but lower future earnings when the
presentation session is longer.
5.2. Results for hypothesis two
I next estimate the relation between scripting and the market reaction at the time of the
conference call. Panel A of Table 3 presents the results of estimating Equation (3). The coefficient
on RSCRIPTi,t is -0.008 and significant at the one percent level in Column (1) without including
the control variables. After including the control variables in Column (2), the magnitude of the
coefficient drops to -0.003 but remains statistically significant at the one percent level. The
coefficient in Column (2) indicates that relative to firms in the bottom decile, firms in the top decile
of RSCRIPTi,t have 139 percent lower abnormal returns at the conference call date relative to the
mean of CC CARi,t (-0.003/0.00216 = -1.39). This result is consistent with investors interpreting
20
scripted calls as a negative signal of future performance and supports my second hypothesis. The
control variables indicate that larger and higher growth firms have lower conference call returns.
I also find a negative relation between the conference call return and return momentum. Firms
with more positive ROA and more positive earnings surprises also have higher abnormal returns.
I also find that firms with more positive earnings guidance on the day of the call have higher
abnormal returns on the day of the call, but that the decision to guide future earnings is negatively
associated with the abnormal return. In addition, firms with more positive conference call tone
have higher abnormal returns consistent with prior research. I also find that firms with longer
presentation sessions have lower abnormal returns.
I next examine whether scripted conference calls are associated with future abnormal
returns to determine whether investors over or under react to scripted calls at the conference call
date. Panel B reports the results of Equation (3) replacing CC CARi,t with FUT CARi,t, defined as
the abnormal return for the 252 trading days following the conference call using the window [2,
254]. In Column (1) I do not find a significant relation between RSCRIPTi,t and FUT CARi,t,
suggesting that the reaction at the conference call date does not reverse in future periods on
average, and hence, was not an overreaction. Instead, scripted calls provide investors with a signal
of future negative performance at the conference call date.
However, some firms are likely to script their Q&A responses for reasons unrelated to
future performance. For example, proprietary costs of inadvertent disclosure can induce some
firms to script their Q&A responses to avoid revealing information about the firm’s products. In
addition, managers who are less confident in their ability to respond to questions are likely to rely
on scripted responses to avoid tarnishing their reputational capital. For these firms, when future
performance materializes and the market’s negative prior assessment of performance is proved
21
inaccurate, the negative stock market response is likely to reverse. I test this conjecture by
including the interaction between RSCRIPTi,t and an indicator variable equal to 1 for below median
values of FUT ROAi,t (LOW FUT ROAi,t) as an additional control variable in the second column of
Panel B. I expect a positive coefficient on RSCRIPTi,t if returns reverse for firms with high
subsequent performance. In contrast, I expect the sum of the coefficients on the RSCRIPTi,t
measure and the interaction between RSCRIPTi,t and LOW FUT ROAi,t to be insignificant if returns
do not reverse for firms with poor subsequent performance. The results in Column (2) are
consistent with these expectations. The coefficient on RSCRIPTi,t is 0.032 and is significant at the
one percent level. In contrast, the sum of the coefficients on the RSCRIPTi,t measure and the
interaction between RSCRIPTi,t and LOW FUT ROAi,t is 0.005 and is insignificant.
Next, I corroborate the results in Panel A of Table 3 by examining revisions of analysts’
EPS forecasts for quarter t+1 following the conference call date. Specifically, I regress analyst
forecast revisions, FREVi,t+1, defined as the median analyst EPS forecast for quarter t+1 for all
forecasts made within 30 days following the conference call date less the median consensus
forecast of quarter t+1 directly prior to the conference call divided by price and multiplied by 100
on the scripting measure and other control variables.18 Table 4 presents the results and reports a
negative coefficient on RSCRIPTi,t of -0.10, which is significant at the one percent level. The
coefficient estimate suggests that moving from the bottom to the top decile of the RSCRIPTi,t
measure is associated with a 51 percent decrease in FREVi,t+1 relative to the mean of FREVi,t+1 (0.10/-0.195 = 0.51). This result is consistent with the abnormal returns tests and suggests that
analysts revise downward their forecasts of future earnings after scripted conference calls. Thus,
sophisticated investors (i.e., analysts) view conference calls as a negative signal of future firm
18
I multiply by 100 to be able to observe the coefficient on the scripting variable without reporting several decimal
places.
22
performance consistent with my second hypothesis. The control variables indicate that analysts
revise their forecasts upward following large current period earnings surprises and following calls
with positive disclosure tone. Analysts also revise their forecasts upward following positive
earnings guidance, but downward if the firm decides to guide earnings. I also find that analysts
revise their forecasts upward following calls with longer Q&A sessions.
Overall, I find evidence consistent with my hypotheses. These results suggest that firms
script Q&A responses when managers possess negative information about future firm
performance, that investors interpret scripted calls negatively.
6. Do firms provide more or less information during scripted conference calls?
The negative market reaction to scripted Q&A is consistent with the following two
alternative explanations: 1) investors interpret scripted responses as a negative signal of future
performance or 2) managers use scripted responses to provide additional information about the
negative expected performance. I attempt to distinguish these explanations in two ways. First, if
firms use scripted conference calls to provide additional information about future performance,
scripted calls are likely associated with a greater propensity to provide guidance about future
earnings. In contrast, if firms provide less information during scripted calls, I expect scripted calls
to be associated with a lower propensity to provide guidance about future earnings. This is a direct
measure of managers’ decisions to provide additional information during scripted earnings calls.
Second, if firms provide additional information during scripted earnings calls, market
participants are likely to have a richer information set to predict future firm performance. I focus
on analysts who aggregate data from firm, industry, and market sources to produce earnings
forecasts, stock recommendations, and other analyses to aid investors in establishing earnings
expectations for the firm (see, e.g., Brown and Rozeff, 1978; Givoly and Lakonishok, 1979; Brown
23
et al., 1987; Fried and Givoly, 1982; Asquith et al., 2005; Frankel et al., 2006). Prior research
suggests that conference calls are useful for analysts in establishing forecasts for future periods.
For example, Bowen et al. (2002) find that conference calls improve analysts’ forecasting ability,
and Mayew (2008) suggests that analysts benefit from their ability to ask questions of management
during the Q&A session of the call. If analysts have a richer information set following scripted
calls, I expect their forecasts to be more accurate. If, on the other hand, firms provide less
information during scripted calls, I expect analyst forecasts are less accurate following scripted
calls.
I test whether firms are less likely to provide earnings guidance when conference calls are
scripted by estimating the following model:
Pr(GUIDANCEi,t) =
β0 + β1 RSCRIPTi,t + β2 EARN SURPi,t + β2 ROAi,t + β4 ln(MVEi,t) +
β5 INSTOWNi,t + β6 ln(ANAL FOLLi,t) + β7 TURNOVERi,t + β8
EARN VOLi,t + β9 RET VOLi,t + β10 ln(AGEi,t) + β11 TONEi,t + β12
ln(CEO WC PRESi,t) + β13 ln(CEO WC QAi,t) + β14 MEET OR
BEATi,t + β15 DISPERSIONi,t + β16 TRENDi,t + εi,t.
(4)
GUIDANCEi,t is an indicator variable equal to 1 if the firm provides earnings guidance for the next
quarter’s EPS on the conference call date and 0 otherwise. As in earlier tests, I use guidance data
from both First Call and IBES to reduce issues associated with the completeness of the datasets
(Chuk et al. 2012). The independent variable of interest is the RSCRIPTi,t measure defined in
section 3.1. I expect a negative association between RSCRIPTi,t and GUIDANCEi,t if firms provide
less information about future earnings during scripted conference calls.
I control for the current period earnings surprise (EARN SURPi,t) and return on assets
(ROAi,t) and expect firms are less likely to issue guidance when current period performance is poor
24
(Rogers and Van Buskirk 2013). I control for analysts’ and investors’ demand for information by
including the percentage of shares held by institutional owners (INSTOWNi,t), share turnover
(TURNOVERi,t), and the number of analysts following the firm (ANAL FOLLi,t) and expect positive
coefficients on these variables. I also include the earnings and return volatility variables (EARN
VOLi,t and RET VOLi,t) to control for the firm’s uncertainty and age (AGEi,t) to control for the
firm’s life cycle stage. I also control for dispersion in analysts’ forecasts (DISPERSIONi,t) and
expect a negative coefficient (Rogers and Van Buskirk 2013). I also control for the proportion of
the previous four quarters that the firm has met or beat analysts’ expectations (MEET OR BEATi,t)
and expect a positive coefficient (Rogers and Van Buskirk 2013). I include the conference call
specific variables TONEi,t, CEO WC PRESi,t and CEO WC QAi,t to control for alternative
conference call features. Finally, I include a trend variable (TRENDi,t) equal to 1 if the first quarter
of 2002, equal to 2 in the second quarter of 2002, etc. to control for a trend in issuing forecasts
over time.
Table 5 reports the results of the logistic estimation of Equation 4. The negative and
significant (one percent level) coefficient on RSCRIPTi,t of -0.275 suggests that firms are less likely
to provide guidance when Q&A is scripted. The odds ratio suggests that firms in the top decile of
the scripting measure are 24.7 percent less likely to guide next quarter’s EPS than firms in the
bottom decile (odds ratio equals 0.753). The control variables indicate that younger firms with
more positive current earnings, higher institutional ownership, more analyst coverage, lower return
volatility, and more positive conference call tone are more likely to guide earnings. I also find that
firms that meet or beat analysts’ expectations more often are more likely to provide guidance.
Finally, firms are more likely to guide earnings when analyst forecast dispersion is lower.
25
I next test whether scripted conference calls provide more or less information for analysts
by estimating the following model:
ACCURACYi,t+1 = ρ0 + ρ1 RSCRIPTi,t + ρ2 EARN SURPi,t + ρ3 ROAi,t + ρ4 ln(MVEi,t) + ρ5
INSTOWNi,t + ρ6 ln(ANAL FOLLi,t) + ρ7 TURNOVERi,t + ρ8 EARN
VOLi,t + ρ9 RET VOLi,t + ρ10 ln(AGEi,t) + ρ11 GUIDANCEi,t + ρ12 GUID
SURPi,t + ρ13 TONEi,t + ρ14 ln(CEO WC PRESi,t) + ρ15 ln(CEO WC
QAi,t) + YEARQTR + INDUSTRY + εi,t.
(5)
ACCURACYi,t+1 is the accuracy of analysts’ forecast revisions following the conference call
defined as the absolute value of the IBES actual earnings per share for quarter t+1 less the median
EPS estimate for all analysts’ forecasts made within 30 days following the conference call
multiplied by negative one hundred and scaled by share price. The independent variable of interest
is the RSCRIPTi,t measure defined in section 3.1. I expect a negative association between
RSCRIPTi,t and ACCURACYi,t+1 if scripted conference calls are less informative for analysts.
I include several variables to control for alternative explanations. Following prior research
(e.g., Alford and Berger 1999; Lang and Lundholm 1996; Dichev and Tang 2009), I expect
analysts to be more accurate when following larger firms (ln(MVEi,t)) with high stock turnover
(TURNOVERi,t), low volatility (EARN VOLi,t and RET VOLi,t), positive earnings surprises (EARN
SURPi,t), and positive earnings (ROAi,t). I include analyst following (ln(ANAL FOLLi,t) to control
for the intensity of competition among analysts and expect greater competition increases analysts’
incentives to forecast accurately (Lys and Soo 1995).
I include institutional ownership
(INSTOWNi,t) to control for investors’ demand for information about the firm and expect higher
accuracy for firms with higher ownership by institutions. I also include firm age (ln(AGEi,t)) to
control for the firm’s life cycle stage and expect analysts’ forecasts are more accurate for older
26
firms with more established operations.
I also include the earnings guidance variables
GUIDANCEi,t and GUID SURPi,t to control for quantitative information provided by the firm about
future performance and expect positive coefficients on these variables. Finally, I include the
conference call specific variables as in previous tests (TONEi,t, ln(CEO WC PRESi,t), and ln(CEO
WC QAi,t)) to control for alternative linguistic properties of the disclosure and for measurement
error in the scripting measure. As in previous tests, I include year and industry fixed effects and
cluster the standard errors by firm.
Table 6 reports the results of estimating Equation (5). I find a negative and significant (ten
percent level) coefficient on RSCRIPTi,t of -0.049 suggesting that moving from the bottom to the
top decile of the RSCRIPTi,t variable reduces analyst forecast accuracy by 8.1 percent (0.049/0.602 = -0.081). These results suggest that analysts gain less information from scripted
conference calls and their forecasting accuracy suffers as a result.
The control variables indicate that analysts’ forecasts are more accurate for larger firms
with higher current period earnings, more positive current period earnings surprises, higher
institutional ownership, lower turnover, and lower returns and earnings volatility consistent with
my expectations. Interestingly, I find that younger firms have more accurate forecasts. Analysts
may exert greater effort to accurately predict earnings for these firms. Analyst forecasts are also
more accurate when the firm provides guidance for the next quarter’s earnings. In addition, I find
analyst forecasts are more accurate following conference calls with net positive tone and with
longer Q&A sessions, suggesting that firms provide more information when management is
optimistic about future performance.
Overall, these results suggest that firms provide less, not more information when Q&A is
scripted. The negative market reaction to scripting is therefore more consistent with scripted Q&A
27
serving as a signal of future firm performance rather than the firm providing additional information
when Q&A is scripted.
7. Sensitivity and Additional Analyses
7.1 Sensitivity
I perform several robustness tests to examine the sensitivity of the analyses. First, in the
main empirical analysis, I use future accounting performance as a proxy for the information
managers possess at the time of the conference call. However, because future accounting
performance is highly correlated with current performance, the coefficient on the scripting measure
may be influenced by current performance.
I control for this by including current period
performance in Equation (2). To provide further support of the negative association between
scripting and future performance, I examine whether the results are robust to using unexpected
earnings as the proxy for future performance. I use two measures of unexpected future earnings.
The first is the quarterly change in earnings before extraordinary items from quarter t to quarter
t+1 scaled by total assets in quarter t assuming a random walk process (UE EARN (RW)i,t+1). The
second is the actual earnings per share in quarter t+1 less the median consensus analyst forecast
of earnings per share for quarter t+1 for all forecasts made prior to the conference call date divided
by share price and multiplied by 100 (UE EARN (ANAL)i,t+1).
Table 7 presents the results of estimating the relation between unexpected future earnings
and Q&A scripting including the control variables used in previous tests. I find a negative and
significant (one percent level) coefficient on RSCRIPTi,t of -0.002 in Column (1) when UE EARN
(RW)i,t+1 is the dependent variable. I also find a negative and significant (five percent level)
coefficient on RSCRIPTi,t of -0.112 in Column (2) when UE EARN (ANAL)i,t+1 is the dependent
variable.
These results provide additional evidence that firms script conference call Q&A
28
responses prior to periods of low performance. The control variables indicate that larger firms
with higher institutional ownership, lower analyst following, more positive earnings guidance, and
more optimistic tone have more positive unexpected future earnings.
Second, Table 3 reports the relation between Q&A scripting and the 2-day abnormal return
in the [0,1] window surrounding the conference call date. However, firms often hold their
conference calls shortly following the earnings announcement press release (Matsumoto et al.
2011). If firms script their Q&A when the current earnings surprise is negative, the 2-day return
may reflect the negative surprise in current earnings and not investors’ reaction to Q&A scripting.
For this reason, in Table 3, I explicitly control for the surprise in earnings. To further rule out the
possibility that my scripting measure is capturing a current period earnings surprise effect, I collect
conference call start times for a sub-sample of calls and use TAQ data to examine the market
reaction prior to, during, and following the conference call. Specifically, for each of the conference
calls in my sample, I search the website seekingalpha.com for the call start times. Seeking Alpha
collects start times for calls beginning in 2006. For the 30,773 calls in my sample, I obtain the
start times for 10,152 calls. I restrict my focus to calls held during trading hours with TAQ data
(4,168 calls).
I compute start and end times for the presentation and Q&A sessions of the call by applying
Matsumoto et al. (2011)’s computed words spoken per minute (160 for the presentation and 157
for the Q&A session) to the calls in my sample. Matsumoto et al. (2011) also assume the
presentation session starts 116 seconds after the scheduled start time to allow for introductory
remarks, and the Q&A session starts 28 seconds after the end of the presentation to allow time for
operator instructions. I then obtain price data from TAQ for the following times on the day of the
call: (1) the opening price for the trading day, (2) the prices at the start and end of the presentation
29
session, (3) the prices at the start and end of the Q&A session, and (4) the price at the close of the
trading day. Using these prices, I then compute the stock return for the presentation and Q&A
sessions (RET(PRES)i,t and RET(QA)i,t, respectively) and also for the periods from market open to the
start of the presentation session (RET(PRE)i,t) and from the end of the Q&A session to the market
close (RET(POST)i,t). To control for potential patterns in intra-day trading, I subtract the median
return during the same time period (i.e., during the PRE, POST, PRES, or QA periods) on all nonconference call days during the quarter to obtain period-specific measures of abnormal returns
(ABN RET(PERIOD)i,t).
Table 8 presents the results of re-estimating Equation (3) replacing the dependent variable
with the abnormal returns during each of the periods identified above. I find a negative and
significant (5 percent level) coefficient on RSCRIPTi,t of -0.005 in the ABN RET(PRE)i,t regression
suggesting that a portion of the negative association between RSCRIPTi,t and the 2-day return
surrounding the conference call date is due to the current period earnings surprise. However, I
also find a negative and significant (10 percent level) coefficient on RSCRIPTi,t of -0.003 in the
ABN RET(POST)i,t regression suggesting that investors respond to Q&A scripting apart from the
current period earnings surprise effect. I also note that the coefficient on EARN SURPi,t is positive
and significant (1 percent level) in the ABN RET(PRE)i,t regression but insignificant in the ABN
RET(POST)i,t regression, suggesting that the earnings announcement effect is concentrated in the
PRE period, not in the POST period. Interestingly, I find insignificant coefficients on RSCRIPTi,t
in the ABN RET(PRES)i,t and ABN RET(QA)i,t regressions, suggesting that investors incorporate the
scripting signal into prices with a delay.
Third, my scripting measure compares words spoken by the CEO during the presentation
and Q&A sessions of the conference call. However, for some firms, the CFO plays a more
30
predominant role in the conference call. I therefore examine whether the results are robust to using
the spokesman executive to measure Q&A scripting, where the spokesman executive is defined as
the CEO or CFO who speaks for the longest portion of the conference call. The results of all tests
remained qualitatively and quantitatively similar using this alternative measure.
Fourth, prior research suggests managers exhibit distinctive styles on their disclosure
policies (Bamber, et al. 2010; Zhang, et al. 2012). If managerial characteristics associated with
scripting are also associated with the firm’s future performance or the market reaction to the
conference call, then the scripting measure may be biased. For example, if low ability managers
are more likely to script their calls and also have poor future performance, ability represents a
correlated omitted variable. To ensure the results are not driven by an unobservable manager
characteristic, I include manager fixed effects in the models and find similar results (untabulated)
for the main analyses with the following exceptions. The coefficient on RSCRIPTi,t loses statistical
significance in the future cash flows and analyst forecast accuracy regressions. I also find the
return reversal for firms with positive ex post accounting performance loses significance.
However, the findings for future ROA and the market reaction to the conference call remain
unchanged providing additional confidence that scripting serves as a negative signal of future
performance apart from an unobservable managerial characteristic.
Finally, as discussed in Section 4, the scripting measure may be subject to greater
measurement error when the CEO speaks fewer words during the call. This may especially be true
given the large positive association between the scripting measure and the number of words spoken
by the CEO during both sessions of the call (see Table 1). To rule out the possibility that
measurement error in the scripting measure is affecting my results, I re-estimate the scripting
measure holding the number of words constant across firms. Specifically, I randomly select 600
31
words spoken by the CEO during both the presentation and the Q&A sessions of the call and reestimate the scripting measure using the function word count vectors of these equal-length word
lists. I continue to find a highly positive correlation between this alternative scripting measure and
the total number of words spoken by the CEO during both the presentation and Q&A sessions of
the call, suggesting measurement error is not driving the large positive association between call
length and my scripting measure. I also find qualitatively and quantitatively similar results for all
tests using this alternative measure of scripting.
7.2 Class action lawsuits
I next examine scripting in the litigation setting to verify that firms script conference calls
during periods prior to negative events and to further validate the scripting measure. I specifically
test whether firms script conference calls during periods prior to and following class action
lawsuits by estimating the following model:
PR(LITIGi,t) =
β0 + β1 RSCRIPTi,t + CONTROLS + εi,t.
(5)
The dependent variable is an indicator variable equal to 1 for periods surrounding class action
lawsuit filing dates (LITIGi,t) and 0 otherwise. I examine four periods surrounding each event and
append a label to the dependent variable of PRE2, PRE1, POST1, or POST2 depending on the
period examined. PRE2 indicates the conference call date is between one and two years prior to
the filing date, PRE1 indicates the conference call date is within one year prior to the filing date,
POST1 indicates the conference call date is within one year after to the filing date, and POST2
indicates the conference call date is between one and two years after to the filing date. I use class
action lawsuits provided by the Stanford Law School Securities Class Action Clearinghouse. The
independent variable of interest is the RSCRIPTi,t variable which is the Q&A scripting measure
defined in section 3.1. I expect a positive association between RSCRIPTi,t and LITIG PRE1i,t if
32
firms are more likely to script prior to litigation events. Firms may also continue to script calls
following litigation, possibly to avoid inadvertently providing additional information that can be
used to build a case against the firm (Rogers and Van Buskirk 2009). If so, I also expect a positive
association between RSCRIPTi,t and LITIG POST1i,t. In contrast, I do not expect to find a relation
(or a less positive relation) between RSCRIPTi,t and the PRE2 and POST2 variables as these
periods are less influenced by the litigation event.
I include similar control variables as in the prior tests and estimate the regressions using
the logistic method. Standard errors are clustered by firm. Table 9 presents the results and reports
a positive and significant (one percent level) coefficient on RSCRIPTi,t when LITIGATION PRE1i,t
is the dependent variable, suggesting that firms script Q&A responses prior to class action lawsuits.
The odds ratio suggests that firms in the top decile of the scripting measure are 68 percent more
likely to receive a class action lawsuit within one year than firms in the bottom decile (odds ratio
equals 1.68). I also find a positive and significant (five percent level) coefficient on RSCRIPTi,t
when LITIGATION POST1i,t is the dependent variable, suggesting that firms continue to script
their calls following lawsuits consistent with Rogers and Van Buskirk (2009) who suggest that
firms reduce disclosure following class action lawsuits. I do not find evidence that firms script
their calls during periods greater than one year before or after the class action lawsuit filing date
as evidenced by the insignificant coefficients on LITIGATION PRE2i,t and LITIGATION POST2i,t.
As an additional test, I restrict my focus to firms that receive class action lawsuits and
examine how scripting changes in the periods surrounding the class action filing date. For each
class action filing date, I compute the average value of RSCRIPTi,t for all conference calls held in
the following yearly windows surrounding the event: [-3, 2], [-2, -1], [-1, 0], [0, 1], [1, 2], and [2,
3]. Figure 3 presents the results. I find a large increase in the RSCRIPTi,t measure from the [-3, -
33
2] window to the [-2, -1] window. In addition, the RSCRIPTi,t measure is highest in the window
[-1, 0] suggesting that scripting is the most prominent in the period immediately prior to receiving
a class action lawsuit. Following the class action filing date, the RSCRIPTi,t measure declines.
These results suggest that firms that receive class action lawsuits increase scripting prior to the
filing date, possibly to avoid providing information useful in building a case against the firm.
8. Conclusion
This study examines whether market participants gain information about future firm
performance by identifying whether firms script responses to questions received during earnings
conference calls with analysts and investors. I provide evidence that firms script responses to
questions received during earnings conference calls prior to periods of poor accounting
performance and that investors interpret scripted Q&A responses negatively. In particular, I find
a negative association between my measure of Q&A scripting and future return on assets and future
cash flows from operations in the four quarters following the conference call. I further find a
negative association between the abnormal return at the time of the conference call and my
measure of scripting. I argue that firms script Q&A responses when future performance is poor to
avoid inadvertently providing information that can be useful in building a lawsuit against the firm.
Finally, I provide evidence that scripted calls are less informative for market participants.
In particular, firms are less likely to provide guidance for next quarter’s earnings when calls are
scripted and analyst forecasts are less accurate following scripted calls suggesting that the negative
market reaction to scripted calls is due to the signal it provides about future performance and not
due to the greater levels of information provided in the call about the future performance.
This paper contributes to the literature that examines the linguistic features of conference
call transcripts to extract information about future firm performance. While prior research finds
34
that conference call tone is associated with the market reaction to the call (Davis, Ge, Matsumoto,
and Zhang 2012), I find that scripted calls also inform the market about information management
possesses. This paper also contributes to the literature that examines whether conference calls
provide material information to the market. While prior research suggests that investors and
analysts benefit from information provided during conference calls, I find that firms with poor
future performance use scripting to avoid inadvertently disclosing information to the market. My
results are of potential interest to market participants who rely on information provided by firms
during earnings conference calls.
35
REFERENCES
Alford, A. W., and P. G. Berger. 1999. A Simultaneous Equations Analysis of Forecast
Accuracy, Analyst Following, and Trading Volume. Journal of Accounting, Auditing &
Finance 14 (3): 219–240.
Asquith, P., M. B. Mikhail, and A. S. Au. 2005. Information content of equity analyst reports.
Journal of Financial Economics 75 (2): 245–282.
Baginski, S. P., J. M. Hassell, and M. D. Kimbrough. 2002. The Effect of Legal Environment on
Voluntary Disclosure: Evidence from Management Earnings Forecasts Issued in U.S. and
Canadian Markets. The Accounting Review 77 (1): 25–50.
Bamber, L. S., J. (Xuefeng) Jiang, and I. Y. Wang. 2010. What’s My Style? The Influence of
Top Managers on Voluntary Corporate Financial Disclosure. The Accounting Review 85
(4): 1131–1162.
Barber, B. M., and J. D. Lyon. 1996. Detecting abnormal operating performance: The empirical
power and specification of test statistics. Journal of Financial Economics 41 (3): 359–
399.
Blau, B. M., J. DeLisle, and S. M. Price. 2012. Costly Talk in Earnings Conference Calls and
Short Selling. Working paper.
Bowen, R. M., A. K. Davis, and D. A. Matsumoto. 2002. Do Conference Calls Affect Analysts’
Forecasts? The Accounting Review 77 (2): 285–316.
Bowen, R. M., S. Rajgopal, and M. Venkatachalam. 2008. Accounting Discretion, Corporate
Governance, and Firm Performance. Contemporary Accounting Research 25 (2): 351–
405.
Brown, L. D., R. L. Hagerman, P. A. Griffin, and M. E. Zmijewski. 1987. An evaluation of
alternative proxies for the market’s assessment of unexpected earnings. Journal of
Accounting and Economics 9 (2): 159–193.
Brown, L. D., and M. S. Rozeff. 1978. The Superiority of Analyst Forecasts as Measures of
Expectations: Evidence from Earnings. The Journal of Finance 33 (1): 1–16.
Brown, S., S. A. Hillegeist, and K. Lo. 2004. Conference calls and information asymmetry.
Journal of Accounting and Economics 37 (3): 343–366.
Brown, S. V., and J. W. Tucker. 2011. Large-Sample Evidence on Firms’ Year-over-Year
MD&A Modifications. Journal of Accounting Research 49 (2): 309–346.
Burrows, J. F. 1987. Word patterns and story shapes: The statistical analysis of narrative style. 2
(Literary and Linguistics Computing): 61–70.
Bushee, B. J., D. A. Matsumoto, and G. S. Miller. 2003. Open versus closed conference calls: the
determinants and effects of broadening access to disclosure. Journal of Accounting and
Economics 34 (1–3): 149–180.
———. 2004. Managerial and Investor Responses to Disclosure Regulation: The Case of Reg
FD and Conference Calls. The Accounting Review 79 (3): 617–643.
Chuk, E., D. Matsumoto, and G. S. Miller. 2013. Assessing methods of identifying management
forecasts: CIG vs. researcher collected. Journal of Accounting and Economics 55 (1): 23–
42.
Collins, D. W., and S. P. Kothari. 1989. An analysis of intertemporal and cross-sectional
determinants of earnings response coefficients. Journal of Accounting and Economics 11
(2–3): 143–181.
Core, J. E., R. W. Holthausen, and D. F. Larcker. 1999. Corporate governance, chief executive
36
officer compensation, and firm performance. Journal of Financial Economics 51 (3):
371–406.
Cutler, J., A. Davis, and K. Peterson. 2013. Voluntary Disclosure and the Outcome of Securities
Litigation. Working paper.
Davis, A. K., W. Ge, D. A. Matsumoto, and J. L. Zhang. 2012. The Effect of Managerial “Style”
on the Tone of Earnings Conference Calls. Working paper.
Davis, A. K., J. M. Piger, and L. M. Sedor. 2012. Beyond the Numbers: Measuring the
Information Content of Earnings Press Release Language. Contemporary Accounting
Research 29 (3): 845–868.
Davis, A. K., and I. Tama-Sweet. 2012. Managers’ Use of Language Across Alternative
Disclosure Outlets: Earnings Press Releases versus MD&A. Contemporary Accounting
Research 29 (3): 804–837.
Dichev, I. D., and V. W. Tang. 2009. Earnings volatility and earnings predictability. Journal of
Accounting and Economics 47 (1–2): 160–181.
Frankel, R., M. Johnson, and D. J. Skinner. 1999. An Empirical Examination of Conference
Calls as a Voluntary Disclosure Medium. Journal of Accounting Research 37 (1): 133–
150.
Frankel, R., S. P. Kothari, and J. Weber. 2006. Determinants of the informativeness of analyst
research. Journal of Accounting and Economics 41 (1–2): 29–54.
Fried, D., and D. Givoly. 1982. Financial analysts’ forecasts of earnings: A better surrogate for
market expectations. Journal of Accounting and Economics 4 (2): 85–107.
Givoly, D., and J. Lakonishok. 1979. The information content of financial analysts’ forecasts of
earnings: Some evidence on semi-strong inefficiency. Journal of Accounting and
Economics 1 (3): 165–185.
Hollander, S., M. Pronk, and E. Roelofsen. 2010. Does Silence Speak? An Empirical Analysis of
Disclosure Choices During Conference Calls. Journal of Accounting Research 48 (3):
531–563.
Kasznik, R., and B. Lev. 1995. To Warn or Not to Warn: Management Disclosures in the Face of
an Earnings Surprise. The Accounting Review 70 (1): 113–134.
Kimbrough, M. D. 2005. The Effect of Conference Calls on Analyst and Market Underreaction
to Earnings Announcements. The Accounting Review 80 (1): 189–219.
Koppel, M., J. Schler, and S. Argamon. 2009. Computational methods in authorship attribution.
Journal of the American Society for Information Science and Technology 60 (1): 9–26.
Lang, M. H., and R. J. Lundholm. 1996. Corporate Disclosure Policy and Analyst Behavior. The
Accounting Review 71 (4): 467–492.
Lansford, B., J. Lee, and J. W. Tucker. 2009. Disclosure of Management Guidance in
Conference Calls: Materiality, Determinants, and Consequences. Working paper.
Larcker, D. F., and A. A. Zakolyukina. 2012. Detecting Deceptive Discussions in Conference
Calls. Journal of Accounting Research 50 (2): 495–540.
Lehavy, R., F. Li, and K. Merkley. 2011. The Effect of Annual Report Readability on Analyst
Following and the Properties of Their Earnings Forecasts. The Accounting Review 86 (3):
1087–1115.
Li, F. 2008. Annual report readability, current earnings, and earnings persistence. Journal of
Accounting and Economics 45 (2–3): 221–247.
———. 2010. The Information Content of Forward-Looking Statements in Corporate Filings—
A Naïve Bayesian Machine Learning Approach. Journal of Accounting Research 48 (5):
37
1049–1102.
Loughran, T., and B. Mcdonald. 2011. When Is a Liability Not a Liability? Textual Analysis,
Dictionaries, and 10-Ks. The Journal of Finance 66 (1): 35–65.
Lys, T., and L. G. Soo. 1995. Analysts’ Forecast Precision as a Response to Competition.
Journal of Accounting, Auditing & Finance 10 (4): 751–765.
Matsumoto, D., M. Pronk, and E. Roelofsen. 2011. What Makes Conference Calls Useful? The
Information Content of Managers’ Presentations and Analysts’ Discussion Sessions. The
Accounting Review 86 (4): 1383–1414.
Mayew, W. J. 2008. Evidence of Management Discrimination Among Analysts during Earnings
Conference Calls. Journal of Accounting Research 46 (3): 627–659.
Mayew, W. J., and M. Venkatachalam. 2012. The Power of Voice: Managerial Affective States
and Future Firm Performance. The Journal of Finance 67 (1): 1–43.
Minton, B. A., C. M. Schrand, and B. R. Walther. 2002. The Role of Volatility in Forecasting.
Review of Accounting Studies 7 (2-3): 195–215.
Mosteller, F. 2010. The Pleasures of Statistics: The Autobiography of Frederick Mosteller. New
York, NY: Springer.
Mosteller, F., and D. L. Wallace. 1963. Inference in an Authorship Problem. Journal of the
American Statistical Association 58 (302): 275–309.
Petersen, M. A. 2009. Estimating Standard Errors in Finance Panel Data Sets: Comparing
Approaches. Review of Financial Studies 22 (1): 435–480.
Price, S. M., J. S. Doran, D. R. Peterson, and B. A. Bliss. 2012. Earnings conference calls and
stock returns: The incremental informativeness of textual tone. Journal of Banking &
Finance 36 (4): 992–1011.
Rogers, J. L., and A. Van Buskirk. 2009. Shareholder litigation and changes in disclosure
behavior. Journal of Accounting and Economics 47 (1–2): 136–156.
———. 2013. Bundled forecasts in empirical accounting research. Journal of Accounting and
Economics 55 (1): 43–65.
Rogers, J. L., A. Van Buskirk, and S. L. C. Zechman. 2011. Disclosure Tone and Shareholder
Litigation. The Accounting Review 86 (6): 2155–2183.
Skinner, D. J. 1994. Why Firms Voluntarily Disclose Bad News. Journal of Accounting
Research 32 (1): 38–60.
Stamatatos, E. 2009. A survey of modern authorship attribution methods. Journal of the
American Society for Information Science and Technology 60 (3): 538–556.
Tasker, S. C. 1998. Bridging the Information Gap: Quarterly Conference Calls as a Medium for
Voluntary Disclosure. Review of Accounting Studies 3 (1-2): 137–167.
Tetlock, P. C., M. Saar-Tsechansky, and S. Macskassy. 2008. More Than Words: Quantifying
Language to Measure Firms’ Fundamentals. The Journal of Finance 63 (3): 1437–1467.
Zhang, J. L., A. K. Davis, W. Ge, and D. A. Matsumoto. 2012. The Effect of Managerial “Style”
on the Tone of Earnings Conference Calls. Working paper.
38
Appendix A. Function words with ranks by usage in conference calls
Articles
the
a
an
Rank
1
8
34
Pronouns
we
our
us
ourselves
ours
i
me
my
mine
myself
you
your
they
their
theyll
theyd
him
her
she
5
9
31
120
131
13
61
76
140
153
18
65
39
56
137
175
132
144
148
Impersonal
Pronouns
it
its
those
anyone
no one
nobody
19
32
45
146
156
160
Prepositions
to
of
in
for
as
on
with
at
from
about
by
up
over
than
into
like
per
down
through
during
before
around
versus
between
past
under
off
within
after
across
excluding
until
along
beyond
Rank
2
4
6
11
12
14
17
22
26
28
29
35
38
42
48
49
50
52
55
57
68
71
72
74
75
78
79
80
81
82
88
96
102
103
Prepositions
(cont'd)
above
against
without
plus
below
despite
towards
upon
following
outside
regarding
near
behind
toward
among
inside
considering
minus
except
onto
via
save
round
concerning
unlike
opposite
besides
underneath
aboard
beneath
amid
beside
Rank
104
105
106
107
108
110
111
112
113
114
115
118
119
122
124
128
129
134
135
138
147
151
154
155
157
169
170
174
182
183
184
185
Conjunctions
and
that
so
but
or
what
now
also
if
where
because
when
then
next
still
how
while
who
further
however
so that
since
of course
yet
why
although
in fact
whether
once
finally
though
provided
for example
therefore
Rank
3
7
23
27
30
36
37
43
46
51
53
54
58
59
60
62
64
77
83
84
85
86
91
92
94
95
97
98
99
100
101
109
116
117
Conjunctions
(cont'd)
so far
even though
thus
otherwise
instead
unless
accordingly
for instance
on the other hand
as long as
even if
anyway
nor
indeed
whereas
consequently
similarly
furthermore
whenever
nevertheless
nonetheless
as if
namely
wherever
likewise
meanwhile
hence
moreover
till
as though
provided that
until now
incidentally
on the contrary
Rank
121
123
125
127
130
133
136
139
141
142
143
145
149
150
152
158
159
161
162
163
164
165
166
167
168
171
172
173
176
177
179
180
181
186
Auxiliary
Verbs
is
are
have
were
be
will
was
would
do
has
had
can
did
being
may
could
should
doing
having
does
might
am
must
shall
Rank
10
15
16
20
21
24
25
33
40
41
44
47
63
66
67
69
70
73
87
89
90
93
126
178
39
Appendix B. Variable Definitions
Variable
Definition
The stock return from end of the Q&A session of the conference call
to the close of the market on the day of the conference call for firm i
ABN RET(POST)i,t
in quarter t less the median return during the same time period for
firm i on all non-conference call days during quarter t.
The stock return from the market opening on the day of the conference
call to the start of the presentation session of the conference call for
ABN RET(PRE)i,t
firm i in quarter t less the median return during the same time period
for firm i on all non-conference call days during quarter t.
The stock return from the start to the end of the presentation session
of the conference call for firm i in quarter t less the median return
ABN RET(PRES)i,t
during the same time period for firm i on all non-conference call days
during quarter t.
The stock return from the start to the end of the Q&A session of the
conference call for firm i in quarter t less the median return during the
ABN RET(QA)i,t
same time period for firm i on all non-conference call days during
quarter t.
The absolute value of IBES actual EPS in quarter t+1 less analysts'
median consensus forecast of quarter t+1 EPS for all forecasts made
ACCURACYi,t+1
within 30 days after the quarter t conference call multiplied by -100
and divided by price for firm i in quarter t.
The number of years from the time the firm first appears in the
AGEi,t
Compustat database to the fiscal quarter end date for firm i in quarter
t.
ANAL FOLLi,t
The number of analysts following firm i in quarter t.
The book value of equity divided by the market value of equity for
BTMi,t
firm i in quarter t.
The buy and hold return over the window [0,1] surrounding the
earnings conference call date for firm i in quarter t less the size and
book-to-market matched portfolio over the same window. The stock
is matched to one of the 25 size-BTM Fama French portfolios based
CC CARi,t
on the market capitalization of the firm at the end of June and the book
value of equity of the last fiscal year end in the prior calendar year
divided by the market value of equity at the end of December of the
prior year.
The number of words spoken by the chief executive officer during the
CEO WC PRESi,t
presentation session of the earnings conference call for firm i in
quarter t.
The number of words spoken by the chief executive officer during the
CEO WC QAi,t
question and answer session of the earnings conference call for firm i
in quarter t.
Operating cash flows divided by lagged total assets for firm i in
CFOi,t
quarter t.
40
DISPERSIONi,t
EARN SURPi,t
EARN VOLi,t
FREVi,t+1
FUT CARi,t
FUT CFOi,t
FUT ROAi,t
GUIDANCEi,t
GUID SURPi,t
INSTOWNi,t
LITIGATION POST1i,t
LITIGATION POST2i,t
LITIGATION PRE1i,t
LITIGATION PRE2i,t
The standard deviation of analyst forecasts of EPS for firm i in quarter
t made prior to the conference call date.
IBES actual EPS less the latest analysts' median consensus EPS
forecast prior to the earnings announcement divided by price for firm
i in quarter t.
The standard deviation of earnings before extraordinary for the
current and prior 15 quarters for firm i in quarter t.
The median analyst EPS forecast for quarter t+1 for all forecasts
made within 30 days following the conference call date less the
median consensus forecast of quarter t+1 directly prior to the
conference call divided by price and multiplied by 100.
The buy and hold return over the window [2,254] surrounding the
earnings conference call date for firm i in quarter t less the size and
book-to-market matched portfolio over the same window. The stock
is matched to one of the 25 size-BTM Fama French portfolios based
on the market capitalization of the firm at the end of June and the book
value of equity of the last fiscal year end in the prior calendar year
divided by the market value of equity at the end of December of the
prior year.
Average operating cash flows divided by lagged total assets for the
four quarters following quarter t for firm i.
Average income before extraordinary items divided by lagged total
assets for the four quarters following quarter t for firm i.
An indicator variable equal to 1 if firm i provides earnings guidance
for quarter t+1 on the day of the conference call at time t and 0
otherwise.
The surprise in managers' guidance of next quarter's EPS on the day
of the conference call equal to 1 if the guidance qualifies as a positive
surprise, equal to 0 if the guidance does not qualify as a surprise, and
equal to -1 if the guidance qualifies as a negative surprise according
to First Call or IBES. If no guidance is given, the variable is set equal
to 0.
The number of shares held by institutional investors divided by the
number of shares outstanding for firm i in quarter t.
An indicator variable equal to 1 if firm i received a class action law
suit within one year prior to the conference call for quarter t and 0
otherwise.
An indicator variable equal to 1 if firm i received a class action law
suit between one and two years prior to the conference call for quarter
t and 0 otherwise.
An indicator variable equal to 1 if firm i receives a class action law
suit within one year following the conference call for quarter t and 0
otherwise.
An indicator variable equal to 1 if firm i receives a class action law
suit between one and two years following the conference call for
quarter t and 0 otherwise.
41
MEET OR BEATi,t
MOMi,t
MVEi,t
RET VOLi,t
ROAi,t
RSCRIPTi,t
SCRIPTi,t
TONEi,t
TRENDi,t
TURNOVERi,t
UE EARN (ANAL)i,t+1
UE EARN (RW)i,t+1
The proportion of the previous four quarters as of time t in which firm
i meets or beats analysts' consensus forecast estimates of EPS.
The buy and hold return over the window [-127,-2] prior to the
earnings conference call date for firm i in quarter t less the size and
book-to-market matched portfolio over the same window. The stock
is matched to one of the 25 size-BTM Fama French portfolios based
on the market capitalization of the firm at the end of June and the book
value of equity of the last fiscal year end in the prior calendar year
divided by the market value of equity at the end of December of the
prior year.
Stock price multiplied by the number of common shares outstanding
for firm i in quarter t.
The standard deviation of monthly stock returns for the previous 12
months for firm i in quarter t.
Income before extraordinary items divided by lagged total assets for
firm i in quarter t.
The decile ranking (0 to 9) of SCRIPTi,t divided by 9.
Cosine similarity score of vectors vQA and vPRES, where vQA (vPRES) is
a vector of word counts for the list of function words in Appendix A
for the CEO during the Q&A (presentation) session of the firm i's
earnings conference call in quarter t. The cosine similarity score is
calculated as the dot product of vectors vQA and vPRES divided by the
product of the magnitude of vectors vQA and vPRES.
The number of positive words spoken during the conference call less
the number of negative words spoken during the conference call
divided by the total number of words spoken during the conference
call for firm i in quarter t. The dictionary of positive and negative
words is taken from Loughran and McDonald (2011).
A variable equal to 1 in the first quarter of 2002, equal to 2 in the
second quarter of 2002, etc.
Trading volume for firm i in quarter t divided by the number of shares
outstanding.
IBES actual EPS for firm i in quarter t+1 less the median analyst
consensus EPS forecast for firm i in quarter t+1 made prior to the
conference call date divided by share price at the end of quarter t and
multiplied by 100.
Income before extraordinary items for firm i in quarter t+1 less
income before extraordinary items in quarter t divided by total assets
in quarter t.
42
FIGURE 1. PE RCE NTAGE O F FIRMS O N CO MPUS TAT
WIT H AT L E AS T O NE E ARNINGS CO NFE RE NCE CAL L
DURING T H E YE AR
80%
PERCENTAGE OF FIRMS
70%
60%
50%
40%
30%
20%
10%
0%
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
YEAR
All Compustat
All Compustat with Anal Foll
Figure 1. This figure presents the annual percentage of firms on Compustat with at least one earnings
conference call during the year. The figure includes the percentage for all firms on Compustat and for
firms on Compustat with analyst following.
43
Figure 2. This figure presents the cumulative percentage of firms in ranks that compare the cosine similarity
of the firm’s conference call during a quarter to its own combined conference calls in all other quarters
during the sample period relative to the combined conference calls for all quarters of nine randomly-selected
firms. A ranking of 1 (10) indicates that the firm’s own conference call sessions are most (least) similar
relative to the conference call sessions of the nine randomly selected firms. The PRES to PRES line
compares the presentation session during the quarter to the presentation sessions of the firm and to the
randomly-selected firms in all other quarters. The Q&A to Q&A line compares the Q&A session during
the quarter to the Q&A sessions of the firm and to the randomly-selected firms in all other quarters. The
PRES to Q&A line is slightly different. It compares the presentation session of the firm during the quarter
to its own Q&A session during the quarter relative to the Q&A session of nine randomly-selected Q&A
sessions of other firms. The bottom solid line represents the expected cumulative percentage of firms in
each ranking if the rankings were random.
44
FIGURE 3. VAL UE S O F R S C R IPT IN YE ARLY WINDO WS
S URRO UNDING T H E L IT IGAT IO N FIL ING DAT E
0.56
VALUE OF RSCRIPT
0.55
0.54
0.53
0.52
0.51
0.5
0.49
[-3, -2]
[-2, -1]
[-1, 0]
[0, 1]
[1, 2]
[2, 3]
YEAR WINDOW SURROUNDING THE LITIGATION FILING DATE
Figure 3. This figure presents the average value of RSCRIPTi,t for all conference calls held in yearly
windows surrounding the class action filing date. For example, the window [-1, 0] includes all calls made
one year prior to the litigation filing date.
45
Table 1
Descriptive statistics.
This table presents the means of variables used in the empirical analysis by quintile of SCRIPTi,t. The sixth column presents the test statistic of the difference
in means between the top and the bottom quintile. The penultimate column reports the means for the full sample and the final column reports the standard
deviations for the full sample. *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively. All continuous variables are winsorized at
the 1st and 99th percentiles. All variables are defined in Appendix B.
SCRIPTi,t Quintile
2
3
Variable
1
SCRIPTi,t
0.797
0.851
Dependent Variables:
FUT ROAi,t
FUT CFOi,t
CC CARi,t
FUT CARi,t
FREVi,t+1
GUIDANCEi,t
ACCURACYi,t+1
0.010
0.015
0.005
0.038
-0.140
0.228
-0.574
Control Variables:
ROAi,t
CFOi,t
MVEi,t
EARN SURPi,t
INSTOWNi,t
ANAL FOLLi,t
TURNOVERi,t
EARN VOLi,t
RET VOLi,t
BTMi,t
MOMi,t
AGEi,t
GUID SURPi,t
TONEi,t
CEO WC PRESi,t
CEO WC QAi,t
0.010
0.019
4,214.6
0.000
0.746
11.162
0.007
0.025
0.138
0.515
0.033
22.250
0.013
0.004
1,062.8
1,609.1
Full Sample
Mean
Std. Dev.
4
5
5 v. 1
0.878
0.902
0.934
312.66***
0.872
0.049
0.008
0.015
0.004
0.055
-0.182
0.242
-0.615
0.007
0.014
0.002
0.048
-0.217
0.237
-0.608
0.004
0.013
0.001
0.042
-0.210
0.234
-0.606
0.005
0.014
-0.001
0.049
-0.225
0.199
-0.606
-7.99***
-3.48***
-4.26***
1.28
-2.64***
-4.01***
-1.38
0.007
0.014
0.002
0.046
-0.195
0.228
-0.602
0.034
0.028
0.083
0.602
1.750
0.420
1.305
0.008
0.016
4,618.7
0.000
0.754
11.976
0.007
0.026
0.141
0.502
0.029
21.945
0.006
0.004
1,271.1
1,942.4
0.007
0.019
4,585.0
0.000
0.758
12.007
0.007
0.027
0.141
0.508
0.017
21.737
-0.002
0.004
1,397.3
2,078.9
0.004
0.015
5,413.5
-0.001
0.762
12.288
0.007
0.027
0.142
0.501
0.017
21.826
0.000
0.004
1,513.5
2,194.9
0.004
0.013
4,971.2
-0.001
0.755
12.116
0.007
0.027
0.140
0.503
0.014
21.335
-0.016
0.004
1,751.4
2,495.3
-8.77***
-3.64***
2.98***
-4.86***
2.32**
6.22***
6.46***
4.31***
1.56
-1.85*
-4.09***
-4.25***
-5.34***
-1.76*
48.11***
39.58***
0.007
0.016
4,760.6
0.000
0.755
11.910
0.007
0.026
0.140
0.506
0.022
21.819
0.000
0.004
1,399.2
2,064.1
0.039
0.097
15,520.0
0.011
0.208
8.659
0.005
0.033
0.066
0.361
0.270
12.165
0.306
0.005
786.6
1,237.3
46
Table 2
Conference call scripting and future accounting performance.
This table presents the OLS regression results of the relation between conference call scripting and
future firm performance. The dependent variables are the return on assets for the four quarters following
quarter t for firm i (FUT ROAi,t) and the operating cash flow scaled by lagged total assets for firm i in
the four quarters following quarter t (FUT CFOi,t) in columns 1 and 2, respectively. The independent
variable of interest is the RSCRIPTi,t measure for firm i in quarter t. Year-quarter and industry (twodigit SIC code) fixed effects are included as additional independent variables. The coefficients on the
year-quarter and industry indicator variables are suppressed. Standard errors are clustered by firm. All
continuous variables are winsorized at the 1% and 99% levels. *, **, and *** represent significance at
the 10%, 5%, and 1% levels, respectively. All variables are defined in Appendix B.
[1]
FUT ROAi,t
Coefficient t-stat
INTERCEPT
RSCRIPTi,t
ROAi,t
CFOi,t
EARN SURPi,t
ln(MVEi,t)
INSTOWNi,t
ln(ANAL FOLLi,t)
TURNOVERi,t
-0.016***
-0.003***
0.484***
EARN VOLi,t
RET VOLi,t
ln(AGEi,t)
GUIDANCEi,t
GUID SURPi,t
TONEi,t
ln(CEO WC PRESi,t)
ln(CEO WC QAi,t)
0.013
-0.037***
-0.000
0.001*
0.004***
0.207***
-0.001*
0.001
#OBS
Adjusted R2
-0.094***
0.004***
0.006***
-0.005***
0.032
30,773
0.492
-2.741
-4.123
34.106
-3.424
12.783
3.640
-7.102
0.506
1.025
-5.747
-0.034
1.767
7.527
4.205
-1.756
1.541
[2]
FUT CFOi,t
Coefficient
t-stat
0.002
-0.003***
0.209
-3.781
0.018***
0.097***
0.003***
0.008***
-0.003***
0.302***
6.754
4.126
8.180
3.650
-3.674
3.872
-2.463
-6.335
-2.137
3.186
6.951
2.426
-0.768
0.851
-0.039**
-0.054***
-0.002**
0.002***
0.004***
0.165**
-0.000
0.000
30,773
0.195
47
Table 3
Cumulative abnormal returns at and following the conference call date.
This table presents the OLS regression results of the relation between cumulative abnormal returns
at and following the conference call date and scripting of the call. The dependent variables are the
size and book-to-market adjusted buy and hold returns for the window [0,1] surrounding the
conference call date (CC CARi,t) in Panel A and for the window [2,254] following the conference
call date (FUT CARi,t) in Panel B. The independent variable of interest is the RSCRIPTi,t measure
for firm i in quarter t. Year-quarter and industry (two-digit SIC code) fixed effects are included as
additional independent variables. The coefficients on the year-quarter and industry indicator
variables are suppressed. Standard errors are clustered by firm. All continuous variables are
winsorized at the 1% and 99% levels. *, **, and *** represent significance at the 10%, 5%, and 1%
levels, respectively. All variables are defined in Appendix B.
Panel A: Conference call CAR
[1]
[2]
CC CARi,t
Coefficient t-stat
CC CARi,t
Coefficient
t-stat
INTERCEPT
0.019***
3.198
0.050***
4.205
RSCRIPTi,t
-0.008***
-5.007
-0.003**
-2.004
EARN SURPi,t
1.533***
21.018
ROAi,t
0.104***
6.186
ln(MVEi,t)
-0.002***
-4.281
BTMi,t
0.009***
4.573
MOMi,t
-0.020***
-9.439
0.001
0.050
GUIDANCEi,t
-0.004***
-3.227
GUID SURPi,t
0.039***
21.424
TONEi,t
2.863***
23.604
ln(CEO WC PRESi,t)
-0.004***
-4.642
0.000
0.351
RET VOLi,t
ln(CEO WC QAi,t)
#OBS
30,773
30,773
Adjusted R2
0.002
0.100
48
Panel B: Future CAR
INTERCEPT
RSCRIPTi,t
[1]
[2]
FUT CARi,t
FUT CARi,t
Coefficient
t-stat
Coefficient
t-stat
0.371**
2.365
0.386***
2.703
0.008
0.666
0.032**
2.309
-0.027
-1.246
-0.207***
-12.255
RSCRIPTi,t * LOW FUT ROAi,t
LOW FUT ROAi,t
EARN SURPi,t
-2.254*
-1.901
-1.921
-1.624
ROAi,t
-0.524***
-2.591
-1.408***
-6.573
ln(MVEi,t)
-0.024***
-4.653
-0.029***
-5.537
BTMi,t
0.004
0.124
0.091**
2.535
MOMi,t
-0.125***
-6.017
-0.146***
-7.220
RET VOLi,t
0.255***
2.645
0.466***
4.879
GUIDANCEi,t
-0.014
-1.542
-0.020**
-2.127
GUID SURPi,t
0.035***
4.122
0.019**
2.260
TONEi,t
2.543**
2.355
1.441
1.370
ln(CEO WC PRESi,t)
-0.015*
-1.959
-0.008
-1.084
-0.020***
-2.861
-0.020***
-2.871
ln(CEO WC QAi,t)
#OBS
Adjusted R
2
30,773
30,773
0.053
0.075
F-test:
RSCRIPTi,t + RSCRIPTi,t * LOW FUT ROAi,t = 0
Value [F-stat] (p-value)
0.005 [0.83] (0.36)
49
Table 4
Analyst forecast revisions following the conference call date.
This table presents the OLS regression results of the relation between analyst forecast revisions
following the conference call and conference call Q&A scripting. The dependent variable is the
analyst consensus forecast of EPS for quarter t+1 for all forecasts made within 30 days following
the quarter t conference call less the consensus forecast of EPS for quarter t+1 immediately prior
to the conference call multiplied by 100 (FREVi,t+1). The independent variable of interest is the
RSCRIPTi,t measure for firm i in quarter t. Year-quarter and industry (two-digit SIC code) fixed
effects are included as additional independent variables. The coefficients on the year-quarter and
industry indicator variables are suppressed. Standard errors are clustered by firm. All continuous
variables are winsorized at the 1% and 99% levels. *, **, and *** represent significance at the
10%, 5%, and 1% levels, respectively. All variables are defined in Appendix B.
FREVi,t+1
Coefficient
t-stat
INTERCEPT
-0.912***
-3.396
RSCRIPTi,t
-0.100***
-2.885
EARN SURPi,t
7.165***
3.392
0.461
0.785
ln(MVEi,t)
0.090***
4.768
INSTOWNi,t
0.165**
1.976
ln(ANAL FOLLi,t)
-0.038
-1.143
TURNOVERi,t
-3.428
-1.084
EARN VOLi,t
0.094
0.653
RET VOLi,t
0.267
1.326
ln(AGEi,t)
-0.078***
-3.260
GUIDANCEi,t
-0.043**
-2.308
GUID SURPi,t
0.350***
14.504
TONEi,t
24.012***
9.163
-0.019
-1.192
ln(CEO WC QAi,t)
0.040**
2.009
#OBS
30,293
ROAi,t
ln(CEO WC PRESi,t)
Adjusted R
2
0.072
50
Table 5
Earnings guidance and conference call scripting
This table presents the logistic regression results of the relation between the probability of
providing earnings guidance for the next quarter's EPS on the day of the conference call and
conference call Q&A scripting. The dependent variable is an indicator variable equal to 1 if the
firm provides earnings guidance for quarter t+1 on the day of the conference call for quarter t
(GUIDANCEi,t). The independent variable of interest is the RSCRIPTi,t measure for firm i in
quarter t. Standard errors are clustered by firm. All continuous variables are winsorized at the 1%
and 99% levels. *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.
All variables are defined in Appendix B.
Pr(GUIDANCEi,t)
Coefficient
z-stat
INTERCEPT
-1.386**
-2.030
RSCRIPTi,t
-0.275***
-2.591
-3.057
-1.386
2.771***
2.913
-0.051
-0.960
INSTOWNi,t
1.056***
4.229
ln(ANAL FOLLi,t)
0.610***
6.152
TURNOVERi,t
-9.144
-1.094
EARN VOLi,t
1.708
1.348
RET VOLi,t
-2.022***
-2.854
ln(AGEi,t)
-0.482***
-4.750
TONEi,t
34.081***
4.404
ln(CEO WC PRESi,t)
0.078
1.194
ln(CEO WC QAi,t)
-0.048
-0.900
MEET OR BEATi,t
0.749***
7.037
DISPERSIONi,t
-3.925***
-7.606
TRENDi,t
-0.021***
-2.030
EARN SURPi,t
ROAi,t
ln(MVEi,t)
#OBS
30,773
Pseudo R2
0.078
51
Table 6
Analyst forecast accuracy
This table presents the OLS regression results of the relation between analyst forecast accuracy
and scripting of the earnings conference call. The dependent variable is -100 multiplied by the
absolute value of actual EPS for quarter t+1 less the median analyst consensus forecast of EPS
for quarter t+1 for all forecasts made within 30 days following the conference call for quarter t
scaled by price at the end of quarter t (ACCURACYi,t+1). The independent variable of interest is
the RSCRIPTi,t measure for firm i in quarter t. Year-quarter and industry (two-digit SIC code)
fixed effects are included as additional independent variables. The coefficients on the yearquarter and industry indicator variables are suppressed. Standard errors are clustered by firm. All
continuous variables are winsorized at the 1% and 99% levels. *, **, and *** represent
significance at the 10%, 5%, and 1% levels, respectively. All variables are defined in Appendix
B.
ACCURACYi,t+1
Coefficient
t-stat
INTERCEPT
-2.835***
-4.984
-0.049*
-1.704
EARN SURPi,t
16.986***
8.197
ROAi,t
3.212***
8.236
ln(MVEi,t)
0.200***
12.630
INSTOWNi,t
0.635***
8.612
-0.032
-1.054
TURNOVERi,t
-15.175***
-4.312
EARN VOLi,t
-2.061***
-4.608
RET VOLi,t
-1.067***
-4.136
ln(AGEi,t)
-0.185***
-6.045
GUIDANCEi,t
0.074***
3.448
GUID SURPi,t
-0.006
-0.332
10.618***
4.820
-0.002
-0.095
0.061***
3.974
RSCRIPTi,t
ln(ANAL FOLLi,t)
TONEi,t
ln(CEO WC PRESi,t)
ln(CEO WC QAi,t)
#OBS
Adjusted R
30,773
2
0.251
52
Table 7
Future unexpected earnings and conference call scripting
This table presents the OLS regression results of the relation between conference call scripting
and future unexpected earnings. The dependent variables are the return on assets in quarter t+1
less the return on assets in quarter t scaled by total assets in period t (UE EARN(RW)i,t) and
earnings per share in quarter t+1 less the median value of analysts' forecasts of EPS of quarter
t+1 made prior to the conference call date scaled by price and multiplied by 100 (UE
EARN(ANAL)i,t) in columns 1 and 2, respectively. The independent variable of interest is the
RSCRIPTi,t measure for firm i in quarter t. Year-quarter and industry (two-digit SIC code) fixed
effects are included as additional independent variables. The coefficients on the year-quarter and
industry indicator variables are suppressed. Standard errors are clustered by firm. All continuous
variables are winsorized at the 1% and 99% levels. *, **, and *** represent significance at the
10%, 5%, and 1% levels, respectively. All variables are defined in Appendix B.
[1]
UE EARN(RW)i,t+1
Coefficient
t-stat
INTERCEPT
[2]
UE EARN(ANAL)i,t+1
Coefficient
t-stat
-0.004
-0.385
-2.650***
-6.029
RSCRIPTi,t
-0.002***
-3.363
-0.112**
-2.455
EARN SURPi,t
-0.157***
-5.520
63.882***
20.589
ROAi,t
-0.414***
-26.056
4.875***
6.450
ln(MVEi,t)
0.003***
12.024
0.339***
14.597
INSTOWNi,t
0.006***
3.856
0.529***
5.040
ln(ANAL FOLLi,t)
-0.004***
-7.576
-0.299***
-6.610
TURNOVERi,t
0.170***
3.008
-22.333***
-3.821
EARN VOLi,t
-0.003
-0.309
-0.746
-1.213
RET VOLi,t
-0.035***
-5.974
0.258
0.652
ln(AGEi,t)
-0.001*
-1.657
-0.318***
-7.515
GUIDANCEi,t
0.001**
2.435
-0.011
-0.344
GUID SURPi,t
0.005***
9.583
0.445***
11.822
TONEi,t
0.280***
6.034
44.570***
11.988
-0.001*
-1.879
-0.035
-1.298
ln(CEO WC QAi,t)
0.000
1.093
0.072***
3.082
#OBS
30,773
30,773
Adjusted R2
0.216
0.296
ln(CEO WC PRESi,t)
53
Table 8
Abnormal returns during time periods on the date of the conference call.
This table presents the OLS regression results of the relation between abnormal returns in the periods surrounding the conference call and
scripting of the call. The dependent variables are the abnormal returns in the following periods: (1) from the stock market open on the day of
the call to the start of the presentation session (ABN RET(PRE)i,t), (2) from the start to the end of the presentation session (ABN RET(PRES)i,t), (3)
from the start to the end of the Q&A session (ABN RET(QA)i,t), and (4) from the end of the Q&A session to the stock market close on the day of
the call (ABN RET(POST)i,t).The independent variable of interest is the RSCRIPTi,t measure for firm i in quarter t. Year-quarter and industry (twodigit SIC code) fixed effects are included as additional independent variables. The coefficients on the year-quarter and industry indicator
variables are suppressed. Standard errors are clustered by firm. All continuous variables are winsorized at the 1% and 99% levels. *, **, and
*** represent significance at the 10%, 5%, and 1% levels, respectively. All variables are defined in Appendix B.
INTERCEPT
[1]
[2]
[3]
[4]
ABN RET(PRE)i,t
Coefficient
t-stat
ABN RET(PRES)i,t
Coefficient
t-stat
ABN RET(QA)i,t
Coefficient
t-stat
ABN RET(POST)i,t
Coefficient
t-stat
0.007
0.486
-0.009**
-2.410
0.009*
1.856
0.006
0.648
RSCRIPTi,t
-0.005**
-1.997
-0.001
-0.826
0.000
0.460
-0.003*
-1.722
EARN SURPi,t
0.255***
2.729
0.006
0.247
-0.003
-0.093
-0.033
-0.563
ROAi,t
0.017
0.583
0.011
1.243
-0.000
-0.034
0.049**
2.434
ln(MVEi,t)
0.000
0.138
0.000
0.215
0.001***
2.729
-0.000
-0.677
BTMi,t
0.003
1.237
0.001
0.761
0.001
1.367
0.000
0.204
MOMi,t
-0.007**
-2.178
-0.000
-0.179
-0.002
-1.360
-0.007***
-2.848
RET VOLi,t
-0.002
-0.131
0.005
0.986
0.016**
2.417
-0.003
-0.219
GUIDANCEi,t
0.000
0.027
0.000
0.439
-0.000
-0.181
0.000
0.130
GUID SURPi,t
-0.000
-0.105
0.001
1.535
0.000
0.101
-0.002
-0.787
TONEi,t
0.246
1.365
0.072
1.300
0.117*
1.868
0.129
1.113
ln(CEO WC PRESi,t)
0.000
0.333
0.001*
1.717
-0.000
-0.667
-0.002**
-1.986
ln(CEO WC QAi,t)
-0.001
-0.507
-0.000
-0.448
-0.000
-0.126
0.002*
1.907
#OBS
4,168
4,168
4,168
4,168
0.010
-0.003
-0.002
0.012
Adjusted R
2
54
Table 9
Conference call scripting and litigation.
This table presents the logistic regression results of the relation between conference call scripting and periods surrounding class action lawsuit
filing dates. The dependent variables are LITIGATION PRE2i,t, LITIGATION PRE1i,t, LITIGATION POST1i,t, and LITIGATION POST2i,t in
Columns 1 to 4, respectively. The independent variable of interest is the RSCRIPTi,t measure. Standard errors are clustered by firm. All continuous
variables are winsorized at the 1% and 99% levels. *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively. All variables
are defined in Appendix B.
[1]
[2]
[3]
[4]
LITIGATION PRE2i,t
LITIGATION PRE1i,t
LITIGATION POST1i,t
LITIGATION POST2i,t
Coefficient
z-stat
Coefficient
z-stat
Coefficient
z-stat
Coefficient
z-stat
INTERCEPT
RSCRIPTi,t
EARN SURPi,t
ROAi,t
ln(MVEi,t)
INSTOWNi,t
ln(ANAL FOLLi,t)
-6.660***
0.216
3.103
-2.397
0.263***
0.157
0.288*
-6.006
1.267
0.770
-1.512
3.597
0.452
1.697
-6.948***
0.496***
-2.263
-3.948***
0.436***
0.042
0.013
-6.084
3.135
-0.522
-2.578
6.124
0.112
0.083
-5.945***
0.400**
1.248
-4.385***
0.210***
-0.681**
0.377**
-4.821
2.268
0.302
-3.264
2.611
-2.021
2.190
-7.389***
0.174
-6.917*
-3.522***
0.375***
0.061
0.156
-6.433
0.957
-1.688
-3.349
4.913
0.190
0.977
TURNOVERi,t
EARN VOLi,t
RET VOLi,t
ln(AGEi,t)
GUIDANCEi,t
GUID SURPi,t
TONEi,t
ln(CEO WC PRESi,t)
18.916
3.030*
3.562***
-0.214
-0.071
0.220
-10.535
-0.119
1.636
1.705
3.200
-1.602
-0.484
1.611
-0.717
-1.038
52.004***
0.416
3.800***
-0.434***
-0.024
-0.192
-39.069***
0.008
5.183
0.222
3.829
-3.156
-0.159
-1.341
-3.088
0.072
46.589***
-0.541
3.218***
0.003
0.067
0.016
-73.442***
-0.003
4.636
-0.313
3.058
0.022
0.447
0.102
-5.222
-0.030
-7.299
1.313
6.843***
-0.067
0.176
-0.237*
-30.309**
0.107
-0.609
0.776
6.974
-0.487
1.210
-1.797
-2.387
0.939
ln(CEO WC QAi,t)
TRENDi,t
0.129
-0.002
1.444
-0.330
0.008
-0.000
0.097
-0.033
-0.066
-0.004
-0.703
-0.654
-0.158*
0.003
-1.651
0.650
#OBS
Pseudo R2
30,773
0.041
30,773
0.070
30,773
0.063
30,773
0.048
55