earnings guidance, bias, and stock price crash risk
TRANSCRIPT
Earnings Guidance, Bias, and Stock Price Crash Risk
Sophia J.W. Hamm
Fisher College of Business
The Ohio State University
Columbus, OH
614 292 2529
Edward Xuejun Li
Zicklin School of Business
Baruch College
New York, NY
646 312 3235
Jeffrey Ng
School of Accounting and Finance
The Hong Kong Polytechnic University
Kowloon, Hong Kong
+852 2766 7099
August 5, 2018
____________________ We appreciate helpful comments from an anonymous reviewer, Anne Beatty, Jeremy Bertomeu, Ilan
Guttman, Darren Roulstone, Lakshmanan Shivakumar (Editor), Nemit Shroff, Andy Van Buskirk, Rodrigo
Verdi, Ro Verrecchia, Jerry Zimmerman, seminar participants at Kent State University, and conference
participants at the University of Minnesota Empirical Accounting Conference 2012, FARS 2013, and the
Baruch–SWUFE Accounting Conference 2013.
Earnings Guidance, Bias, and Stock Price Crash Risk
Abstract
Many recent studies have explored how earnings properties such as opacity, conservatism, and
comparability are related to stock price crash risk. Motivated by the importance of earnings
guidance as a voluntary disclosure mechanism, we investigate how guidance and bias in guidance
are related to crash risk. Our initial analysis shows that more guidance on average is associated
with a higher crash risk, which is somewhat surprising if one relates more disclosure to greater
transparency. Upon in-depth investigation, we find that optimistic guidance drives this result
through its positive relation with stock price crash risk. This finding is consistent with guidance
optimism, intentional or unintentional, temporarily suppressing bad news until its future
revelation. Overall, our findings highlight that earnings guidance, when optimistic, can expose
equity investors to significant downside risk.
Keywords: crash risk, earnings guidance, forecast bias, optimism
JEL classification: G14, M41, M43
Data Availability: The data used in this study are available from the public sources identified in
the paper.
1
1. Introduction
In response to a series of major corporate scandals (e.g., Enron, AIG, etc.) and the recent
financial crisis, investigating the cause of extreme price declines has become a source of
considerable interest to regulators, practitioners, and researchers. In her testimony before the
Financial Crisis Inquiry Commission, then-SEC Chairman Mary Schapiro contended that “[a]
central question [...] is whether investors received timely and accurate disclosure concerning
deteriorating business conditions” (Schapiro, 2010). While many recent studies in finance and
accounting investigate how characteristics of the earnings generation process (e.g., accrual quality,
conservatism, and comparability) are related to future stock price crashes (e.g., Hutton et al., 2009;
Kim and Zhang, 2014, 2016; DeFond et al., 2015; Kim et al., 2016), there is limited evidence on
the impact of voluntary disclosure on stock price crash risk.
A careful analysis of a prominent voluntary disclosure mechanism, earnings guidance, and
the bias therein could enhance our understanding of the relation between earnings information and
crash risk in two ways. First, prior studies highlight guidance as a primary outlet of timely earnings
news. For example, Ball and Shivakumar (2008, p.1009) estimate that guidance, if issued, explains
20%-25% of the total quarterly stock return variance as compared to only 3.5%-4.5% from
earnings releases. Similarly, Beyer et al. (2010) show that earnings releases and SEC filings
account for less than 12% of the total stock return variance explained by financial disclosures,
compared to over 55% from guidance. Given its impact on stock returns, guidance could have
important implications for crash risk. Second, while prior studies document significant
associations between various earnings characteristics and crash risk, there is limited evidence on
how disclosure bias gets impounded into price and leads to future crashes. One difficulty is that
the metrics of disclosure bias often have significant measurement errors and interpretive ambiguity
2
(Dechow et al., 2010). For example, discretionary accrual models have a low explanatory power
and can attribute changes in business fundamentals to misreporting (Owens et al., 2017). Earnings
guidance, however, provides a nice setting for capturing forecast bias and hence allows for a
cleaner and more direct test. The purpose of our study, therefore, is to investigate how guidance
and its bias are related to stock price crash risk.
Our empirical investigation is important because, ex ante, there are no clear predictions on
the relation between guidance and crash risk. Intuitively, one might expect that issuing guidance
lowers crash risk because a vast literature views guidance as an opportunity for managers to reveal
private information and adjust market expectations towards their beliefs (Ajinkya and Gift, 1984;
Kasznik and Lev, 1995; Matsumoto, 2002). Prior studies also suggest that guidance allows for
better monitoring, which curbs managers’ value-destroying behaviors (e.g., Bushman and Smith,
2001; Healy and Palepu, 2001; Nagar et al., 2003). Further, litigation risk prompts managers to
forewarn investors about bad news (Skinner, 1994). Collectively, this typical view predicts that
managers issue guidance to reduce the risk of a future stock price crash. We refer to this prediction
as the crash preemption hypothesis.
However, considering that guidance is voluntary and not audited before issuance, there are
growing concerns about guidance contributing to inflated market expectations (Core, 2001; Healy
and Palepu, 2001), which could, in turn, raise crash risk. One possibility is managers’ intentional
misuse of guidance.1 Specifically, if declining business conditions prompt a manager’s career
concerns, it could incentivize her to deviate from truthful reporting (Jin and Myers, 2006; Bleck
and Liu, 2007; Benmelech et al., 2010). Instead, she could sugarcoat poor results with optimistic
1 For prior studies, see, for example, Aboody and Kasznik (2000), Amel-Zadeh and Meeks (2018), Bergman and
Roychowdhury (2008), Cheng and Lo (2006), Cotter et al. (2006), Feng et al. (2015), Noe (1999), Rees et al. (2014),
and Shroff et al. (2013).
3
guidance that creates an illusion of stability while betting that a future revival will hide the
discrepancy (Graham et al., 2005; Kothari et al., 2009). Given asymmetric information, it is often
difficult for investors to immediately detect changes in guidance incentives (Fischer and
Verrecchia, 2000; Hutton et al., 2003; Rogers and Stocken, 2005).2 Another possibility is that
managers issue unintentionally biased guidance. For example, in periods of high sentiment,
managers can overinvest and justify their decision with optimistic forecasts. Investors influenced
by the same high sentiment can keep the consequent bubble alive for some time. In either case, to
the extent that a firm cannot produce earnings to meet inflated expectations and the future
revelation of bad news triggers an abrupt decline in stock price, more guidance could engender a
higher crash risk. We refer to this predication as the inflated expectation hypothesis.
Collectively, the above hypotheses highlight the ex-ante tension in the research question
about the net impact of guidance on crash risk. To address this question, we analyze a sample of
71,909 firm years from 1997 to 2015. We follow Hutton et al. (2009) to measure crash risk after
controlling for both market and industry returns. Such a firm-specific measure helps alleviate the
concern that any result is purely driven by market-wide shocks. To test the link between guidance
and future crashes, we focus on long-horizon guidance (i.e., annual earnings guidance with its
realized value reported in the following year) and use its incidence and frequency within a fiscal
year as our initial measures of guidance.3
Our first analysis shows a positive relation between a firm’s guidance frequency and its
crash risk, after controlling for information characteristics such as accruals quality (Hutton et al.,
2 The financial reporting fraud at Qwest Communications (United States v. Nacchio (No. 07-1311, March 17, 2008)
offers a good illustration. In September 2000, Qwest’s CEO Joseph Nacchio issued an annual revenue forecast of
$21.3 to $21.7 billion for fiscal year 2001, despite an internal memo hinting at a disappointing $20.4 billion or worse.
He reaffirmed the guidance several times later in spite of deteriorating prospects. When Qwest finally revealed the
bad news in June 2001, its stock price plunged about 21%. 3 Untabulated analysis finds no significant relation between crash risk and short-horizon guidance (i.e., earnings
guidance, annual or quarterly, with its realized value reported in the same year).
4
2009), accounting conservatism (Kim and Zhang, 2016), financial statement comparability (Kim
et al., 2016), real earnings management (Francis et al., 2016), and 10-K readability (Ertugrul et al.,
2017). Further analysis indicates that this relation is economically significant. This finding is
surprising given that prior literature often relates more disclosure to greater transparency.
To provide direct evidence on the mechanism of this relation, we classify each guidance as
optimistic or pessimistic by comparing guidance with its realized value. We then examine how a
firm’s crash risk is related to its issuance of optimistic and pessimistic guidance. We find a
significant positive relation between the issuance of optimistic guidance and crash risk but no
significant relation between pessimistic guidance and crash risk.4 These results suggest that on
average, the inflated expectation hypothesis is descriptive of the link between guidance and crash
risk. Specifically, optimistic guidance is related to inflated expectations, which lead to a higher
crash risk. However, there is no general evidence to support the crash preemption hypothesis.
We then attempt to address various robustness and endogeneity issues that could arise in
the analysis of the relation between optimistic guidance and crash risk.5 First, we show that our
finding is generalizable across most years and not specific to years with high sentiment or market
crashes. Second, we use a Conditional Logit model and Chamberlain’s Random Effects (CRE)
probit model (Wooldridge, 2002) to control for firm fixed effects and find robust results. Third,
we exploit a natural experiment under Regulation SHO (Reg SHO) for the relation between
guidance optimism and crash risk. Chen et al. (2014) and Li and Zhang (2015) show that while
Reg SHO has no significant impact on the pilot firms’ guidance frequency and bias, it increases
4 Neutral guidance is left out of this analysis because it comprises only a small portion (6%) of the long-horizon
guidance sample. Including neutral guidance does not change the tenor of results. In particular, untabulated analysis
shows that neutral guidance exhibits no significant association with crash risk. 5 All our results related to guidance optimism are robust to including the corresponding guidance pessimism
measures as control variables.
5
short selling pressure and price sensitivity to bad news for such firms. Such increased sensitivity
to disappointing realized earnings predicts a stronger relation between optimistic guidance and
crash risk for the pilot firms during the Reg SHO treatment period. However, such a differential
relation should not be present in either the pre- or post-Reg SHO periods. We find results consistent
with our predictions. Given the randomness in selecting the pilot firms, this analysis provides
strong evidence that the relation between guidance optimism and crash risk is not purely driven by
omitted correlated variables. Lastly, we consider Regulation Fair Disclosure (Reg FD) as an
exogenous shock for guidance since firms issue more guidance to replace the selective disclosure
that Reg FD prohibits (Bailey et al., 2003; Heflin et al., 2003; Heflin et al., 2016).6 Because the
idea behind Reg FD is “leveling the playing field” and boosting confidence in the capital market,
it is unlikely to directly raise the probability of future crashes.7 Our two stage method yields a
similar positive relation between optimistic guidance and crash risk. A caveat is that many market-
wide events surrounding Reg FD could contaminate this result.
While guidance optimism could arise either intentionally or unintentionally, one might
expect investor perception of the latter origin to engender a lower crash risk. To proxy for
conditions under which investors would perceive bias as more unintentional, we use high litigation
risk, which is expected to curb intentional bias. We find that when bias is more likely to be
unintentional, the positive relation between guidance optimism and crash risk becomes more
attenuated. To further examine the issue of unintentional and intentional bias, we decompose
guidance bias into a predictable portion that arises from serial correlation and an innovation in bias,
6 To give a sense of the shock, we show that the percentage of guidance firms jumped from 27.2% in 2000 to 37.6%
in 2001 and the percentage of firms that issue optimistic guidance rose from 13.7% to 24.3%. 7 On the contrary, Kothari et al. (2009) provide evidence that firms reduced the extent of bad news withholding
relative to good news after Reg FD, which implies that the regulation may have indirectly decreased crash risk. This
speaks to the strength of Reg FD as our instrumental variable (Larcker and Rusticus, 2010).
6
as Gong et al. (2011) argue that bias due to serial correlation is associated with managers’
unintentional information processing bias rather than opportunistic forecasting behaviors. We find
that crash risk is positively related to both types of guidance bias.
We also conduct several more analyses to triangulate our results. First, we test market
reactions to guidance issued prior to crashes and find no evidence that investors undo the guidance
bias. This outcome offers direct evidence on how disclosure bias is impounded into price before
crashes. Second, we find a negative relation between guidance optimism and future stock returns.
We note that there is an important distinction between this type of negative returns and price
crashes, as theories suggest that investors have a strong aversion to large occasional crashes and
demand extra compensation for bearing such a high level of crash risk (Bates, 1991; Pan, 2002).
Nevertheless, the similarity in the results with stock price crash risk and future stock returns
suggest that the negative impact of stock price crashes are not transitory.
While we find no significant relation between pessimistic guidance and crash risk in
general, it is difficult to fully dismiss the preemptive role of guidance. In our final analysis, we
follow Kim and Park (2012) in identifying a subset of pessimistic guidance issued for downward
expectation management and show that such guidance is negatively related to future crashes,
lending some support to the crash preemption hypothesis.
Taken as a whole, our study extends prior research on corporate disclosure and crash risk.
Our results suggest that earnings guidance plays an important role in crash risk, one that is
incremental to the many earnings characteristics prior literature examines. We also provide the
first direct evidence on the mechanism by which the forecast bias gets impounded into price and
leads to future crashes. Finally, our investigation of a long-horizon capital market outcome of
guidance also adds to the management forecast literature that typically focuses on short term
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market reactions (e.g., Patell, 1976; Penman, 1980; Rogers et al., 2009). On a broader note, our
study highlights an important link between voluntary disclosure and future stock price downside
risk. We caution that our evidence does not suggest that managers have a general tendency to
inflate their forecasts or that guidance optimism is prevalent, because a stock price crash, by
construction, is an extraordinary event. A more appropriate, albeit narrower, conclusion is that if
a firm provides guidance that later turns out to be optimistic, there is a higher stock price crash
risk and predictable variations exist in this relation.
The rest of the paper is organized as follows. Section 2 summarizes the related research
and our predictions. In Section 3, we discuss our data and the basic research design. Section 4
presents empirical results on the replication of prior studies and the relation between guidance and
crash risk. In Section 5, we conduct a direct test of forecast bias and crash risk. Section 6 provides
several supplemental analyses. We conclude in Section 7.
2. Prior research and predictions
2.1 Prior research on corporate disclosure and crash risk
Beginning with Jin and Myers (2006), researchers have been concerned about whether the
information asymmetry between managers and shareholders, coupled with managers’ self-interest,
could be related to stock price crash risk. As Taleb (2007) indicates, a good understanding of these
extreme outcomes can offer valuable insight into their true nature. The recent literature on crash
risk argues that a stock price crash occurs when investors realize that stock prices have been
(severely) inflated and that a crash’s occurrence could be an indicator of prior agency problems.8
While several analytical works use different models and settings, the underlying themes are largely
8 While recent research has focused on the agency problems that lead to extreme price declines, the early literature
examines a few equity market-based explanations for price crashes (e.g., Chen et al., 2001; French et al., 1987; Hong
and Stein, 2003; Romer, 1993).
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similar: managers’ career concerns give them an incentive to conceal bad news (i.e., job security
or compensation) and opacity allows managers to hoard bad news, which subsequently leads to a
price crash (Jin and Myers, 2006; Bleck and Liu, 2007; Benmelech et al., 2010).9
Seeking to explore the precise nature of the agency problems, recent empirical studies have
investigated how crashes arise from managers’ bad news hoarding, which could be due to tax
avoidance (Kim et al., 2011a), equity-based compensation (Kim et al., 2011b), and opaque
reporting practices (Jin and Myers, 2006; Hutton et al., 2009; Kim and Zhang, 2014; Kim and
Zhang, 2016; Kim et al., 2016; Kim et al., 2018). More specifically, based on information from
earnings reports, Hutton et al. (2009) demonstrate that poor accruals quality in reported annual
earnings allows managers to conceal bad news, which leads to future price crashes. Kim and Zhang
(2014) confirm Hutton et al.’s (2009) inference using expected crash risk. Kim and Zhang (2016)
also show that the level of accounting conservatism inferred from reported earnings is negatively
associated with crash risk. Further, based on information from 10-K filings, Kim et al. (2018) and
Ertugrul et al. (2017) find that less readable 10-Ks predict a higher future price crash risk. Finally,
Kim et al. (2016) find that financial statement comparability can discipline managers from
concealing bad news and shows a negative relation between comparability and expected crash risk.
Despite these efforts, the bad news hoarding mechanisms examined to date have largely
been confined to characteristics of the earnings generation process or disclosures within the
mandatory reporting system. This is limited evidence on the role played by voluntary disclosure.
However, prior studies have shown that voluntary disclosure has a significant price impact (Ball
9 For example, the model in Jin and Myers (2006) predicts the following link between opacity and crash risk. Self-
interested managers have incentives to hide bad news about cash flow innovations because their informational
advantage allows them to exploit shareholders. Opacity about firm operations helps managers conceal information.
When the news is negative, managers would personally absorb losses and conceal the bad news to keep their jobs.
However, when the accumulated losses become excessive, they exercise the abandonment option and reveal the
accumulated bad news all at once, leading to extreme price declines.
9
and Shivakumar, 2008; Beyer et al., 2010), which is particularly relevant when managers’ motives
in disclosing are tied to stock price outcomes. While prior studies document associations between
earnings characteristics and crash risk, there is little evidence on how disclosure bias gets
incorporated into stock price, subsequently leading to crashes. A major hurdle faced by prior
studies may be the lack of clean measures on disclosure bias. Prior research shows that
discretionary accrual models often misattribute changes in business fundamentals to opportunistic
reporting (Owens et al., 2017). Dechow et al. (2010) also contend that many estimation models
(e.g., accruals, conservatism, etc.) are plagued by low explanatory power and interpretative
ambiguity.
To address these issues, we extend prior studies by investigating a prominent type of
voluntary disclosure, namely earnings guidance. Two unique features make this an important
disclosure setting to examine. First, as discussed above, guidance has a considerable impact on
stock price. Second, it is relative easy and straightforward to measure disclosure bias by comparing
a forecast with the subsequently realized value. The objective of our paper is to provide a more
complete picture on the relation between corporate disclosure and crash risk.
2.2 Earnings guidance and crash risk
In addition to the aforesaid reasons, our empirical investigation into the relation between
management earning guidance and stock price crash risk is important because, ex ante, there are
no clear predictions on how guidance and its forecast bias affect future stock price crashes.
Therefore, we adopt a more balanced approach to carefully evaluate the different effects that
guidance and its bias could have on crash risk.
Specifically, in line with traditional disclosure theory (Verrecchia, 2001), the expectation
adjustment hypothesis by Ajinkya and Gift (1984) posits that managers issue guidance to narrow
10
the gap between managers’ and investors’ expectations about future earnings. The idea here is that
information asymmetry exists between the firm and market participants. Managers, seeking to
reduce this information asymmetry, issue earnings guidance to synchronize investors’ earnings
expectations with managers’ beliefs. A series of studies present evidence in support of the
expectations adjustment hypothesis (e.g., Hassell and Jennings, 1986; Kasznik and Lev, 1995;
Matsumoto, 2002). Consistent with guidance reducing information asymmetry, Coller and Yohn
(1997) find that bid-ask spreads decrease after guidance is issued. Frankel et al. (1995) offer further
evidence that managers issue more guidance before accessing capital markets to lower the costs of
raising capital. If managers provide guidance to forewarn investors when their firms face a
downturn in business, we would expect guidance to reduce the risk of a future stock price crash.
In addition, the information contained within earnings guidance also allows for better monitoring
and reduces managers’ incentives to shirk or engage in value-destroying behaviors that are likely
to trigger price crashes (Bushman and Smith, 2001; Healy and Palepu, 2001; Nagar et al., 2003).
Furthermore, Skinner (1994) suggests that managers have strong incentives to avoid litigation risk
by issuing bad news guidance that preempts large negative earnings surprises, which would trigger
significant price declines and considerable litigation costs. Collectively, these arguments suggest
that guidance could have a crash preemption role, which would thus create a negative relation
between guidance and stock price crash risk.
However, recent literature has considered the possibility that earnings guidance could lead
to a misalignment between market expectations and firm fundamentals because of possible bias in
earnings guidance. The bias could be either intentional or unintentional. First, prior studies provide
extensive discussion and examination of the opportunistic use of voluntary disclosure. As Healy
and Palepu (2001, p. 425) caution, “the extent to which voluntary disclosure mitigates resource
11
misallocation in the capital market depends on the degree of credibility of information [...].
Because managers have incentives to make self-serving voluntary disclosures, it is unclear whether
management disclosures are credible.” Core (2001) adds to this agency point of view by noting
that in addition to the informational role of disclosure, it is important to jointly consider managers’
incentives and corporate governance structure to understand firms’ optimal disclosure policies and
their enforcement. Fischer and Verrecchia (2000) further demonstrate that managers have
incentives to bias reports if there is sufficient uncertainty about their reporting objectives.
Hermalin and Weisbach (2012) also predict that career concerns (i.e., job security or
compensation) could induce managers to opportunistically distort disclosure if they are to be
evaluated against it. As Kothari et al. (2009) argue, managers face asymmetric payoffs in
disclosure because good news increases compensation and extends tenure, whereas bad news leads
to adverse outcomes such as reduced compensation, termination of employment, and a tarnished
reputation in labor markets. Consequently, managers have incentives to issue optimistic guidance
that camouflages bad news in the hope that their firm’s business conditions will improve insofar
as to nullify the need to ever report such news (Graham et al., 2005). Even if the guidance is bound
to be verified later against realized values, it cannot completely discourage managers’
opportunistic guidance.10
One might also expect unintentional upward bias in guidance to contribute to inflated
expectations. Hurwitz (2017) finds that guidance optimism increases with investor sentiment and
10 A general concern about the opportunistic disclosure conjecture is that managers should ex-ante engage in truthful
disclosure out of a rational belief that the truth will be revealed in the future. In fact, an extensive literature provides
evidence that managers are, on average, not manipulative. Our study, however, narrows down to the context in which
managers are not always perfectly truthful in their disclosure. Likely rationales include career concerns, as well as the
notion that the optimal disclosure choice is not always the perfect transparency because eliminating all manipulations
can be too costly to stockholders (Watts and Zimmerman, 1986; Lambert et al., 1991). Another possibility is that
(some) managers are not completely rational and/or they believe that their misrepresentation will not be detected
(Dichev et al., 2013).
12
that such sentiment-driven optimism is most likely unintentional. In periods of high sentiment,
managers holding optimistic views would overinvest and issue optimistic guidance to justify their
decision. Hribar and Yang (2016) find consistent evidence that managerial overconfidence
increases the amount of optimism in management forecasts. When the sentiment-driven bubble
subsequently bursts, investor and managers revise their beliefs, causing a stock price crash. Kim,
et al. (2016) present a similar argument that when managers overestimate their investment returns,
more crashes ensue. While they do not test guidance, we conjecture that unintentional guidance
optimism would play an important role. In other words, to the extent that the market does not
immediately unravel unintentional optimism in guidance, there could be inflated market
expectations and a higher likelihood of a future stock price crash.11
Taken together, regardless of whether the inflated expectations are related to intentional or
unintentional optimistic guidance, there will be a positive relation between guidance and crash
risk. We refer it as the inflated expectation hypothesis. In sum, the average effect of guidance and
its bias on stock price crash risk is an open empirical question. We investigate this question first
providing an initial analysis of the relation between guidance and stock price crash risk. We then
provide in-depth analyses on how guidance optimism, the guidance characteristic most likely to
contribute to inflated expectations, could explain the relation between guidance and stock price
crash risk.
3. Data and basic research design
11 In the real world, it is ex-ante difficult for investors to price-protect against bias in information, intentional or
unintentional, especially in the presence of information asymmetry between the firm and its investors. The nature of
voluntary disclosure is likely to make price-protection even more difficult. The assumption that investors can easily
undo the intentional optimistic bias at the time of the disclosure begs the question of why managers would make the
effort to create bias, thereby exposing themselves to litigation and reputation risk. If the disclosure contains
optimistic bias because both the managers and market participants are overly optimistic about the firm’s prospects,
investors are even more likely to fail to infer and price the bias.
13
3.1 Data
We draw our primary sample from the intersection of CRSP and Compustat. Our sample
begins in 1997, the first year that the First Call CIG provides a comprehensive coverage of earnings
guidance, and ends in 2015. Because First Call CIG was discontinued in 2011, we supplement it
with the new I/B/E/S guidance data, which cover guidance from 2002 and onward12. As we
examine the relation between guidance and future stock price crashes, we focus on 62,817 long-
horizon annual EPS guidance with points, range, and open-ended estimates from the union of these
two databases. Long-horizon guidance refers to management forecasts whose realized values are
to be reported in the next year. Consistent with prior literature, we exclude pre-announcements.
Similar to Hutton et al. (2009), we exclude low-priced stocks and firms in the financial and utilities
industries and calculate crash risk using CRSP data. We further use I/B/E/S estimates, Thomson
Reuters Insiders, and Institutional Holdings (13f) databases for control variables. Our main sample
consists of 71,909 firm-year observations.
3.2 Basic research design
To test our hypothesis on how guidance is related to stock price crash risk, we follow
Hutton et al. (2009) in adopting the following basic research design for our regression analyses:
𝐶𝑟𝑎𝑠ℎ𝑡+1 = 𝛼 + 𝛽 ∗ 𝐺𝑢𝑖𝑑𝑎𝑛𝑐𝑒𝑉𝑎𝑟𝑠𝑡 + 𝛾1𝑅𝑂𝐸𝑡
+ 𝛾2𝑆𝑖𝑧𝑒𝑡 + 𝛾3𝑀𝐵𝑡
+ 𝛾4𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑡
+𝛾5𝑀𝑒𝑎𝑛𝑅𝑒𝑡𝑡 + 𝛾6𝑆𝑡𝑑𝑅𝑒𝑡𝑡
+ 𝛾7𝐼𝑛𝑠𝑖𝑑𝑒𝑟𝑜𝑤𝑛𝑡 + 𝛾8𝑁𝑎𝑛𝑎𝑙𝑦𝑠𝑡𝑡 + 𝛾9𝐼𝑛𝑠𝑡𝑜𝑤𝑛𝑡 + 𝛾10𝑁𝑆𝑒𝑔𝑡 + 𝜀𝑡+1, (1)
where Crash measures the risk of stock price crashes in fiscal year t+1. In particular, we use the
dummy variable Crash to capture, after controlling for market and industry returns, whether a
firm-specific extreme-negative stock return occurs in any of the 52 weeks during fiscal year t+1.
Appendix 1 provides a detailed definition of the variable. GuidanceVars refers to the set of test
12 One reason for combining the unique observations from both databases is to mitigate the issue of earnings
guidance coverage bias highlighted in Chuk et al. (2013)
14
variables for guidance calculated in fiscal year t. We describe each of these variables in detail as
we present their results in subsequent sections.
We also include two sets of control variables measured as of fiscal year t. The first set is
based on Hutton et al. (2009). ROE is the net income before extraordinary items over the
shareholders’ equity; Size is the natural logarithm of the market value; MB is the market to book
ratio; and Leverage is the ratio of total liability to total assets. To mitigate concerns about the
endogenous nature of guidance, we further include a few determinants of guidance issuance that
Nagar et al. (2003) identify as additional controls. We define MeanRet and StdRet as the mean and
standard deviation of the weekly returns in year t. MeanRet is a proxy for firm performance that
could affect both disclosure and stock crash risk. StdRet serves as a control for the increase in
volatility upon guidance that Rogers et al. (2009) document. InsiderOwn is the number of shares
held by insiders divided by the total number of shares outstanding. Nanalyst is the number of
analysts following and Instown is the % of institutional ownership. NSeg is the number of business
segments. We also control for year and industry fixed effects (not shown). To deal with potentially
inflated z-statistics due to the cross-sectional and time-series dependence of the residuals in panel
data, we cluster the standard errors by both firm and year (Petersen, 2009). As an abbreviation, we
use ControlVars to denote all these control variables for the rest of the paper.
3.3 Basic descriptive statistics
First, we provide descriptive statistics on the guidance level by examining the forecast bias
of the 62,817 long-horizon annual earnings guidance in our sample. We calculate the guidance
bias as the difference between the forecasted value and actual earnings, deflated by the fiscal year
end stock price. For a range forecast, the bias is positive (negative) if the actual value falls below
(exceeds) the minimum (maximum) and zero if it falls within range. 39.2% of forecasts are
15
optimistically biased, whereas 54.8% are classified as pessimistic. The remaining 6% are neutral.
In Figure 1, we plot the frequencies of guidance in each bias interval with width of 0.005. The
dotted bar on zero represents the frequency of neutral guidance. For readability, frequency bars for
guidance with bias outside the 10% and 90% percentiles are omitted from the figure. Guidance
with a negative bias is generally distributed closer to the zero bias threshold while optimistic bias
is more dispersed, which suggests that in providing long-horizon guidance, managers are more
likely to be pessimistic but not overly pessimistic in magnitude.
We then count guidance for our firm-year sample. Table 1 presents the sample’s yearly
distribution. Out of the 71,909 firm-year observations, 16,975 (23.60%) have at least one guidance
and 9,239 (12.84%) have at least one optimistic guidance. As shown in the table, there is a surge
in the frequency of guidance in 2001, which is consistent with prior research that Reg FD creates
incentives for firms to issue more guidance to replace the selective disclosures the regulation
prohibits (Bailey et al., 2003; Heflin et al., 2003; Heflin et al., 2016). Similar to Twedt (2016), we
find that guidance frequency starts to decline after 2004 but remains largely stable for the rest of
the sample period.
Table 2, Panel A presents the descriptive statistics of our main sample. Guide is a dummy
variable indicating whether the firm issues any guidance during the fiscal year and GuideFreq is
the total number of guidance. GuideOpt is a dummy variable indicating whether a firm issued at
least one optimistic guidance in fiscal year t. GuideOptFreq is the number of optimistic guidance
that a firm issued in fiscal year t, and GuideOptLast is a dummy variable indicating whether the
last guidance a firm issued in fiscal year t is optimistic. We consider this measure as setting the
“final tone” for the annual forecast bias. The mean values of GuideOptFreq and GuideOptLast are
12.8% and 8.7%, respectively, indicating a low frequency of optimistic guidance. Panel B presents
16
the piecewise correlation coefficients among the main variables. We find positive correlations
between the crash and optimistic guidance variables.
4. Analysis on earnings guidance and crash risk
We begin our investigation into the link between earning guidance and crash risk with the
general count measures of guidance. Specifically, we estimate the relation between guidance and
crash risk with the following model:
𝐶𝑟𝑎𝑠ℎ𝑡+1 = 𝛼 + 𝛽1𝐺𝑢𝑖𝑑𝑒𝑡(𝐺𝑢𝑖𝑑𝑒𝐹𝑟𝑒𝑞𝑡) + 𝛽2𝑂𝑝𝑎𝑞𝑡 + 𝛽3𝑂𝑝𝑎𝑞𝑡2 + 𝛽2𝐶𝑜𝑚𝑝𝑎𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑡 +
𝛽4𝐶_𝑆𝑐𝑜𝑟𝑒𝑡 + 𝛽5𝐷𝑅𝑂𝑡 + 𝛽610𝐾𝐹𝑖𝑙𝑒𝑠𝑖𝑧𝑒𝑡 + 𝛾 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑠𝑡 + 𝜀𝑡+1, (2)
where Guide is a dummy variable indicating whether the firm issues any guidance during fiscal
year t. A positive (negative) coefficient on Guide indicates that guidance firms have a higher (lower)
probability of a future stock price crash than do non-guidance firms. GuideFreq is the total number
of guidance defined as above. A positive (negative) coefficient on GuideFreq indicates that firms
that issue more guidance have a higher (lower) crash risk. These two variables capture the guidance
incidence and frequency. 13 To control for previously documented influential information
characteristics on crash risk, we include five measures: accrual quality (Opaq), financial statement
comparability (Comparability), accounting conservatism (C_Score), real earnings management
(DRO), and 10-K readability (10KFilesize). Opaq is the three-year moving sum of the absolute
annual discretionary accruals estimated from the modified Jones model (Dechow et al., 1995) from
Hutton et al. (2009). C_Score is the firm-year accounting conservatism measure estimated from
the model by Khan and Watts (2009). Comparability is the average comparability scores calculated
with all other firms in the same industry, downloaded from Rodrigo Verdi’s website. Following
13 In untabulated sensitivity analyses, we find qualitatively similar results using variations of the guidance frequency
variables: i) the natural logarithm of the number of earnings guidance, and ii) the number of days with at least one
forecast over the course of the year.
17
Kim et al. (2016), we convert raw comparability scores into decile ranks standardized between
zero and one. DRO is the real earnings management measure calculated as a moving three-year
sum of the absolute values of abnormal discretionary expenses and production following Francis
et al. (2016) and 10KFilesize is the size of 10-Ks, downloaded from Bill McDonald's website
(Ertugrul et al. 2017).14 Prior studies predict a positive coefficient on Opaq, DRO, and 10KFilesize,
but negative coefficients on C_Score and Comparability.
Table 3 presents the results of the above regression. In Columns (1) and (2), we document
that stock price crash risk is positively associated with the respective issuance and frequency of
guidance after controlling for various economic and institutional characteristics, industry fixed
effects, and year fixed effects. In the remaining columns, we show that these results are robust to
controlling for different information characteristics. In Columns (3) and (4), we introduce the
financial reporting opacity proxy, Opaq, used in Hutton et al. (2009). We show that our results are
robust to controlling for financial reporting opacity. Consistent with Hutton et al. (2009), we find
a positive coefficient on financial reporting opacity. More importantly, we find that the guidance
variables have significantly positive coefficients. In Columns (5) and (6), we add additional
information characteristics as control variables: Comparability, C_Score, DRO, and 10KFilesize.
We note that the number of observations tend to be much smaller in these columns due to the data
requirements. We continue to find a significant positive association between earnings guidance
and stock price crash risk. In Column (7), we report consistent results from estimating the
regression on a reduced sample with at least one guidance issued during the fiscal year.15
14 DRO is equivalent to DRO_1 in Francis et al. (2016). The results are largely similar if we use the real earnings
management measures from Francis et al. (2016) or those from Khurana et al. (2018). Our results are also
unchanged if we use the 10-K readability measures from Loughran and McDonald (2011). 15 Our results with regard to the information-related variables are not directly comparable to those in prior studies,
for two reasons. First, there are differences in our sample due to the addition of other variables, each with its own
data requirements. Our sample becomes significantly smaller once these variables are added. Second, there is
evidence that the results can vary over time, e.g., before and after SOX (Hutton et al., 2009; Francis et al., 2016).
18
The above results suggest that on average, guidance is related to inflated expectations and
the unravelling of these expectations in the future leads to a stock price crash. This finding is
interesting and possibly counter-intuitive for two reasons. First, prior literature often regards more
disclosure as a reflection of greater transparency, which is expected to preempt a future stock price
crash. Second, the descriptive evidence above on guidance bias indicates that optimistic guidance
is a non-pervasive phenomenon and less frequent than pessimistic guidance. If pessimistic and
optimistic guidance have comparable effects on future crashes, we should observe an average
negative relation between guidance frequency and crash risk. The surprising average positive
effect prompts us to dig deeper into what guidance properties lead to a higher crash risk.
In the next section, we provide sharper analyses to examine the effect of guidance bias on
stock price crash risk. Intuitively, among different types of forecast characteristics, the one that is
most likely to lead to inflated expectations is guidance optimism. Such a focus on optimistic bias
in guidance is also consistent with the notion commonly expressed in the stock price crash risk
literature that when investors are unaware that the firm’s true state is worse than projected, there
will be a higher likelihood of a stock price crash. As noted earlier, regardless of whether
optimistically biased forecasts are the result of managers’ intention to mislead investors or of their
unintentional optimism, the end result is inflated expectations on the part of market participants.
5. Analysis on forecast bias and crash risk
5.1 Guidance optimism and crash risk
In the last section, we show that guidance issuance is positively related to crash risk, but
we are still unclear about the mechanism that underlies this relation. In this section, we closely
examine the relation by investigating the role played by management forecast bias. More
specifically, we examine the inflated expectation hypothesis, which predicts that firms issue
19
guidance that inflates investors’ expectations as a way of camouflaging bad news; a stock price
crash occurs in future periods when investors realize their expectations are inflated.
We first present univariate analyses in Panel A of Table 4 where we divide 71,909 sample
firm-years into three groups based on the frequency of optimistic guidance: no optimistic guidance
(GuideOpt=0), low frequency (1<=GuideOptFreq<4), and high frequency (GuideOptFreq>=4).
For each group, we calculate the percentage of firm-years with stock price crashes in the following
year. We observe that the percentage increases monotonically from the no optimistic guidance
group (20.64%) to the high frequency group (28.47%) and that the differences are highly
significant. We document a similar pattern for subsamples divided by the sign of the bias of the
last guidance of the year. This univariate evidence is in line with the inflated expectation
hypothesis.
Next, we estimate the following model:
𝐶𝑟𝑎𝑠ℎ𝑡+1 = 𝛼 + 𝛽1𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝑡 (𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐹𝑟𝑒𝑞𝑡 𝑜𝑟 𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐿𝑎𝑠𝑡𝑡) + 𝛾 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑠𝑡 + 𝜀𝑡+1. (3)
Table 4, Panel B presents the estimation results. In Column (1), GuideOpt (0.118, z-
stat=6.59) exhibits a significant positive relation. We find similar results in Columns (2) and (3)
where GuideOptFreq and GuideOptLast are significantly related to crash risk. These positive
coefficients are in support of the inflated expectation hypothesis. Unlike the results in Table 3,
which can be alternatively interpreted as the sheer volume of guidance potentially signaling a
future crash, the results in this table shed light on the relation’s direct source in that ex post, the
optimistic bias in guidance tends to successfully blind investors until it gets revealed. 16
16 Because the majority of guidance is either optimistic or pessimistic, we do not separately examine neutral
guidance (6%) in this regression. In untabulated analysis, neutral guidance exhibits no significant relation with crash
risk. In addition, our results are robust to the inclusion of short-horizon guidance, which by itself has a very limited
impact on future crashes. Our results are also robust to the inclusion of quarterly earnings guidance, most of which
(74%) has a short horizon.
20
The crash preemption hypothesis predicts that firms issue non-optimistic guidance to avoid
building up price inflation that leads to a future crash. To further delve into this possibility, we add,
in Columns (4) − (6), GuidePes, GuidePesFreq, and GuidePesLast, which are calculated in the
same manner as the optimistic guidance variables. We find no significant association between the
pessimistic guidance variables and crash risk.17 In Column (7), we use GuideNetOptFreq, which
is GuideOptFreq net of GuidePesFreq, and find similar results to those in Column (2). In summary,
these results lend more support to the inflated expectation hypothesis that optimistic guidance leads
to future crashes, as opposed to the crash preemption hypothesis that predicts that firms issue non-
optimistic guidance to avoid building up an inflated stock price. These results also confirm the
notion that optimistic bias in guidance is the driving force behind the relation between guidance
and crash risk. Should we observe significant positive coefficients on both the optimistic and
pessimistic guidance variables, it might suggest that the relation between guidance and crash risk
is a manifestation of other unobserved factors that drive the overall guidance decision. From now
on, we focus on our main results on GuideOpt, GuideOptfreq, GuideOptLast in further analyses
and sensitivity tests, though untabulated sensitivity tests on Guide and Guidefreq or pessimistic
guidance variables produce similar results.
Further, we examine the stability of our results for the sample period by running separate
regressions for individual years. Panel C presents the coefficients on GuideOpt, GuideOptFreq,
and GuideOptLast, from estimating Eq. (3) in each year. We find that the positive relation is most
pronounced in the 1999-2006 and 2010-2013 sub-periods, while it is insignificant in 6 of the 19
sample years. This evidence indicates that our findings are not restricted to limited years nor that
17 The pessimistic guidance variables are not associated with Crash in the absence of optimistic guidance variables
in the regression. The correlation between GuideOpt and GuidePes is 0.2794 and that between GuideOptFreq and
GuidePesFreq is 0.0861. By construction, there is a negative correlation (-0.1174) between GuideOptLast and
GuidePesLast. These results indicate no multicollinearity between the optimistic and pessimistic guidance variables.
21
they are simply driven by the financial crisis of 2007-2009. In addition, we plot the coefficients
and their confidence intervals in Figure 2. GuideOpt is positively associated with crash risk in all
years except one, GuideOptFreq is positively associated with crash risk in all years, as is
GuideOptLast, except for two years.18
Lastly, we show in Panel D that our main results are robust to estimating other regression
specifications such as Fama-MacBeth and Conditional Logit, as well as to the use of a reduced
sample with at least one guidance issued in a firm-year. In summary, our results lend support to
the notion that optimism in guidance poses a higher stock price crash risk.
5.2 Endogeneity
We recognize that the decision to issue optimistic guidance could be driven by
unobservable firm heterogeneity, which also raises crash risk. While it is difficult to identify
unobservable firm heterogeneity that is unrelated to managers’ opportunism and that would only
affect optimistic guidance, we still attempt to address this potential endogeneity issue with the
following four strategies.
5.2.1 Controlling for other determinants of crashes
To address the potential endogeneity issue that omitted correlated variables might affect
guidance optimism and crash risk at the same time, in Columns (1) – (3) of Table 5, Panel A, we
include the disclosure-related determinants of crash risk the literature documents. Our main result,
that optimism in guidance increases crash risk, is robust to the inclusion of Opaq, Comparability,
C_Score, DRO, and 10KFilesize, although the sample size becomes much smaller due to data
18 Hutton et al. (2009) document that the positive association between accruals-based financial reporting opacity and
stock price crash risk disappears post SOX. Francis et al. (2016) show that real earnings management’s predictive
ability with regard to stock crash risk significantly increases after SOX, while accrual earnings management’s
predictive power declines over the same period. An untabulated test confirms that there is no significant difference
in the relation between guidance optimism and stock price crash risk in the pre- and post-SOX periods.
22
availability.
Next, we consider an alternative explanation that in periods when investor sentiments are
high, manager sentiments could also be high, which would simultaneously affect guidance bias
and stock price inflation. In Columns (4) – (6), we control for investment sentiment from Baker
and Wurgler (2006, 2007). Sentiment is the fiscal-year average of monthly investor sentiment
measure from Wurgler’s website. While we confirm that Sentiment increases crash risk, our main
finding is robust to controlling for Sentiment, which eases the endogeneity concern. We also
explore the possibility that manager’s overconfidence is one of the drivers for our finding, in that
guidance optimism is a manifestation of manager overconfidence, which then affects other
managerial decisions that lead to price crashes. We define Overconfidence as a dummy variable
indicating that a CEO has served for at least two years and the average moneyness of the vested
options she holds is over 67% at least twice in the sample (Campbell et al. 2011). In Columns (7)
– (9), we find that our main finding continues to be robust to controlling for CEO overconfidence,
which is, in itself, positively associated with crash risk. Lastly, we include Overinvestment as
another potential omitted correlated variable that is calculated as the abnormal investment from
estimating a model of the total investment on lagged sales growth following Richardson (2006)
and Biddle et al. (2009). We find that our results are robust to the inclusion of Overinvestment.
5.2.2 Chamberlain’s Random Effects Model to control for firm fixed effects
Our second strategy is to re-estimate the relation between optimistic guidance and crash
risk by controlling for firm fixed effects, which can help mitigate the effects of unobserved firm
heterogeneity. However, because our dependent variable, Crash, is a dummy variable and the
probit model assumes a non-linear relation between the dependent variable and regressors, a
regular fixed effects probit model will produce inconsistent estimates (Wooldridge, 2002, Chapter
23
15). To address this issue, we follow Wooldridge’s (2002) suggestion to estimate Eq. (3) using a
Chamberlain’s Random Effects (CRE) probit model.
The basic idea behind the CRE approach is to model unobserved firm heterogeneity as a
function of the firm-specific average of each time-varying regressor in the panel data. For
implementation, we first calculate the firm-specific mean values of all the time-varying
independent variables in Eq. (3) and then add these firm-specific mean values as additional control
variables to Eq. (3) to address unobserved firm heterogeneity. We present the results in Table 5,
Panel B. Columns (1) − (3) show significant positive coefficients on GuideOpt (0.044, z-
stat=2.24), GuideOptFreq (0.014, z-stat=2.16), and GuideOptLast (0.086, z-stat=2.91). These
results provide further evidence that the positive relation between optimistic guidance and crash
risk is not purely driven by endogeneity.
5.2.3 Exogenous shock from Regulation SHO
As a third strategy, we take advantage of a unique natural experiment introduced by
Regulation SHO, which temporarily suspended the short-sale price tests for 1000 pilot firms
randomly chosen from the Russell 3000 composite and thereby made it easier to short the pilot
firms. The pilot firms were announced in July 2004 and the temporary suspension was in effect
from May 2005 through August 2007 (treatment period). Chen et al. (2014) and Li and Zhang
(2015) show that while Regulation SHO had no significant impact on the pilot firms’ guidance
frequency and bias, it increased short selling pressure and price sensitivity to bad news on such
firms. This increased sensitivity to bad news provides an exogenous shock to the hypothesized
relation between guidance optimism and a stock price crash. If the market reaction to the
realization of disappointing earnings is magnified for the pilot firms, then during the Reg SHO
treatment period, we should observe a stronger relation between guidance optimism and stock
24
price crash risk for these firms than for the control firms. However, the stronger relation should
not be present either prior to or after the treatment period (placebo periods).
Due to data availability on the test variables, our treatment period sample is composed of
2,301 firms in their first fiscal years affected by Reg SHO (2004 or 2005). Out of the 2,301 firms,
736 belong to the pilot group. The pre- (post-) period sample consists of the same set of firms’
fiscal year 2003 (2007) observations. We find results consistent with our predictions, which we
present in Table 5, Panel C. In the first three columns under the treatment period (Columns (1) –
(3)), we show that the positive relation between crash risk and guidance optimism variables hold
on our Russell 3000 sample. In Columns (4) – (6), we show that the pilot firms are more likely to
experience future crashes, consistent with Reg SHO raising short-selling pressure on them. More
importantly, we find positive coefficients on the interaction terms between Pilot and guidance
optimism variables (e.g., 0.025 on GuideOptFreq× Pilot in Column (5)). This result is consistent
with Reg SHO providing an exogenous shock that magnifies the effect of guidance optimism on
crash risk.
In contrast, we do not observe differential effects between the two groups in either the pre
or post period (Columns (7) – (12)), which speaks to the effective randomization in the pilot firm
selection. More importantly, the effect of optimistic guidance on crash is shown to be generally
more pronounced for the pilot firms, which mitigates the endogeneity concern that the relation
could be due to firm-specific characteristics. These results are robust to using a pre-period sample
from fiscal years 2001 or 2002, or a post-period sample from fiscal years 2008 or 2009.
5.2.4 Exogenous shock from Regulation Fair Disclosure
Our last strategy is to consider an exogenous shock to firms’ guidance behavior. Prior
studies on Regulation Fair Disclosure show that the regulation creates incentives for firms to issue
25
more guidance to replace the selective disclosure that Reg FD prohibits (Bailey et al., 2003; Heflin
et al., 2003; Heflin et al., 2016). As shown previously in Table 1, the magnitude of the surge is
dramatic, as the percentage of firms that issue long-horizon annual earnings guidance jumps from
15.67% in 2000 to 26.6% in 2001 and the percentage of firms that issue optimistic guidance rises
from 8.53% to 17.87%. We note that we do not assume that a given manager becomes more or
less optimistic or opportunistic pre- and post-Reg FD. We use Reg FD as an event that increases
the total amount of managerial optimism/opportunism in public disclosure that enters the equity
markets, because those who are supposedly more opportunistic and who relied on private
communication pre-Reg FD would turn to guidance after constraints were placed on private
disclosure.
In addition, the SEC adopted Reg FD to ensure that all investors have equal access to
material corporate disclosures and to boost investors’ confidence in capital markets. It is unlikely
that the regulation would have a direct positive effect on the probability of stock price crashes.
Furthermore, firm disclosures, as well as the availability of public information, remain constant
after Reg FD (Bushee et al., 2004; Francis et al., 2006). Thus, it is also unlikely that it would
indirectly influence future crashes by reducing the sheer amount of public information.
Collectively, we consider Reg FD a reasonable exogenous shock to our setting.
To implement this strategy, we estimate the instrumental variables (IV) probit model.19
Our first stage determinant model for guidance is as follows:
𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝑡 (𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐹𝑟𝑒𝑞𝑡 𝑜𝑟 𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐿𝑎𝑠𝑡𝑡) = 𝛼 + 𝛽1𝑅𝑒𝑔𝐹𝐷 + 𝛾 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑠𝑡 + 𝜀𝑡+1. (4)
The dependent variables and most of the control variables are described in Section 3. RegFD is a
dummy variable equal to one if fiscal year t comes after the effective date of Reg FD (i.e., October
19 Given that the dependent variable in our second stage model is an indicator variable, the conventional 2SLS
method for instrumental variables would produce inconsistent estimates.
26
23, 2000) and zero otherwise. The second stage model focuses on optimistic guidance:
𝐶𝑟𝑎𝑠ℎ𝑡+1 = 𝛼 + 𝛽1𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝑡 (𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐹𝑟𝑒𝑞𝑡 𝑜𝑟 𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐿𝑎𝑠𝑡𝑡) + 𝛾 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑠𝑡 + 𝜀𝑡+1. (5)
The results from an IV probit regression based on the endogenous variable GuideOpt are
presented in the first two columns of Table 5, Panel D. The first column shows the results of the
first stage determinant model. The positive and significant coefficient on RegFD (0.046) indicates
that firms increase guidance frequency after the regulation’s passage. All other significant
regressors have consistent signs, as predicted. Column (2) presents the results of the second stage
regression. The coefficient on GuideOpt (1.994, z-stat=2.38) is statistically significant at the 0.05
level. The next two columns present the results from an IV probit regression based on the
endogenous variable GuideOptFreq. GuideOptFreq (1.131, z-stat=2.84) exhibits a slightly
stronger result than GuideOpt, but the overall implications are largely consistent. Lastly, in
Column (6), the coefficient on GuideOptLast (2.387, z-stat=2.51) is statistically significant at the
0.05 level. The Wald tests also suggest that endogeneity is not a severe problem for either variable.
In sum, the results presented in Panel D indicate that the positive relation between optimistic
guidance and crash risk is not purely driven by endogeneity.
A caveat with using Reg FD as an exogenous shock is that the adoption was contaminated
with many market-wide events, such as the collapse of Enron, WorldCom, etc. It is possible that
our finding of more post-Reg FD crashes is driven by such market-level shocks. While our crash
risk measure is, by construction, both market and industry adjusted, which might alleviate this
concern to a certain extent, we acknowledge that it is not perfect and can still pick up effects from
market-wide crashes.
5.3 Other guidance characteristics
5.3.1. Joint optimism with analysts
We examine whether a subsequent analyst forecast that reiterates or contrasts with the
27
optimistically biased guidance can exacerbate the relation between guidance optimism and crash
risk. We first create three variables: JointOpt is a dummy variable indicating that at least one
optimistic guidance is followed by an optimistic analyst forecast. JointOptFreq is the number of
optimistic guidance with a subsequent optimistic analyst forecast. JointOptLast is a dummy
variable equaling one if the last guidance in fiscal year t is optimistically biased and followed by
an optimistic analyst forecast, zero otherwise. The results are reported in Table 6, Panel A. The
coefficients on JointOpt (0.136), JointOptFreq (0.046), and JointOptLast (0.309) are higher than
their counterparties, GuideOpt (0.118), GuideOptFreq (0.037), and GuideOptLast (0.155), from
Table 4, Panel B. The results indicate that there is higher crash risk when managers’ optimism is
subsequently followed by analysts’ optimistic bias about the firm’s future performance.
5.3.2. Bundled vs. non-bundled guidance, reaffirming vs. updating guidance, and guidance from
routine vs. sporadic guiders.
Table 6, Panel B presents the results from estimating the main test based on other guidance
characteristics the literature examines. First we construct separate sets of GuideOpt,
GuideOptFreq, and GuideOptLast using bundled vs. non-bundled guidance, and reaffirming vs.
updating guidance. Guidance is defined as bundled if it is issued concurrently with an earnings
announcement (Rogers and Van Buskirk, 2013). While we do not differentiate between bundled
and non-bundled guidance in our analysis, we note that prior studies focus on non-bundled
guidance (e.g., Rogers et al., 2009). We define guidance as reaffirming if the forecasted earnings
are within the ± 10% range of previously guided earnings (Clement et al., 2003). We find no
differential effects on stock price crash risk. Lastly, we construct separate sets of GuideOpt,
GuideOptFreq, and GuideOptLast using guidance by a routine vs. sporadic guider. On a guidance
day, a firm is a r guider if it issued any guidance in three out of the four calendar quarters prior to
28
that date (Rogers et al., 2009). We find that both routine and sporadic forecasts are significantly
and positively related to crash risk and that their magnitudes are comparable. While firm-specific
economic shocks could help explain the issuance of sporadic guidance, this is less likely to be true
for routine guiders. This evidence mitigates the concern that our result is purely driven by
idiosyncratic economic shocks.
5.4 Intentional vs. unintentional bias in guidance
While the previous sections provide evidence on a significant relation between optimistic
guidance and crash risk that supports the inflated expectation hypothesis, we have yet to
differentiate between intentional and unintentional bias in guidance. In this section, we conduct
additional tests to investigate these two sources of guidance optimism and their relation with crash
risk.
5.4.1 Truthful managers’ honest mistakes
Truthful managers could make honest mistakes in response to preexisting market
optimism, leading to a positive relation between unintentional optimistic guidance and crash risk.
This contrasts with intentional optimism, which is driven by career concerns. We attempt to
capture unintentional bias with a mechanism that could tame its intentional counterpart. Prior
literature has argued that when providing guidance, higher litigation risk results in a lower
propensity to engage in the intentional suppression of bad news (Cao and Narayanamoorthy,
2011). LitRisk_High indicates a higher litigation risk calculated from Kim and Skinner (2012)’s
model of the predicted likelihood of being sued. We expect that when the litigation risk is high,
optimistic guidance issued is more likely to be unintentional. In Table 7, Panel A, we find negative
coefficients on most interaction terms between the guidance optimism variables and LitRisk_High.
Such results indicate that when bias is more likely unintentional, the association between guidance
29
optimism and crash risk is mitigated.
5.4.2 Serial correlation in forecast bias
Gong et al. (2011) show that forecast bias exhibits serial correlation and can be attributed
to managers’ unintentional information processing bias rather than to opportunistic forecasting
behavior. To address this concern, we decompose GuideOpt, GuideOptFreq, and GuideOptLast
into two components: serial correlation and innovation in the current year. For example, for
GuideOpt, we first choose firms with at least 10 years of observations and estimate a 10-year
rolling AR(1) model. We then obtain the residual, RGuideOptt, to proxy for the innovation in
upward guidance bias, whereas PGuideOptt proxies for the predicted bias from the model. We then
estimate the following model:
𝐶𝑟𝑎𝑠ℎ𝑡+1 = 𝛼 + 𝛽1𝑅𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝑡 (𝑅𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐹𝑟𝑒𝑞𝑡 𝑜𝑟 𝑅𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐿𝑎𝑠𝑡𝑡)
+𝛽2𝑃𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝑡 (𝑃𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐹𝑟𝑒𝑞𝑡 𝑜𝑟 𝑃𝐺𝑢𝑖𝑑𝑒𝑂𝑝𝑡𝐿𝑎𝑠𝑡𝑡) + 𝛾 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑠𝑡 + 𝜀𝑡+1. (6)
We assume that predicted guidance optimism reflects inherent optimism, possibly due to factors
such as innate managerial optimism and the growth status of the firm. We assume that residual
guidance optimism arises from management choice and might capture intentional bias on the
manager’s part.20 If crash risk is purely driven by managers’ unintentional information processing
bias, we would expect an insignificant result on β1. In Table 7, Panel B, we find significant positive
coefficients on RGuideOpt, RGuideOptFreq, and RGuideOptLast, indicating that forecast bias due
to managers’ opportunistic disclosure contributes to crash risk. We control for PGuideOpt,
PGuideOptFreq, and PGuideOptLast in their respective models and also find positive coefficients,
which suggest that unintentional information processing bias also leads to a higher crash risk.
20 Just like any measure of discretionary disclosure based on a regression model of non-discretion, the validity of the
inference about discretion is contingent upon the regression model’s ability to adequately capture non-discretion.
30
6. Other supplemental tests
6.1. Does forecast bias get impounded into price?
Even if managers issue optimistic guidance with the intention of misleading markets,
investors could perfectly undo this bias by discounting forecast news, which would render the bias
harmless. We test this possibility by directly examining the market reaction to guidance issued
during the twelve quarters before crash year t+1. Specifically, we test whether the bias portion of
guidance news gets impounded into the stock price. If investors can unravel bias, we would expect
the markets to assign less value to forecast bias in the last four quarters. Therefore, we explicitly
test market reactions to forecast bias with the following models:
𝐶𝐴𝑅𝑡+1 = 𝛼 + 𝛽1𝑇𝑟𝑢𝑒𝑛𝑒𝑤𝑠𝑡 + 𝛽2𝐺𝑢𝑖𝑑𝑒𝑏𝑖𝑎𝑠𝑡 + 𝛽3𝐿𝑎𝑠𝑡𝑄𝑡 + 𝛽4𝑇𝑟𝑢𝑒𝑛𝑒𝑤𝑠𝑡 ∗ 𝐿𝑎𝑠𝑡𝑄𝑡
+𝛽5𝐺𝑢𝑖𝑑𝑒𝑏𝑖𝑎𝑠𝑡 ∗ 𝐿𝑎𝑠𝑡𝑄𝑡 + 𝛽6𝐶𝑜𝑛𝑐𝐸𝐴𝑛𝑒𝑤𝑠𝑡 + 𝜀𝑡+1, (7)
where CAR is the five day (day -2 through day +2) cumulative abnormal return around the last
long-horizon guidance issued in fiscal quarter t. We decompose guidance news into two
components: TrueNews, measured as realized earnings minus the pre-window consensus analyst
forecasts, and GuideBias, measured as the management forecast minus the realized earnings. Both
true news and guidance bias are scaled by the pre-window stock price on trading day -3. Last4Q
is a dummy variable indicating whether guidance is issued in the last four fiscal quarters prior to
the crash year. ConcEANews is the earnings surprise of a concurrent earnings announcement for
bundled forecasts. If investors are unable to see through the guidance bias, β2 will be significantly
positive and β5 will not be different from zero. Otherwise, β2 would be insignificant.
The regression results are presented in Table 8, Panel A. Columns (1) – (6) present the
results that estimate Eq. (7) without interaction terms. In all columns, while we find a larger
coefficient on TrueNews, the coefficient on GuideBias is also significantly positive, regardless of
31
the sign of GuideBias or whether the guidance is bundled. In Columns (7) – (12), the interaction
term GuideBias × Last4Q is significantly different from zero if the guidance is non-bundled with
an optimistic bias. Together, these results provide direct evidence that investors fail to undo bias
when there is a shift in managers’ guidance incentives and optimistic forecast bias gets impounded
into price, which leads to future crashes.
6.2 One-year-ahead returns.
In addition to future stock crashes, we examine the general market consequences in the
following year. In Table 8, Panel B, we show that optimistic guidance in year t is associated with
lower cumulative returns in year t+1. For example, in Column (1), GuideOpt has a negative
coefficient (-0.032, t-stat= -2.48). While this result indicates that guidance optimism can lead to
long term negative shareholder returns, there is an important distinction between such negative
returns and large occasional crashes.
Finance theory suggests that investors have a strong aversion to large occasional crashes
and demand extra compensation for bearing such crash risk (Bates, 1991; Pan, 2002). Empirical
studies provide consistent evidence of a large positive risk premium on stocks with a high crash
risk, even after controlling for volatility or skewness (e.g., Bollerslev and Todorov, 2011; Conrad,
Dittmar, and Ghysels, 2013; Santa-Clara and Yan, 2010). For example, Bollerslev and Tohdorov
(2011) show that "the market generally incorporates the possible occurrence of rare disasters in
the way it prices risky payoffs, and the fear of such events accounts for a surprisingly large fraction
of the historically observed equity and variance risk premia." In addition, crash risk is also related
to the "volatility smirk" in the option market. Specifically, while the Black-Scholes model suggests
that every option on the same underlying stock should have the same implied volatility, the implied
volatility on out-of-the-money put options (which are subject to crash risk) is higher than the
32
implied volatility on at-the-money call options (which are less susceptible to crashes). Therefore,
occasional large crashes also have important and unique implications for the option market. We
expect our findings to be relevant to investors in both stock and option markets.
6.3 First Call CIG coverage issue
Our guidance sample is the union of the First Call CIG and I/B/E/S guidance. The CIG
database was discontinued in 2011, whereas I/B/E/S covers our full sample period. During 1997 –
2011, when the databases’ coverage overlaps, 90% of CIG coverage is also covered by I/B/E/S,
and 93% of I/B/E/S coverage is also covered by CIG. In Table 8, Panel C, we conduct our main
test using CIG- and I/B/E/S-covered guidance separately and confirm that our result is not affected
by CIG coverage discontinuation or by the choice between CIG and I/B/E/S.
On a separate note, Chuk et al. (2013) suggest that the CIG database has a symmetric
coverage bias. For example, it has better coverage of firms with more analysts following and higher
institutional ownership. This raises a concern about whether our finding on the relation between
guidance and crash risk is driven by coverage bias. However, the firm coverage bias cannot explain
optimistic and pessimistic guidance’s differential effects on crash risk because they are subject to
the same coverage issue. Nevertheless, we follow Chuk et al.’s (2013) suggestion to sort our
sample firms into quintiles based on analyst following and find a significant positive relation
between guidance and crash risk across all five groups (untabulated).
6.4 Downward expectation management guidance
While we document in Table 3, Panel A that pessimistic guidance variables do not, on
average, reduce stock price risk, it is difficult to fully dismiss the preemptive role of guidance, as
it is plausible that in specific settings, managers can guide the market’s expectations downward,
reducing crash risk. We attempt to identify the specific setting by utilizing the guidance
33
classification scheme by Kim and Park (2012), who sort guidance into three categories: guidance
that is more optimistic but not more accurate than analysts’ forecasts (upward expectation
management), guidance that is more accurate than analysts’ (communication), and guidance that
is either pessimistic when analysts are optimistic or more pessimistic than analysts’ pessimistic
forecasts (downward expectation management). We construct a set of guidance variables
separately for each category of guidance and label them UEM, Comm, and DEM, respectively. For
example, GuideUEMFreq is the number of guidance classified as managing expectations upward.
In Table 8, Panel D, we show that GuideUEM, GuideUEMFreq, and GuideUEMLast, which by
definition are all constructed with optimistically biased guidance, are also all positively associated
with crash risk. In contrast, GuideDEM and GuideDEMFreq decrease crash risk. These results
suggest that pessimistic guidance reduces crash risk within a very specific context in which
managers are either even more pessimistic than pessimistic analysts or they are pessimistic when
analysts are optimistic.
Interestingly, we also find that when guidance is issued for communication purposes
(GuideCommFreq), there is still a higher crash risk. This result suggest that even when managers
are trying to lead market expectations closer to actual earnings, which could be a good example of
unintentional optimism, the optimistic bias leads to more crashes. This adds to the evidence on
both intentional and unintentional optimism discussed in Section 5.4.
7. Conclusion
The relation between corporate disclosure and financial stability is an important issue that
has attracted significant attention from regulators, practitioners, and academics. The evidence from
recent studies suggests that various characteristics (e.g., financial reporting opacity, accounting
conservatism, and accounting comparability) of the earnings generation process are associated
34
with stock price crash risk. However, given the importance of voluntary disclosure such as
management earnings guidance in explaining the variation in stock returns, we extend prior studies
by documenting a significant positive relation between guidance and crash risk that is incremental
to the information characteristics examined in the prior literature.
A key feature of our analysis is that we provide direct evidence on how disclosure bias,
specifically guidance optimism, gets impounded into stock prices, which, in turn, leads to a future
stock price crash. This result is robust to a battery of tests that aim to address the endogeneity issue,
including year-by-year analysis, controlling for firm fixed effects, and relying on exogenous
shocks from Reg SHO and Reg FD. We also show that both intentional and unintentional guidance
optimism contributes to inflated expectations and future crashes, though the relation on
unintentional optimism is relatively weaker. Further, while we could not find evidence that
pessimistic guidance preempts future crashes in a general sample, we find some indicators that it
could in a very specific context.
As a last note, we emphasize that our findings do not suggest that providing guidance is
necessarily detrimental for the equity markets, especially since there is evidence that guidance has
many positive equity market effects, such as reducing information asymmetry and mitigating
litigation risk. In contrast, our results indicate that on average, guidance, particularly optimistic
guidance, is associated with the higher risk of a future price crash. An analogy to our finding would
be where there are cars, particularly speeding cars, there is a higher likelihood of a crash.
The evidence we provide is important in light of i) Hutton et al. (2009)’s finding on the
relation between financial reporting opacity and stock price crash risk, ii) the significant tension
in the hypothesis about the relation between guidance and stock price crashes, and iii) related, the
typical view that more disclosure equates with better disclosure, which would lead to better capital
35
market outcomes. Overall, we believe that that our paper sheds new and important insight into the
impact of disclosure on the equity markets.
36
Appendix 1 Variable Definitions
Outcome variable (t+1)
Crash A crash in stock price, measured following Hutton et al. (2009). Specifically, for
each firm-year observation with at least 26 weekly stock returns in the following
fiscal year (i.e., year t+1), we estimate the following firm-specific regression:
ri,w = β0 + β1 rmkt,w-1 + β2 rmkt,w + β3 rmkt,w+1 + β4 rind,w-1 + β5 rind,w + β6 rind,w+1 + εi,w,
where ri,w is the current weekly return for firm i; rmkt,w (rmkt,w-1, rmkt,w+1) is the weekly
market return in the current (prior, next) week; and rind,w (rind,w-1, rind,w+1) is the
weekly industry return in the current (prior, next) week.21 We then compute Wi,w,
the natural logarithm of one plus the residual return, εi,w, from the regression;
Crash is a dummy variable equaling one if, in year t+1, there is at least one
extremely low Wi,w, which is defined as a Wi,w smaller than [Mean (Wi,w) - 3.09 ×
Std Dev (Wi,w)] and zero otherwise.
Disclosure variables (t)
Guide Indicator variable equaling one if a firm issued at least one annual earnings
forecast in fiscal year t, for earnings announced after year t (long-horizon
guidance).
GuideFreq The number of annual earnings forecasts issued over the course of fiscal year t, for
earnings announced after year t (long-horizon guidance). All the guidance
variables defined below are based on the annual long-horizon earnings guidance.
GuideOpt Indicator variable equaling one if a firm issued at least one optimistic forecast in
fiscal year t. A forecast is defined as optimistic if the actual earnings fall short of
the forecast value or the minimum of the forecast range.
GuideOptFreq The number of optimistic earnings forecasts issued in fiscal year t.
GuideOptLast Indicator variable equaling one if the last guidance issued in fiscal year t is
optimistic.
GuidePes Indicator variable equaling one if a firm issued at least one pessimistic forecast in
fiscal year t. A forecast is defined as pessimistic if the actual earnings exceed the
forecast value or the maximum of the forecast range.
GuidePesFreq The number of pessimistic earnings forecasts issued in fiscal year t.
GuidePesLast Indicator variable equaling one if the last guidance issued in fiscal year t is
pessimistic.
JointOpt Indicator variable equaling one if a firm issued at least one optimistic forecast in
fiscal year t that is followed by optimistic analyst forecasts.
JointOptFreq The number of optimistic earnings forecasts issued in fiscal year t, followed by
optimistic analyst forecasts.
JointOptLast Indicator variable equaling one if the last guidance issued in fiscal year t is
optimistic and followed by optimistic analyst forecasts.
RGuideOpt Residual value from a 10-year rolling autoregressive model of GuideOpt.
RGuideOptFreq Residual value from a 10-year rolling autoregressive model of GuideOptFreq.
RGuideOptLast Residual value from a 10-year rolling autoregressive model of GuideOptLast.
PGuideOpt Predicted value from a 10-year rolling autoregressive model of GuideOpt.
PGuideOptFreq Predicted value from a 10-year rolling autoregressive model of GuideOptFreq.
PGuideOptLast Predicted value from a 10-year rolling autoregressive model of GuideOptLast.
21 The weekly stock (market) returns are computed using the daily stock (value-weighted market) returns from CRSP. The weekly value-weighted
Fama-French industry returns are computed using the daily industry returns available from Kenneth French’s website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
37
GuideUEM
Indicator variable equaling one if a firm issued at least one guidance that is more
optimistic but less accurate than the analyst consensus in fiscal year t (Kim and
Park, 2012). GuideUEMFreq and GuideUEMLast are the frequency and last of the
fiscal year dummy for the variable, respectively.
GuideComm
Indicator variable equaling one if a firm issued at least one guidance that is more
accurate in fiscal year t (Kim and Park, 2012). GuideCommFreq and
GuideCommLast are the frequency and the last of the fiscal year dummy for the
variable, respectively.
GuideDEM
Indicator variable equaling one if a firm issued at least one guidance that is more
pessimistic but less accurate than the analyst consensus in fiscal year t (Kim and
Park, 2012).GuideCommFreq and GuideCommLast are the frequency and the last
of the fiscal year dummy for the variable, respectively.
Control variables (t)
ROE Ratio of net income to the book value of equity at the fiscal year end (Compustat
IB/CEQ).
SIZE Natural logarithm of the market value of equity at the fiscal year end (log of CRSP
Abs(PRC)/SHROUT ).
MB Ratio of the market value of equity (CRSP Abs(PRC)/SHROUT) to the book value
of equity at the fiscal year end (Compustat CEQ * 1000).
Leverage Ratio of total debt to total assets (Compustat (DLTT+DLC)/AT).
MeanRet Average of weekly returns.
StdRet Standard deviation of weekly returns.
Insiderown Shares owned by insiders as a percentage of the total shares outstanding.
Nanalyst Number of analysts issuing one-year-ahead EPS forecasts at the end of fiscal year
t.
Instown % of Institutional ownership.
Nseg Number of business segments in which the firm operates.
Other variables (t)
Sentiment Fiscal year average of monthly investor sentiment (Baker and Wurgler, 2006,
2007), downloaded from Jeffery Wurgler's webpage
(http://people.stern.nyu.edu/jwurgler/).
Overconfidence Indicator variable equaling one if a CEO has served for at least two years and the
average moneyness of the vested options held by the CEO is over 67%, at least
twice in the sample (Campbell et al., 2011), zero otherwise. The moneyness of
options is the ratio of the end of the fiscal year stock price to the average exercise
price, minus one.
Overinvestment Abnormal investment calculated as the residual from the model of total investment
on lagged sales growth, adjusted with the abnormal industry average investment
(Richardson, 2006; Biddle et al., 2009).
Pilot Indicator variable equaling one for the short sale restriction pilot test firms and
zero for the other firms in the Russell 3000 index during the pilot test period.
RegFD Indicator variable equaling one if fiscal year t is after the effective date of
Regulation Fair Disclosure (i.e., October 2000), zero otherwise.
LitRisk_High Indicator variable equaling one for a higher-than-fiscal-year median of the
predicted litigation risk estimated from a probit model of the likelihood of being
sued (Kim and Skinner, 2012), zero otherwise.
Opaq Moving three-year average of the absolute values of abnormal discretionary
accruals measured from the modified Jones model (Hutton et al., 2009).
38
Comparability The average comparability scores with the four firms that are most comparable
(m4_acctcomp) with firm i, downloaded from Rodrigo Verdi’s webpage
(http://www.mit.edu/~rverdi/). Following Kim et al. (2016), we convert raw scores
into decile ranks standardized between zero and one.
C_Score The firm-year conservatism measure estimated from the model by Khan and Watts
(2009).
DRO Deviation in real operations calculated as a moving three-year sum of the absolute
values of abnormal discretionary expenses and production following Francis et al.
(2016).
10Kfilesize Log of the gross 10-K file size in megabytes (Loughran and McDonald, 2011),
downloaded from Bill McDonald's webpage (https://sraf.nd.edu/data/).
Event-study variables (quarterly)
CAR Cumulative abnormal return over a five day window surrounding a management
forecast.
Last4Q Indicator variable equaling one if the quarter to which a guidance issuance date
belongs is one of the last four fiscal quarters prior to the fiscal year with stock
price crashes, and zero otherwise.
GuideBias Bias in management forecasts, measured as management forecast minus actual
earnings, scaled by the pre-window stock price on trading day -3.
TrueNews True news in management forecasts, measured as actual earnings minus the pre-
guidance consensus analyst estimates, scaled by the pre-window stock price on
trading day -3.
ConcEANews Quarterly earnings news announced on the same day as a bundled earnings
guidance (Rogers and Van Buskirk, 2013), measured as actual quarterly earnings
minus the most recent analyst consensus (or quarterly guidance if there is no
analyst consensus, or (q-4) earnings if there is neither analyst consensus nor
guidance), scaled by the pre-window stock price on trading day -3.
39
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46
Figure 1 Management guidance bias distribution This figure plots frequencies of annual long-horizon guidance in each bias interval with width of 0.0005 between the
10% and 90% percentile bias range (-0.0113, 0.0277). Bias is defined as (forecast EPS-Actual EPS) deflated by the
fiscal year end stock price. Zero on the horizontal axis is marked by a dotted line.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
-0.0105 -0.005 0 0.0055 0.011 0.0165 0.022
Fre
qu
en
cy
Guidance Bias
47
Figure 2 The relation between stock price risk (t+1) and guidance optimism (t) by year This graph depicts the coefficient estimates and the associated 95% confidence intervals, from a probit regression of
crash risk on guidance optimism estimated by year.
Panel A: Coefficient estimates from Table 4, Panel B, Column (1) and their confidence intervals.
Panel B: Coefficient estimates from Table 4, Panel B, Column (2) and their confidence intervals.
Panel C: Coefficient estimates from Table 4, Panel B, Column (3) and their confidence intervals.
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
GuideOpt
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
GuideOptFreq
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
GuideOptLast
48
Table 1 Distribution of sample firms
This table presents the distribution of firms across the years for the sample period from 1997 to 2012.
Year
Number of
firms
Number of
firms with at
least one
guidance
Percentage of
firms with at
least one
guidance
Number of firms
with at least one
optimistic
guidance
Percentage of firms
with at least one
optimistic guidance
1997 5,716 365 6.39% 171 2.99%
1998 5,375 635 11.81% 308 5.73%
1999 5,174 715 13.82% 327 6.32%
2000 4,946 775 15.67% 422 8.53%
2001 4,293 1,142 26.60% 767 17.87%
2002 3,873 1,145 29.56% 656 16.94%
2003 3,721 1,176 31.60% 679 18.25%
2004 3,766 1,288 34.20% 737 19.57%
2005 3,674 1,117 30.40% 678 18.45%
2006 3,637 1,133 31.15% 628 17.27%
2007 3,588 1,062 29.60% 591 16.47%
2008 3,319 949 28.59% 651 19.61%
2009 3,057 750 24.53% 346 11.32%
2010 3,050 771 25.28% 288 9.44%
2011 2,962 790 26.67% 359 12.12%
2012 2,855 813 28.48% 429 15.03%
2013 2,908 802 27.58% 438 15.06%
2014 3,036 807 26.58% 408 13.44%
2015 2,959 740 25.01% 356 12.03%
Total 71,909 16,975 9,239
49
Table 2 Descriptive statistics Panel A: Descriptive statistics This table presents the descriptive statistics of the variables in the main analysis. Variable definitions are in Appendix
1.
Main variables N Mean Std Dev Minimum 25th Pctl Median 75th Pctl Maximum
Crash 71,909 0.215 0.411 0 0 0 0 1
ROE 71,909 -0.079 0.856 -7.781 -0.100 0.067 0.150 6.005
Size 71,909 12.671 1.998 8.156 11.223 12.594 13.978 18.567
MB 71,909 2.992 5.513 -44.961 1.108 1.921 3.492 58.122
Leverage 71,909 0.207 0.215 0 0.010 0.152 0.332 1.118
MeanRet 71,909 0.003 0.013 -0.060 -0.003 0.003 0.008 0.092
StdRet 71,909 0.079 0.045 0.013 0.046 0.067 0.098 0.336
Insiderown 71,909 0.010 0.037 0 0 0 0.001 0.335
Nanalyst 71,909 1.419 0.981 0 0.693 1.386 2.197 3.555
Instown 71,909 0.391 0.331 0 0.026 0.367 0.694 1
Nseg 71,909 5.066 4.168 1 3 3 8 20
Guide 71,909 0.236 0.425 0 0 0 0 1
GuideFreq 71,909 0.855 1.860 0 0 0 0 10
GuideOpt 71,909 0.128 0.335 0 0 0 0 1
GuideOptFreq 71,909 0.328 1.018 0 0 0 0 7
GuideOptLast 71,909 0.087 0.282 0 0 0 0 1
50
Table 2 (continued) Panel B: Correlation – Main variables
Crash GuideOpt
GuideOpt
Freq
GuideOpt
Last ROE Size MB Leverage MeanRet StdRet Insiderown Nanalyst Instown Nseg
Crash 0.053 0.053 0.049 0.019 0.063 0.011 -0.016 0.024 -0.050 0.008 0.076 0.067 -0.002
GuideOpt 0.053 0.839 0.805 0.054 0.227 0.011 0.025 -0.063 -0.122 -0.005 0.236 0.216 0.102
GuideOptFreq 0.054 0.997 0.691 0.053 0.221 0.009 0.033 -0.066 -0.129 -0.010 0.218 0.208 0.101
GuideOptLast 0.049 0.805 0.804 0.035 0.150 0.009 0.021 -0.056 -0.075 0.005 0.164 0.146 0.069
ROE 0.034 0.097 0.098 0.056 0.148 -0.415 0.033 0.098 -0.245 0.009 0.117 0.142 0.076
Size 0.071 0.235 0.238 0.158 0.312 0.111 0.070 -0.098 -0.394 -0.067 0.803 0.521 0.231
MB 0.033 0.072 0.072 0.050 -0.043 0.294 -0.083 -0.090 0.112 0.018 0.061 -0.038 -0.070
Leverage -0.016 0.056 0.058 0.044 0.084 0.114 -0.142 -0.062 -0.025 -0.032 0.053 0.025 0.053
MeanRet 0.026 -0.073 -0.075 -0.066 0.178 -0.066 -0.171 -0.054 0.139 0.022 -0.011 0.013 0.006
StdRet -0.043 -0.129 -0.134 -0.075 -0.378 -0.444 0.051 -0.078 0.010 0.031 -0.274 -0.355 -0.175
Insiderown 0.038 0.074 0.076 0.051 0.096 0.168 0.107 -0.040 0.035 -0.087 -0.051 -0.055 -0.012
Nanalyst 0.078 0.238 0.240 0.166 0.249 0.806 0.243 0.088 0.013 -0.289 0.177 0.524 0.102
Instown 0.062 0.203 0.206 0.136 0.236 0.507 0.032 0.047 0.039 -0.356 0.181 0.488 0.128
Nseg 0.005 0.090 0.091 0.063 0.099 0.177 -0.092 0.074 0.017 -0.159 0.079 0.074 0.103
51
Table 3 Stock price crash risk and guidance
This table presents probit regressions on variables from prior studies in the presence of guidance in our main sample.
Variable definitions are in Appendix 1. Year and industry dummies are included in all the regressions but their
coefficients are not tabulated. Standard errors are clustered by firm and by year. The z-statistic for each coefficient is
provided in parentheses below. Significance levels are based on two-tailed tests. ***, **, and * denote significance at
the 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6) (7)
Intercept -0.489*** -0.488*** -0.604*** -0.604*** -0.637*** -0.635*** -0.444
(-4.32) (-4.26) (-4.96) (-4.88) (-2.73) (-2.71) (-1.40)
Guide 0.069*** 0.063*** 0.090***
(2.99) (2.77) (3.63) GuideFreq 0.011** 0.010** 0.016*** 0.014*
(2.10) (2.02) (2.81) (1.71)
Opaq 0.104*** 0.103*** 0.163*** 0.162*** 0.151
(3.12) (3.10) (3.06) (3.03) (1.25)
Opaq2 -0.011*** -0.011*** -0.015*** -0.015*** -0.032***
(-3.08) (-3.07) (-2.94) (-2.93) (-2.85)
Comparability -0.084** -0.082* -0.029
(-2.02) (-1.95) (-0.45)
C_Score -0.032 -0.025 -0.076
(-0.39) (-0.29) (-0.61)
DRO 0.003 0.003 0.014
(0.21) (0.21) (0.85)
10KFilesize 0.017 0.016 -0.017
(1.05) (1.02) (-0.53)
ROE 0.016** 0.016** 0.013* 0.014* 0.017 0.017 0.157***
(2.11) (2.15) (1.91) (1.94) (0.77) (0.79) (3.29)
Size -0.013 -0.013 -0.009 -0.009 -0.015 -0.015 -0.031
(-1.38) (-1.37) (-0.86) (-0.85) (-0.88) (-0.89) (-1.25)
MB 0.003*** 0.003*** 0.002 0.002 0.003 0.003 -0.003
(2.63) (2.64) (1.52) (1.53) (1.57) (1.56) (-0.94)
Leverage 0.008 0.007 0.016 0.015 -0.012 -0.013 0.032
(0.24) (0.24) (0.49) (0.46) (-0.22) (-0.23) (0.27)
MeanRet 3.581*** 3.556*** 4.343*** 4.310*** 5.219*** 5.125*** -3.028
(4.69) (4.64) (5.30) (5.24) (4.09) (4.03) (-1.22)
StdRet -0.919*** -0.935*** -1.195*** -1.209*** -1.591*** -1.599*** -0.702
(-3.24) (-3.32) (-3.63) (-3.68) (-2.95) (-2.95) (-0.71)
Insiderown 0.310** 0.318** 0.369*** 0.377*** 0.362 0.371 -0.282
(2.32) (2.36) (2.91) (2.96) (1.29) (1.31) (-0.41)
Nanalyst 0.091*** 0.095*** 0.092*** 0.095*** 0.088*** 0.091*** 0.051
(6.88) (7.36) (7.23) (7.67) (3.95) (4.10) (1.32)
Instown 0.059*** 0.063*** 0.074*** 0.078*** 0.070 0.075* 0.115*
(2.73) (2.93) (2.72) (2.88) (1.59) (1.74) (1.84)
NSeg -0.006*** -0.006*** -0.006*** -0.006*** -0.009*** -0.009*** -0.005**
(-3.65) (-3.59) (-3.26) (-3.26) (-3.78) (-3.82) (-2.11)
Observations 71,909 71,909 58,469 58,469 22,492 22,492 6,485
Pseudo R-squared 0.0204 0.0202 0.0221 0.0220 0.0207 0.0205 0.0180
52
Table 4 Stock price crash and guidance optimism Panel A: Guidance optimism and stock price crash risk
This table presents a univariate analysis of the relation between a stock price crash and optimistic guidance frequency.
The median GuideOptFreq value among observations with GuideOptFreq > 0 is used as a cutoff for high/low
frequency.
Number of firm-
years
Percentage of
firms with Crash
= 1
Return in year
t+1
GuideOpt = 0 62,670 20.64% 11.92%
GuideOpt = 1 9,239 27.18% 10.58%
Difference 6.54% -1.34%
t-stat (14.31) *** (-1.61)
GuideOptFreq = Low 3,285 24.84% 9.42%
GuideOptFreq = High 5,954 28.47% 11.22%
Difference b/w High and 0 groups 7.83% -0.70%
t-stat (14.11) *** (-0.69)
Difference b/w Low and 0 groups 4.20% -2.50%
t-stat (5.78) *** (-1.84) *
Difference b/w High and Low groups 3.63% 1.80%
t-stat (3.75) *** (1.46)
GuideOptLast = 0 65,643 20.85% 12.06%
GuideOptLast = 1 6,266 28.07% 8.42%
Difference 7.22% -3.65%
t-stat (13.32) *** (-3.71) ***
53
Table 4 (continued) Panel B: Probit regression using the full sample (1997 – 2015)
This table presents the relation between a stock price crash and guidance optimism using a probit model. Variable
definitions are in Appendix 1. Year and industry dummies are included in all the regressions but their coefficients are
not tabulated. Standard errors are clustered by firm and by year. The z-statistic for each coefficient is provided in
parentheses below. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively.
(1) (2) (3) (4) (5) (6) (7)
Intercept -0.492*** -0.493*** -0.500*** -0.497*** -0.500*** -0.502*** -0.520***
(-4.31) (-4.31) (-4.41) (-4.40) (-4.40) (-4.43) (-4.62)
GuideOpt 0.118*** 0.122***
(6.59) (6.87) GuideOptFreq 0.037*** 0.036***
(6.05) (5.93) GuideOptLast 0.155*** 0.153***
(5.75) (5.36) GuidePes -0.026
(-1.00) GuidePesFreq -0.006
(-0.81) GuidePesLast -0.008
(-0.24) GuideNetOptFreq 0.018***
(3.79)
ROE 0.016** 0.016** 0.016** 0.017** 0.017** 0.016** 0.017**
(2.10) (2.11) (2.11) (2.16) (2.15) (2.14) (2.23)
Size -0.012 -0.012 -0.012 -0.012 -0.012 -0.011 -0.010
(-1.30) (-1.31) (-1.23) (-1.26) (-1.25) (-1.21) (-1.07)
MB 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003***
(2.64) (2.63) (2.64) (2.67) (2.65) (2.64) (2.75)
Leverage 0.007 0.006 0.007 0.007 0.007 0.007 0.012
(0.22) (0.19) (0.23) (0.24) (0.22) (0.24) (0.39)
MeanRet 3.816*** 3.788*** 3.816*** 3.876*** 3.840*** 3.826*** 3.848***
(4.87) (4.82) (4.83) (4.94) (4.91) (4.84) (4.91)
STDRet -0.963*** -0.965*** -0.970*** -0.981*** -0.980*** -0.974*** -1.011***
(-3.43) (-3.45) (-3.45) (-3.42) (-3.44) (-3.38) (-3.66)
Insiderown 0.310** 0.318** 0.301** 0.309** 0.317** 0.301** 0.312**
(2.31) (2.36) (2.26) (2.30) (2.35) (2.26) (2.33)
Nanalyst 0.092*** 0.093*** 0.092*** 0.093*** 0.094*** 0.093*** 0.099***
(7.20) (7.37) (7.20) (7.11) (7.37) (7.08) (7.74)
Instown 0.055** 0.058*** 0.058*** 0.057*** 0.058*** 0.058*** 0.064***
(2.53) (2.66) (2.60) (2.62) (2.71) (2.64) (2.90)
NSeg -0.006*** -0.006*** -0.006*** -0.006*** -0.006*** -0.006*** -0.005***
(-3.65) (-3.63) (-3.56) (-3.61) (-3.58) (-3.55) (-3.28)
Observations 71,909 71,909 71,909 71,909 71,909 71,909 71,909
Pseudo R-squared 0.0208 0.0207 0.0210 0.0208 0.0207 0.0210 0.0205
54
Table 4 (continued) Panel C: Probit regression by year This table presents the relation between a stock price crash and guidance optimism from the probit model in Panel A
estimated each year. Variable definitions are in Appendix 1. Industry dummies and control variables are included in
all the regressions but their coefficients are not tabulated. The z-statistic for each coefficient is provided in parentheses
below. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10%
levels, respectively.
(1) (2) (3)
GuideOpt GuideOptFreq GuideOptLast
1997 0.073 0.073 0.049
(0.65) (0.65) (0.41)
1998 0.087 0.093 0.114
(0.97) (1.39) (1.19)
1999 0.166** 0.060 0.240***
(2.03) (1.26) (2.69)
2000 -0.029 -0.002 -0.084
(-0.37) (-0.05) (-0.99)
2001 0.144** 0.055** 0.197***
(2.46) (2.27) (3.14)
2002 0.135** 0.027 0.196***
(2.07) (1.14) (2.76)
2003 0.164*** 0.075*** 0.144**
(2.65) (3.33) (2.02)
2004 0.214*** 0.056*** 0.262***
(3.63) (3.04) (4.04)
2005 0.114* 0.050*** 0.112
(1.83) (2.69) (1.57)
2006 0.127** 0.049** 0.187**
(1.97) (2.53) (2.53)
2007 0.021 0.004 -0.043
(0.33) (0.21) (-0.59)
2008 0.109 0.025 0.211***
(1.57) (1.41) (2.74)
2009 0.047 0.006 0.028
(0.54) (0.21) (0.24)
2010 0.176** 0.064** 0.392***
(1.99) (2.32) (3.70)
2011 0.203*** 0.044** 0.225**
(2.62) (1.96) (2.39)
2012 0.247*** 0.059*** 0.321***
(3.29) (2.86) (3.52)
2013 0.188** 0.069*** 0.266***
(2.47) (3.31) (2.99)
2014 0.024 0.007 0.008
(0.31) (0.32) (0.09)
2015 0.107 0.030 0.133
(1.31) (1.38) (1.28)
55
Table 4 (continued) Panel D: Tests using alternative regression models
This table presents the regression results of a stock price crash on guidance optimism from the Fama-MacBeth model in Columns (1)-(3), a conditional logit model
in Columns (4)-(6), and the main model run on the reduced sample with at least one guidance in Columns (7)-(9). Variable definitions are in Appendix 1. The t-
statistic for each coefficient is provided in parentheses below. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively.
Fama-MacBeth regression Conditional logit regression Firm-years with at least one guidance
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Intercept 0.161*** 0.161*** 0.155*** -0.214 -0.201 -0.235
(3.08) (3.09) (2.98) (-1.33) (-1.23) (-1.47)
GuideOpt 0.038*** 0.080** 0.108***
(6.83) (2.48) (4.71) GuideOptFreq 0.014***
0.030*** 0.035***
(7.20) (2.87)
(4.35) GuideOptLast 0.049*** 0.147*** 0.139***
(5.25) (4.17) (4.54)
ROE 0.007* 0.006* 0.007* 0.021 0.020 0.020 0.012 0.010 0.013
(1.83) (1.83) (1.82) (1.37) (1.37) (1.37) (0.51) (0.44) (0.56)
Size -0.003 -0.003 -0.003 0.228*** 0.228*** 0.228*** -0.031** -0.032** -0.030**
(-1.21) (-1.25) (-1.13) (12.04) (12.02) (12.04) (-2.28) (-2.36) (-2.15)
MB 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002
(1.46) (1.43) (1.45) (0.69) (0.70) (0.68) (0.88) (0.92) (0.81)
Leverage -0.004 -0.004 -0.003 0.336*** 0.335*** 0.335*** -0.051 -0.060 -0.052
(-0.34) (-0.39) (-0.28) (3.89) (3.88) (3.87) (-0.94) (-1.11) (-0.95)
MeanRet 1.115*** 1.117*** 1.103*** 16.164*** 16.167*** 16.221*** 2.873** 2.858* 2.832*
(5.40) (5.44) (5.19) (16.47) (16.48) (16.54) (2.00) (1.92) (1.94)
StdRet -0.059 -0.057 -0.063 -4.258*** -4.263*** -4.269*** -0.981* -0.928 -1.011*
(-0.77) (-0.74) (-0.81) (-10.98) (-10.99) (-11.00) (-1.73) (-1.61) (-1.74)
Insiderown 0.084* 0.086* 0.081* 0.418 0.424 0.411 -0.187 -0.168 -0.215
(2.02) (2.04) (1.94) (1.32) (1.34) (1.30) (-0.50) (-0.45) (-0.57)
Nanalyst 0.025*** 0.026*** 0.026*** 0.178*** 0.178*** 0.177*** 0.080*** 0.078*** 0.082***
(7.14) (7.29) (7.14) (6.53) (6.54) (6.49) (3.65) (3.56) (3.71)
Instown 0.020*** 0.020*** 0.020*** 0.163** 0.163** 0.163** 0.051 0.052 0.058*
(3.20) (3.35) (3.30) (2.41) (2.40) (2.40) (1.47) (1.53) (1.66)
NSeg -0.002*** -0.002*** -0.002*** -0.000 -0.000 -0.000 -0.002 -0.002 -0.001
(-3.04) (-3.04) (-2.99) (-0.03) (-0.03) (-0.04) (-0.58) (-0.63) (-0.51)
Observations 71,909 71,909 71,909 60,229 60,229 60,229 16,975 16,975 16,975
Pseudo R-squared 0.0135 0.0136 0.0137 0.0232 0.0233 0.0235 0.0145 0.0149 0.0153
56
Table 5 Endogeneity tests Panel A: Controlling for other variables
This table presents the results of estimating a probit model of a stock price crash on guidance optimism after controlling for other possible predictors of a stock
price crash risk. Variable definitions are in Appendix 1. Year and industry dummies are included in all the regressions but their coefficients are not tabulated.
Standard errors are clustered by firm and by year. The z-statistic for each coefficient is provided in parentheses below. Significance levels are based on two-tailed
tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Intercept -0.647*** -0.645*** -0.655*** -0.490*** -0.491*** -0.498*** 0.151 0.157 0.136 -0.572*** -0.575*** -0.582***
(-2.77) (-2.74) (-2.80) (-4.30) (-4.30) (-4.40) (0.55) (0.57) (0.49) (-4.38) (-4.39) (-4.49)
GuideOpt 0.158*** 0.118*** 0.121*** 0.123***
(7.58) (6.56) (7.51) (6.62) GuideOptFreq 0.049*** 0.037*** 0.034*** 0.038***
(5.67) (6.07) (6.64) (6.94) GuideOptLast 0.183*** 0.155*** 0.158*** 0.156***
(4.86) (5.73) (5.51) (5.17)
Opaq 0.160*** 0.161*** 0.159***
(3.04) (3.05) (3.03) Opaq2 -0.015*** -0.015*** -0.014***
(-2.92) (-2.94) (-2.91) Comparability -0.084** -0.083** -0.081*
(-2.00) (-1.97) (-1.94) C_Score -0.035 -0.034 -0.043
(-0.45) (-0.42) (-0.56) DRO 0.003 0.003 0.003
(0.21) (0.20) (0.21) 10KFilesize 0.017 0.017 0.017
(1.07) (1.07) (1.08) Sentiment 0.071** 0.072*** 0.069**
(2.54) (2.61) (2.47) Overconfidence 0.058** 0.057** 0.059**
(2.17) (2.13) (2.19) Overinvestment 0.001* 0.001* 0.001*
(1.92) (1.91) (1.93)
Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 22,492 22,492 22,492 71,909 71,909 71,909 21,882 21,882 21,882 57,442 57,442 57,442
Pseudo
R-squared 0.0215 0.0215 0.0216 0.0208 0.0208 0.0210 0.0193 0.0191 0.0197 0.0231 0.0230 0.0232
57
Table 5 (continued) Panel B: Chamberlin Random Effect (CRE) model regression
This table presents coefficients on guidance variables from a CRE probit model. Variable definitions are in Appendix
1. Year dummies are included in all the regressions but their coefficients are not tabulated. Standard errors are
clustered by firm and by year. The z-statistic for each coefficient is provided in parentheses below. Significance levels
are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
(1) (2) (3)
Intercept -0.315** -0.316** -0.337***
(-2.46) (-2.46) (-2.62)
GuideOpt 0.044**
(2.24) GuideOptFreq 0.014**
(2.16) GuideOptLast 0.086***
(2.91)
ROE 0.018** 0.018** 0.018**
(2.41) (2.41) (2.40)
Size 0.116*** 0.115*** 0.115***
(6.00) (5.99) (6.06)
MB 0.001 0.001 0.001
(1.00) (0.99) (1.00)
Leverage 0.162*** 0.161*** 0.161***
(2.75) (2.76) (2.75)
MeanRet 8.308*** 8.297*** 8.355***
(6.97) (6.95) (7.02)
StdRet -1.193*** -1.192*** -1.216***
(-4.14) (-4.15) (-4.21)
Insiderown 0.225 0.227 0.220
(1.14) (1.15) (1.11)
Nanalyst 0.105*** 0.106*** 0.105***
(6.16) (6.16) (6.10)
Instown 0.063 0.063 0.061
(1.47) (1.48) (1.40)
NSeg -0.001 -0.001 -0.001
(-0.25) (-0.25) (-0.26)
Observations 71,909 71,909 71,909
Pseudo R-squared 0.0262 0.0262 0.0264
58
Table 5 (continued) Panel C: Difference-in-difference test using Regulation SHO
This table presents the relation between a stock price crash and guidance optimism using a probit model for the Russell 3000 firms in our sample during the Rule
202T—Pilot Program period (i.e., July 2004 through Aug 2007). The sample is composed of the first fiscal years affected by Reg SHO, with 2,169 firm-years in
fiscal year 2004 and 132 in fiscal year 2005. Out of these firm-years, 736 are pilot stocks whereas the others are controls. The pre- (post-) period sample is composed
the same firms’ fiscal year 2003 (2007) observations. Variable definitions are in Appendix 1. Industry dummies are included in all the regressions but their
coefficients are not tabulated. Standard errors are clustered by firm and by year. The z-statistic for each coefficient is provided in parentheses below. Significance
levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Treatment Period Pre Period Post Period
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Intercept -5.038*** -5.011*** -5.025*** -5.087*** -5.065*** -5.063*** -1.181** -1.207** -1.205** -0.182 -0.159 -0.123
(-13.91) (-14.30) (-13.68) (-14.10) (-14.67) (-13.86) (-2.08) (-2.13) (-2.12) (-0.32) (-0.28) (-0.21)
GuideOpt 0.189*** 0.195*** 0.235*** 0.149
(5.70) (4.84) (2.76) (1.59) GuideOptFreq 0.051*** 0.045*** 0.080*** 0.031
(3.43) (2.94) (2.73) (1.18) GuideOptLast 0.223*** 0.219*** 0.205** -0.016
(28.53) (22.33) (2.07) (-0.15)
Pilot 0.087*** 0.067*** 0.080*** 0.034 0.000 0.022 0.012 -0.017 -0.033
(10.90) (18.38) (77.95) (0.46) (0.00) (0.31) (0.15) (-0.23) (-0.44)
GuideOpt × Pilot 0.002 -0.060 -0.261
(0.07) (-0.42) (-1.62) GuideOptFreq×
Pilot 0.025*** 0.035 -0.045
(43.31) (0.68) (-0.99) GuideOptLast×
Pilot 0.032*** -0.029 -0.092
(4.03) (-0.17) (-0.50)
Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 2,301 2,301 2,301 2,301 2,301 2,301 2,221 2,221 2,221 1,825 1,825 1,825
Pseudo R-squared 0.0332 0.0326 0.0336 0.0339 0.0335 0.0344 0.0342 0.0359 0.0327 0.0323 0.0315 0.0310
59
Table 5 (continued) Panel D: Instrumental variable test using Regulation FD
This table presents the relation between a stock price crash and guidance optimism estimated using a probit model
with a continuous endogenous regressor (Newey, 1987; Foster, 1997). We employ RegFD, the Regulation FD dummy,
as an instrumental variable for GuideOpt, GuideOptFreq, and GuideOptLast, and estimate Ivprobit regressions for a
sub-sample period (1999-2003) surrounding RegFD adoption. Variable definitions are in Appendix 1. Year dummies
are included in all the regressions but their coefficients are not tabulated. Standard errors are clustered by firm. The z-
statistic for each coefficient is provided in parentheses below. Significance levels are based on two-tailed tests. ***,
**, and * denote significance at the 1%, 5%, and 10% levels, respectively.
1st 2nd 1st 2nd 1st 2nd
Stage Stage Stage
GuideOpt Crash GuideOptFreq Crash GuideOptLast Crash
Intercept -0.064** -0.822*** -0.162*** -0.680** -0.020 -0.857***
(-2.19) (-3.76) (-2.74) (-2.47) (-0.79) (-3.92)
RegFD 0.046*** 0.073*** 0.036***
(3.54) (3.34) (3.04) GuideOpt 1.994**
(2.38) GuideOptFreq 1.131***
(2.84) GuideOptLast 2.387**
(2.51)
ROE 0.004 -0.003 0.007 -0.005 0.002 -0.001
(1.28) (-0.23) (1.37) (-0.36) (0.84) (-0.07)
Size 0.006*** 0.005 0.020*** -0.007 0.002 0.012
(2.63) (0.33) (3.82) (-0.41) (0.92) (1.04)
MB -0.000 0.005** -0.000 0.005** -0.000 0.005**
(-0.35) (2.26) (-0.30) (2.04) (-0.32) (2.14)
Leverage 0.059*** -0.137** 0.127*** -0.162** 0.043*** -0.123**
(4.99) (-2.00) (5.48) (-2.41) (4.08) (-1.98)
MeanRet -0.761*** 4.686*** -1.120*** 4.150*** -0.721*** 4.744***
(-6.26) (7.02) (-5.26) (5.92) (-6.35) (7.28)
StdRet -0.031 -0.672** -0.177** -0.468 0.024 -0.759***
(-0.69) (-2.31) (-2.00) (-1.39) (0.58) (-2.74)
Insiderown 0.034 -0.351 -0.081 -0.164 0.066 -0.427
(0.51) (-1.23) (-0.68) (-0.60) (1.04) (-1.52)
Nanalyst 0.054*** -0.054 0.087*** -0.049 0.044*** -0.054
(11.86) (-0.92) (9.08) (-0.96) (10.81) (-0.93)
Instown 0.082*** -0.043 0.153*** -0.065 0.064*** -0.039
(5.64) (-0.40) (5.01) (-0.62) (5.18) (-0.38)
NSeg 0.004*** -0.013*** 0.008*** -0.013*** 0.003*** -0.012***
(4.64) (-3.43) (4.20) (-3.74) (4.41) (-3.61)
Observations 14,413 14,413 14,413 14,413 14,413 14,413
Wald test of exogeneity 3.032 3.032 3.429 3.429 2.920 2.920
60
Table 6 Refinement of guidance optimism measures Panel A: Joint optimism of managers and analysts
This table presents the relation between a stock price crash and guidance in conjunction with optimism in subsequent
analyst forecasts. Variable definitions are in Appendix 1. Year and industry dummies are included in all the regressions
but their coefficients are not tabulated. Standard errors are clustered by firm and by year. The z-statistic for each
coefficient is provided in parentheses below. Significance levels are based on two-tailed tests. ***, **, and * denote
significance at the 1%, 5%, and 10% levels, respectively.
(1) (2) (3)
Intercept -0.496*** -0.497*** -0.505***
(-4.34) (-4.34) (-4.46)
JointOpt 0.136***
(7.22) JointOptFreq 0.046***
(5.39) JointOptLast 0.309***
(3.11)
ROE 0.016** 0.016** 0.017**
(2.12) (2.13) (2.18)
Size -0.012 -0.012 -0.011
(-1.30) (-1.30) (-1.20)
MB 0.003*** 0.003*** 0.003***
(2.69) (2.66) (2.63)
Leverage 0.006 0.006 0.010
(0.19) (0.20) (0.31)
MeanRet 3.807*** 3.786*** 3.590***
(4.86) (4.81) (4.64)
StdRet -0.961*** -0.969*** -0.960***
(-3.43) (-3.49) (-3.50)
Insiderown 0.315** 0.319** 0.308**
(2.34) (2.37) (2.31)
Nanalyst 0.093*** 0.094*** 0.097***
(7.35) (7.52) (7.51)
Instown 0.056** 0.059*** 0.065***
(2.55) (2.70) (2.96)
NSeg -0.006*** -0.006*** -0.005***
(-3.65) (-3.63) (-3.43)
Observations 71,909 71,909 71,909
Pseudo R-squared 0.0208 0.0206 0.0204
61
Table 6 (continued) Panel B: Tests using different type of guidance to construct guidance optimism measures
This table presents the result from estimating a probit model of a stock price crash on guidance optimism constructed with different types of guidance. Each
coefficient is from a separate model of crash risk on individual guidance variable and controls. A forecast is defined as bundled if the announcement day falls
within the [-2, +2] window surrounding an earnings announcement date (Rogers and Van Buskirk 2013). A forecast is defined as reaffirming (updating) if the
forecast is not the first of the fiscal year, and the forecasted earnings is within (out of) the +/− 10% range of the previous guidance. A firm is a routine guider for a
fiscal year if it, on any guidance date in the fiscal year, has issued guidance in three out of the four calendar quarters prior to that date (Rogers et al. 2009); it is a
sporadic guider otherwise. Variable definitions are in Appendix 1. Year and industry dummies are included in all the regressions but their coefficients are not
tabulated. Standard errors are clustered by firm and by year. The z-statistic for each coefficient is provided in parentheses below. Significance levels are based on
two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Bundled Non-bundled Reaffirming Updating Routine
guider Sporadic guider
GuideOpt 0.115*** 0.103*** 0.114*** 0.141*** 0.110*** 0.107***
(5.38) (5.18) (5.50) (5.08) (5.75) (3.72)
GuideOptFreq 0.047*** 0.056*** 0.040*** 0.102*** 0.033*** 0.063***
(5.56) (3.82) (4.81) (4.03) (5.42) (4.73)
GuideOptLast 0.151*** 0.133*** 0.168*** 0.121*** 0.160*** -0.000
(4.93) (5.47) (5.23) (3.54) (5.29) (-0.00)
62
Table 7 Unintentional Bias Panel A: Cross-sectional variation in proxies for unintentional bias This table examines the relation between crash risk and guidance optimism, when bias is likely unintentional because
of high litigation risk. Variable definitions are in Appendix 1. Year and industry dummies are included in all the
regressions but their coefficients are not tabulated. Standard errors are clustered by firm and by year. The z-statistic
for each coefficient is provided in parentheses below. Significance levels are based on two-tailed tests. ***, **, and *
denote significance at the 1%, 5%, and 10% levels, respectively.
(1) (2) (3)
Uninten_Bias: LitRisk_High
Intercept -0.503*** -0.504*** -0.511*** (-4.12) (-4.11) (-4.21)
GuideOpt 0.179***
(6.17)
GuideOptFreq 0.063***
(5.90)
GuideOptLast 0.181*** (5.01)
Uninten_Bias 0.067*** 0.068*** 0.056*** (3.15) (3.35) (2.70)
GuideOpt × Uninten_Bias -0.092***
(-2.59)
GuideOptFreq × Uninten_Bias -0.038***
(-3.11)
GuideOptLast × Uninten_Bias -0.038 (-1.03)
ROE 0.012 0.012 0.012 (1.39) (1.39) (1.40)
Size -0.017 -0.017* -0.016 (-1.64) (-1.66) (-1.55)
MB 0.002* 0.002* 0.002* (1.88) (1.85) (1.86)
Leverage 0.008 0.007 0.009 (0.26) (0.22) (0.31)
MeanRet 4.363*** 4.329*** 4.381*** (5.73) (5.68) (5.71)
StdRet -1.201*** -1.207*** -1.204*** (-4.53) (-4.59) (-4.53)
Insiderown 0.334** 0.341** 0.329** (2.33) (2.37) (2.30)
Nanalyst 0.091*** 0.092*** 0.092*** (7.64) (7.83) (7.58)
Instown 0.057** 0.058** 0.061** (2.30) (2.42) (2.46)
NSeg -0.005*** -0.005*** -0.005*** (-3.02) (-3.00) (-2.92)
Observations 63,555 63,555 63,555
Pseudo R-squared 0.0224 0.0224 0.0225
63
Table 7 (continued) Panel B: Analyses of predicted and residual guidance optimism
This table examines how predicted and residual optimism in guidance, measured as residuals from a rolling AR(1)
model, are associated with stock price crash risk. Variable definitions are in Appendix 1. Year and industry dummies
are included in all the regressions but their coefficients are not tabulated. Standard errors are clustered by firm and by
year. The z-statistic for each coefficient is provided in parentheses below. Significance levels are based on two-tailed
tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
(1) (2) (3)
Intercept -0.400** -0.388** -0.409**
(-2.38) (-2.32) (-2.42)
RGuideOpt 0.118***
(3.94) RGuideOptFreq 0.041***
(4.30) RGuideOptLast 0.191***
(4.50)
PGuideOpt 0.172***
(5.22) PGuideOptFreq 0.036***
(5.64) PGuideOptLast 0.211***
(4.45)
ROE 0.006 0.006 0.006
(0.43) (0.39) (0.43)
Size -0.026** -0.027** -0.025**
(-2.15) (-2.22) (-2.06)
MB -0.000 -0.000 -0.000
(-0.14) (-0.18) (-0.16)
Leverage 0.031 0.032 0.037
(0.61) (0.63) (0.73)
MeanRet 3.909*** 3.935*** 3.920***
(3.11) (3.07) (3.07)
StdRet -0.453 -0.461 -0.485
(-1.28) (-1.31) (-1.40)
Insiderown 0.317* 0.329* 0.309*
(1.80) (1.89) (1.79)
Nanalyst 0.097*** 0.101*** 0.099***
(5.13) (5.48) (5.18)
Instown 0.013 0.021 0.016
(0.39) (0.63) (0.49)
NSeg -0.005*** -0.005** -0.004**
(-2.60) (-2.58) (-2.43)
Observations 24,168 24,168 24,168
Pseudo R-squared 0.0160 0.0159 0.0164
64
Table 8 Supplemental tests Panel A: Market reaction to forecast bias in guidance
This table examines how markets react to the news and bias in long-horizon guidance using the last earnings guidance issued in a fiscal quarter. The first column
presents the results from an OLS regression estimated for the full firm-quarter panel, and the second column presents the results from an OLS regression estimated
for guidance that is not bundled with an earnings announcement. CAR is measured for the five day window surrounding the guidance date. Last4Q is the dummy
variable assigned to the four fiscal quarters prior to a crash year. The concurrent quarterly earnings news (ConcEAnews) is calculated as an earnings surprise for
guidance bundled with announcements of quarter t-1 earnings and is set to zero for non-bundled guidance. More detailed variable definitions are in Appendix 1.
Year and industry dummies are included in all the regressions but their coefficients are not tabulated. Standard errors are clustered by firm and by quarter. The t-
statistic for each coefficient is provided in parentheses below. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Bundled Non-Bundled Bundled Non-Bundled Bundled Non-Bundled Bundled Non-Bundled
GuideBias>0 GuideBias<=0 GuideBias>0 GuideBias<=0 GuideBias>0 GuideBias<=0 GuideBias>0 GuideBias<=0
Dep. Var: CAR
Intercept -0.008 -0.014** 0.005 -0.013 0.000 -0.018*** -0.008 -0.013** 0.005 -0.013 0.000 -0.018***
(-0.96) (-2.30) (0.49) (-1.48) (0.05) (-2.81) (-0.95) (-2.28) (0.50) (-1.49) (0.05) (-2.73)
TrueNews 0.282*** 0.373*** 0.188*** 0.394*** 0.225*** 0.751*** 0.281*** 0.338*** 0.185*** 0.426*** 0.187*** 0.801***
(4.93) (4.54) (4.52) (2.96) (3.30) (7.14) (5.73) (3.86) (4.61) (4.22) (2.61) (6.53)
GuideBias 0.103*** 0.151*** 0.085*** 0.228** 0.105*** 0.553*** 0.100*** 0.127** 0.081*** 0.244*** 0.082** 0.600***
(2.95) (2.76) (2.92) (2.01) (2.64) (4.45) (2.92) (2.44) (2.76) (2.65) (2.38) (4.09)
Last4Q 0.001 -0.003 0.002 0.000 -0.004 -0.002 0.000 -0.002 0.002 0.001 -0.002 -0.002
(0.50) (-1.42) (1.34) (0.25) (-1.34) (-0.63) (0.43) (-1.19) (1.11) (0.51) (-0.59) (-0.84)
TrueNews 0.004 0.272** 0.019 -0.144 0.360*** -0.220
× Last4Q (0.04) (2.14) (0.42) (-0.64) (3.64) (-0.81)
GuideBias 0.015 0.214** 0.022 -0.079 0.257*** -0.220
× Last4Q (0.31) (2.05) (0.83) (-0.37) (2.88) (-0.84)
ConcEAnews 0.141*** 0.153*** 0.129*** 0.141*** 0.153*** 0.131***
(4.07) (2.94) (7.10) (4.30) (2.97) (7.46)
Observations 47,398 15,325 18,815 28,583 5,797 9,528 47,398 15,325 18,815 28,583 5,797 9,528
R-squared 0.0217 0.0629 0.0187 0.0233 0.0804 0.0596 0.0217 0.0645 0.0188 0.0236 0.0841 0.0602
65
Table 8 (continued) Panel B: One-year-ahead returns
This table presents the relation between future returns and optimistic guidance. Variable definitions are in Appendix
1. Year and industry dummies are included in all the regressions but their coefficients are not tabulated. Standard
errors are clustered by firm and by year. The z-statistic for each coefficient is provided in parentheses below.
Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels,
respectively.
(1) (2) (3)
Dep. Var: One-year ahead returns
Intercept 0.669*** 0.669*** 0.672***
(3.17) (3.17) (3.19)
GuideOpt -0.032**
(-2.48)
GuideOptFreq -0.008*
(-1.70)
GuideOptLast -0.052***
(-3.55)
ROE 0.004 0.004 0.004
(0.33) (0.32) (0.33)
Size -0.029** -0.029** -0.030**
(-2.36) (-2.36) (-2.38)
MB -0.003** -0.003** -0.003**
(-2.45) (-2.44) (-2.45)
Leverage -0.013 -0.013 -0.013
(-0.27) (-0.27) (-0.26)
MeanRet -3.802* -3.788* -3.814*
(-1.94) (-1.93) (-1.95)
StdRet -0.116 -0.115 -0.114
(-0.12) (-0.12) (-0.11)
Insiderown 0.104 0.102 0.107
(1.40) (1.38) (1.44)
Nanalyst 0.024 0.024 0.025
(1.46) (1.44) (1.49)
Instown 0.093*** 0.092*** 0.093***
(4.75) (4.68) (4.74)
NSeg 0.003*** 0.003*** 0.003***
(3.34) (3.28) (3.28)
Observations 71,909 71,909 71,909
R-squared 0.1158 0.1157 0.1160
66
Table 8 (continued) Panel C: CIG discontinuation
This table presents the relation between crash risk and optimistic guidance measured using CIG- and I/B/E/S-covered
guidance separately. Variable definitions are in Appendix 1. Year and industry dummies are included in all the
regressions but their coefficients are not tabulated. Standard errors are clustered by firm and by year. The z-statistic
for each coefficient is provided in parentheses below. Significance levels are based on two-tailed tests. ***, **, and *
denote significance at the 1%, 5%, and 10% levels, respectively.
1997-2011 (CIG) 1997-2015 (IBES)
(1) (2) (3) (4) (5) (6)
Intercept -0.624*** -0.622*** -0.613*** -0.493*** -0.494*** -0.490***
(-5.36) (-5.36) (-5.38) (-4.32) (-4.31) (-4.32)
GuideOpt 0.111*** 0.122***
(5.13) (6.85) GuideOptFreq 0.037*** 0.037***
(4.64) (6.20) GuideOptLast 0.063*** 0.070***
(2.71) (2.95)
ROE 0.015 0.015 0.015 0.016** 0.016** 0.016**
(1.56) (1.57) (1.57) (2.10) (2.11) (2.11)
Size -0.003 -0.003 -0.004 -0.012 -0.012 -0.013
(-0.31) (-0.33) (-0.36) (-1.30) (-1.31) (-1.37)
MB 0.002 0.002 0.002 0.003*** 0.003*** 0.003***
(1.48) (1.50) (1.49) (2.65) (2.64) (2.63)
Leverage 0.027 0.026 0.029 0.007 0.006 0.008
(0.89) (0.85) (0.95) (0.22) (0.19) (0.25)
MeanRet 4.226*** 4.204*** 4.034*** 3.813*** 3.784*** 3.576***
(5.01) (4.96) (4.99) (4.86) (4.81) (4.66)
STDRet -1.094*** -1.097*** -1.057*** -0.962*** -0.965*** -0.919***
(-3.67) (-3.70) (-3.54) (-3.43) (-3.45) (-3.25)
Insiderown 0.340** 0.347** 0.342** 0.309** 0.318** 0.310**
(2.32) (2.37) (2.34) (2.30) (2.36) (2.32)
Nanalyst 0.087*** 0.089*** 0.087*** 0.092*** 0.093*** 0.092***
(6.02) (6.18) (5.66) (7.16) (7.35) (6.88)
Instown 0.054** 0.055** 0.057** 0.055** 0.058*** 0.059***
(2.07) (2.17) (2.29) (2.51) (2.65) (2.69)
NSeg -0.006*** -0.006*** -0.006*** -0.006*** -0.006*** -0.006***
(-3.63) (-3.59) (-3.63) (-3.66) (-3.63) (-3.65)
Observations 60,151 60,151 60,151 71,909 71,909 71,909
Pseudo R-squared 0.0206 0.0206 0.0203 0.0208 0.0207 0.0204
67
Table 8 (continued) Panel D: Downward expectation management guidance
This table presents the relation between crash risk and guidance that is provided to manage expectations upward, to
communicate, or to manage expectations downward (Kim and Park 2012). Variable definitions are in Appendix 1.
Year and industry dummies are included in all the regressions but their coefficients are not tabulated. Standard errors
are clustered by firm and by year. The z-statistic for each coefficient is provided in parentheses below. Significance
levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
(1) (2) (3)
Intercept -0.504*** -0.505*** -0.501***
(-4.48) (-4.45) (-4.45)
GuideUEM 0.090*** (3.71)
GuideComm 0.062*** (3.43)
GuideDEM -0.056** (-2.36)
GuideUEMFreq 0.045***
(3.62) GuideCommFreq 0.018***
(2.93)
GuideDEMFreq -0.017**
(-2.25) GuideUEMLast 0.140***
(3.94)
GuideCommLast 0.091***
(3.25)
GuideDEMLast -0.003
(-0.12)
ROE 0.016** 0.016** 0.016**
(2.12) (2.10) (2.09)
Size -0.011 -0.011 -0.012
(-1.22) (-1.20) (-1.26)
MB 0.003*** 0.003*** 0.002***
(2.63) (2.60) (2.59)
Leverage 0.008 0.008 0.008
(0.27) (0.25) (0.27)
MeanRet 3.741*** 3.683*** 3.617***
(4.82) (4.75) (4.71)
StdRet -0.966*** -0.962*** -0.932***
(-3.39) (-3.40) (-3.27)
Insiderown 0.310** 0.317** 0.306**
(2.30) (2.34) (2.28)
Nanalyst 0.093*** 0.094*** 0.090***
(7.01) (7.24) (6.73)
Instown 0.056*** 0.058*** 0.057***
(2.60) (2.73) (2.62)
NSeg -0.006*** -0.005*** -0.006***
(-3.53) (-3.52) (-3.52)
Observations 71,909 71,909 71,909
Pseudo R-squared 0.0207 0.0207 0.0207