School of Accounting
Seminar – Session 1, 2012
Attracting Attention in a Limited Attention World: An Exploration of the Determinants and Consequences of Positive Extreme Earnings Surprises
Mark Soliman University of Washington
Date: Friday 9th March Time: 3:00 to 4:30 pm Venue: Webster256 Attracting attention in a limited attention world: An exploration of the determinants and consequences of positive extreme earnings surprises
Allison Koester Assistant Professor of Accounting McDonough School of Business Georgetown University [email protected]
Russell Lundholm Professor of Accounting Sauder School of Business University of British Columbia [email protected]
Mark Soliman* Associate Professor of Accounting Foster School of Business University of Washington [email protected]
Current draft: January 28, 2012 First draft: August 2010
*Corresponding author
Acknowledgements: The paper has benefitted from insightful comments by Phil Berger, Ryan Ball, David Burgstahler, Brian Cadman, Asher Curtis, Jared Jennings, David Hirshleifer, Jon Karpoff, Sarah McVay, Mort Pincus, Shiva Rajgopal, Scott Richardson, Devin Shanthikumar (FARS 2011 discussant), Lakshmanan Shivakumar, Doug Skinner, Abbie Smith, Terry Shevlin, Siew Hong Teoh, Irem Tuna, but not Mark Soliman, as well as from workshops at Arizona State University, London Business School, the University of California at Irvine, the University of Chicago, the University of Utah, the University of Washington, the 2010 UBCOW Conference, the 2011 FARS mid-year meeting, and the 2011 AAA annual meeting. Attracting attention in a limited attention world: An exploration of the determinants and consequences of positive extreme earnings surprises
Abstract This paper examines the causes and consequences of large positive deviations between expected and realized earnings (earnings surprises). The post-earnings-announcement-drift literature treats earnings surprises as exogenous events that precipitate subsequent stock price drift (Ball, 1978; Bernard and Thomas, 1989; Doyle, Lundholm and Soliman, 2006). However, analyst expectations and earnings realizations are the result of conscious decisions made by analysts and managers. While neither analysts nor managers have an obvious incentive for a large surprise, this phenomenon is regularly observed. Accordingly, in this paper we explore two hypotheses as to why large positive earnings surprises occur: attention-seeking managers and inattentive analysts. While we find evidence consistent with both hypotheses, we find the most support for the manager attention-seeking hypothesis. Further, if managers created the earnings surprise to attract attention, they were successful. Following a large positive earnings surprise, there is a significant increase in analyst following, the percentage of firm shares held by institutional investors, and short and long-term trading volume. Finally, we find ample motivation for these actions including managerial increases in firm ownership prior to the earnings announcement, a lack of managerial earnings guidance, and a strong need for external financing that is fulfilled in the months subsequent to the large positive earnings surprise. Taken together, the evidence is consistent with large positive earnings surprises evolving as a function of economic decisions rather than as random exogenous events.
Keywords: Earnings Surprise, Attracting Attention, Analyst Forecast Accuracy
JEL classification: M4 1. INTRODUCTION
Beginning with Foster, Olsen and Shevlin (1984) and Bernard and Thomas (1989, 1990), a large literature has examined the post-earnings-announcement-drift in stock prices following large earnings surprises. The maintained assumption in this work is that extreme earnings surprises are random events that are exogenous in nature.1 Although this assumption makes the
empirical work tractable, it is a difficult to justify. The very fact that the market underreacts to
extreme earnings surprises suggests that the explanation for this event might go beyond random
chance. For example, defining the earnings surprise relative to the consensus analyst forecast,
Doyle et al. (2006) document a 9.4 percent abnormal return in the subsequent year for the top
decile of earnings surprise firms. The inability of market participants to accurately price firms
with such extreme good news suggests that more subtle forces might be at play.
In this paper we take a step back and ask why the large positive surprise occurred in the
first place. Instances where a firm’s earnings-per-share (EPS) far exceeds the analyst consensus
forecast are interesting because, ex ante, it seems that neither managers nor analysts have an
incentive for this kind of large deviation to occur. The analysts appear inaccurate and out of
touch with what is happening at the firm. And because the earnings response to good news is
generally concave, the firm gains little from being in the extreme positive tail of the earnings
distribution. Why has communication between the analyst and the firm broken down?
Most of the earnings surprise literature has focused on small positive surprises,
suggesting that the unusually large number of small positive surprises is evidence of earnings
management (Burgstahler and Eames 2006). In contrast, we are interested in the extreme
1 In our sample the median earnings realization in the top decile of quarterly earnings surprises is more than two times greater than its expected value (e.g., the median earnings realization is 13 cents per share and the median earnings expectation is 4 cents per share). In absolute terms, this is an extremely large deviation from expectations.
1 positive tail of the earnings surprise distribution. Are there reasons beyond random chance that cause certain firms to end up in this tail? Is it possible that the earnings surprise was more than a random extreme occurrence? Did management intentionally surprise the analysts? If so, then part of the explanation for the subsequent returns documented in the literature may reside with management’s motivation to surprise market participants. Alternatively, did the analyst community fail to revise its forecasts in response to available information? If so, then the explanation for the subsequent returns may depend on characteristics of the analysts who follow the firm. While large negative surprises are often attributed to “big bath” stories (Watts and
Zimmerman 1978, 1986), there is currently little known about the causes and consequences of large positive earnings surprises.
We look for evidence that positive extreme earnings surprises are the result of management or analyst decisions. However, the null hypothesis is that neither managers nor analysts anticipated that an unexpectedly good quarter was materializing, and the positive unexpected earnings were truly a surprise to all parties involved. By definition, 10 percent of the firms will always be in the top earnings surprise decile (which we refer to as an “extreme” earnings surprise). To account for this we include a number of control variables selected to proxy for a firm’s ex ante earnings volatility. After controlling for this volatility, is there evidence that management intentionally created the surprise or that analysts were not paying sufficient attention?
We explore two reasons why a firm might have a positive extreme earnings surprise
(PEES). Our first hypothesis explores the incentives of managers. We know from prior research that while positive earnings surprises are typically met with stock price increases, the earnings/return relation is S-shaped (Freeman and Tse 1992; Kinney et al. 2002; Burgstahler and
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Chuk 2009). This suggests that a large positive earnings surprise “wastes” earnings, as the
marginal stock price reaction is diminishing in earnings surprise magnitude. Anticipating this
market reaction, managers have an incentive to shift some of the earnings to a different quarter.
Because they didn’t do this in the PEES quarter, it must be the case that there is some other
benefit from the extreme surprise to compensate for the lost marginal stock return.
Prior research finds that PEES firms tend to be smaller, with less analyst coverage and
lower institutional ownership (Doyle et al. 2006). We hypothesize that managers of these
smaller and less visible firms might be attempting to garner capital markets attention by
announcing very large positive earnings results and “surprising” the market. As Merrill Lynch
economist Trent Barnett notes, “if a company surprises…it gets the market's attention” (Maguire
2002). For example, on September 7, 2011, the firm Conn’s, Inc. reported second quarter
earnings that beat the analyst forecast by more than 50 percent, causing its price to jump 40
percent. That afternoon, the Associated Press, Marketwatch, Bloomberg, Forbes, Barrons and
the Wall Street Journal all commented on the price increase, citing the large earnings surprise as
the cause. In the next few days two analyst services upgraded the stock, and on September 23,
2011 Forbes wrote an article about the company titled “Everything’s Bigger in Texas, including
this Retailer’s Earnings” (Ryniec 2011).
We find several pieces of evidence in support of our manager attention-seeking
hypothesis. First, we find that PEES firm-quarters are associated with firms that exhibit strong
past earnings performance that was not fully appreciated by the market, suggesting the firm is
somewhat “neglected” by capital market participants and providing a motivation for attention-
seeking behavior. We also find that these firm-quarters are associated with strong future performance in that they experience larger increases in future return on assets relative to non-
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PEES firms. This is consistent with the idea that management has private, favorable information about their firm’s future prospects at the time of the surprise. Interestingly, PEES firm managers are less likely to issue EPS guidance, indicating that the resulting earnings “surprise” may be the result of management withholding favorable information until the earnings announcement date.
We also find that managers make the most of their firms’ positive future prospects in that these managers are more likely to engage in net insider stock purchases during the weeks preceding a large PEES. There are also real financing decisions that may be driving this behavior. We find that PEES firm-quarters are associated with firms with greater financing needs who are more likely to issue equity following a PEES quarter. Finally, we also show that management’s attempt to attract attention is ultimately successful, documenting a significant increase in the number of analysts following the firm, the percentage of shares held by institutions, and short- term and long-term trading volume following a PEES quarter.
The following example illustrates how management might benefit from manufacturing an earnings surprise. MedImmune is a biotechnology firm that develops and sells products used in the prevention and treatment of infectious diseases. On April 22, 1998, the firm announced its
1998 first quarter earnings of 44 cents per share, more than double the 6-analyst consensus forecast of 21 cents per share. While management provided no earnings guidance prior to the announcement, first quarter revenues had increased nearly six-fold relative to the prior year quarter due to increased demand for the firm’s respiratory drug line. Insiders purchased 2.6 million shares of Medimmune’s stock during the four weeks prior to the earnings announcement.
In fact, purchases by executive suite managers accounted for more than 70 percent of the insider shares purchased. Industry-adjusted return on assets increased by 11 percent from the year prior to the year following the PEES, and the firm’s cash from equity issuances increased
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monotonically in each of the following three years. Analyst following more than doubled and
trading volume increased by more than eight percent over the subsequent 12 quarters.
Our second hypothesis is that a PEES occurs because analysts are not paying attention to
the positive developments happening at the firm. Analysts may not be sufficiently interested in a
stock to put forth the necessary effort to stay abreast of management communications and other
events that might aid in predicting the upcoming quarter’s earnings. Relative to non-PEES firms,
we find that PEES firms have lower analyst following, the analysts who are following PEES
firms are following a greater number of other firms, and these analysis issue individual earnings
forecasts which have greater than normal dispersion.
While testing these two hypotheses, we recognize that the earnings in some firm-quarters
are naturally more volatile than others and these firm-quarter observations are more likely to arrive in the extreme positive decile. In fact, it could be that neither party in the earnings announcement game knew anything about the impending large positive earnings surprise.
Consistent with this explanation, we find that PEES are more likely for firms with volatile prior quarterly return on assets, higher operating leverage, and firms with large special items in that quarter. Further, all our results are incremental to controlling for firm size and ownership by institutional investors (proxies for general interest in the company).
Recognizing that our two hypotheses are not mutually exclusive, we attempt to quantify the economic significance of our findings between the hypotheses by analyzing the joint odds ratio for each set of independent variables. We examine how a one standard deviation (unit)
change in each continuous (binary) variable influences the odds of observing a PEES firm-
quarter. We find that such a change in the four manager attention-seeking proxies triples the odds of observing a PEES firm-quarter (joint odds ratio of 3.00), while the five analyst
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inattention variables fall short of doubling the odds of observing a PEES firm-quarter (joint odds ratio of 1.59). Finally, a one unit or one standard deviation change in all the variables in the full model presented in Column 3 of Panel A of Table 4 yields more than a seventeen-fold increase
in the odds of observing a positive extreme earnings surprise (joint odds ratio of 17.68). Thus,
the results of our tests appear to be both statistically and economically significant in explaining
the occurrence of extreme earnings surprises.
Our paper proceeds as follows. Section 2 provides our hypothesis development and the
proxies we use to capture our constructs of interest, and Section 3 presents our descriptive
statistics. Section 4 presents our regression results, and Section 5 concludes.
2. HYPOTHESES AND MEASURES
2.1 Defining a Positive Extreme Earnings Surprise (PEES)
We define quarterly earnings surprise (SURPRISE) as the I/B/E/S actual EPS for a firm-
quarter less the I/B/E/S analyst consensus EPS forecast for the firm-quarter, divided by the firm’s stock price per share at the end of the fiscal quarter. The I/B/E/S actual EPS is unadjusted for subsequent splits, and the I/B/E/S analyst consensus EPS forecast is the most recent median forecast preceding the firm’s earnings announcement date.2 We use EPS figures unadjusted for
subsequent stock splits because the unsplit-adjusted database gives the EPS that was actually
reported in the company’s earnings announcement (e.g., the news that the market observed at
that time of the earnings announcement) and because prior research finds that using split-
2 Note that this is the definition of an analyst forecast error commonly used in the literature. We choose to label this measure ‘earnings surprise’ because labeling the deviation as an analyst forecast error implies that it is the analysts who are the cause of the difference between actual and forecasted EPS. 6
adjusted data can potentially distort both time-series and cross-sectional characteristics of earnings surprises (Baber and Kang 2002; Payne and Thomas 2003).3
We rank firm-quarter observations by SURPRISE in each calendar quarter and set the
binary indicator variable PEES_D equal to one when a firm-quarter observation is in the top calendar-quarter SURPRISE decile and equal to zero otherwise. By defining PEES by calendar quarter as opposed to sorting the entire pooled sample over the time period examined, we control for economic conditions that change over time. Without this within-calendar quarter definition, observations from time periods with large unexpected improvements in economy-wide performance would dominate the PEES sample, and the answer to why firms have large positive earnings surprises would revolve around predicting when the economy will experience unexpected improvements.
Figure 1 shows the minimum and mean SURPRISE values for PEES observations by year-quarter. The lower bound cutoff SURPRISE value is remarkably stable over our 10.5 year sample period (January 1998 through June 2008), ranging from a minimum of 0.31 percent of stock price in the first quarter of 1998 to a maximum of 0.91 percent of stock price in the third quarter of 2002, but generally staying close to .50 percent. The mean SURPRISE value for PEES observations is between 0.8 and 2.8 percent of stock price with a mean value of 1.4 percent.
3 Our confidence in the assertion that the I/B/E/S unsplit-adjusted actual EPS number is the value that market participants observe is based on findings in Doyle et al. (2006) and Doyle et al. (2010). Doyle et al. (2006) compare the earliest available I/B/E/S actual EPS in the unsplit-adjusted database with the actual press release found through LexisNexis for fifty firms and find that the data from the two sources matched in all fifty cases. In addition, Doyle et al. (2010) match 1,000 randomly selected quarterly observations from 1997 through 2000 from the I/B/E/S unsplit- adjusted database to the press release issued by firms via a Lexis-Nexis search and find that I/B/E/S corresponds perfectly with the press release in 915 cases. Given this high level of documented accuracy, we do not believe that large systematic errors in the I/B/E/S actual EPS value have a material impact on our results. 7
Below we develop hypotheses about independent variables designed to predict PEES_D
in a logistic regression.
2.1 Manager Attention-Seeking Hypothesis
This hypothesis is based on the premise that managers of “neglected” firms want to
attract investor attention with large positive earnings surprises. If the amount of attention a firm receives influences its stock price, then the manager of a neglected firm has an incentive to draw attention to its firm. In Merton’s limited attention CAPM (1987) model, investors form diversified portfolios only from the set of firms they know about, suggesting investor awareness has important cost of capital implications. More recently, Odean (1999) proposes that investors do not evaluate each of the thousands of stocks actively trading in public markets when making their investment decisions. Rather, investors limit their investment decisions to stocks that have recently caught their attention. A firm that catches an investor’s attention is more likely to be included in an investor’s portfolio, which has the potential to decrease the firm’s cost of capital.
A simple online search shows that earnings surprises certainly garner media attention, with prominent financial media outlets like the Wall Street Journal providing daily updates to its
“Earnings Surprises” website,4 NASDAQ populating its “Daily Earnings Surprise” website,5 and
Zacks announcing both bottom and top line surprises on its “Today’s EPS and Sales Surprise”
website.6
Lehavy and Sloan (2008) provide some empirical evidence consistent with this model,
finding that increases in institutional ownership (a proxy for investor awareness) are associated
with decreases in expected returns. More directly, Brown et al. (2009) find that both bid-ask
4 http://online.wsj.com/mdc/public/page/2_3024-zurprise.html 5 http://www.nasdaq.com/earnings/daily-earnings-surprise.aspx 6 http://www.zacks.com/research/earnings/today_eps.php 8 spreads and the probability of informed trading (PIN) decline after large positive earnings surprises, and interpret this as evidence that the firm has increased its market visibility.7
Similarly, Irvine (2003) finds that firms experience a significant increase in liquidity following the initiation of analyst coverage, and that the effect increases with the analyst’s recommendation. Barber and Odean (2008) find that individual investors tend to buy stocks that grab their attention – i.e., those mentioned in the news, with high daily trading volume, with an extreme daily return, etc. These same investors do not exhibit attention-based selling behavior because they can only sell a stock they already own. Finally, Busse and Green (2002) use TAQ data to document abnormally high trading volume precisely when investor attention to a stock increases. For example, when Maria Bartiromo mentions a stock during the CNBC television show Midday Call, average trading volume for this stock increases nearly five-fold during the few minutes following the mention. Taken together, the prior literature indicates that managers have incentives to bring attention to their firms.
It is possible that managers may use large favorable earnings surprises to attract attention from analysts and investors. Consider the following anecdote: In the second quarter of 2005,
TETRA Technologies Inc., a small-cap oil service company based in Texas, reported earnings that exceeded analyst expectations by nearly 50 percent, or earnings of 64 cents per share with an analyst consensus forecast of only 44 cents per share. During TETRA’s quarterly conference call, President and CEO Geoffrey Hertel opened his remarks with “I imagine the second quarter
7 Brown et al. (2009) examines the effect of earnings surprises on changes in information asymmetry. The authors find that information asymmetry is lower (higher) in firms that experience a positive (negative) surprise relative to firms with no earnings surprise. This relation is stronger when earnings surprises are larger, which is interpreted as evidence that larger surprises are more successful at increasing firm visibility. While Brown et al. are interested in exploring information environment changes that occur after an earnings surprise, we are interested in exploring why large positive earnings surprises occur and whether evidence consistent with managers anticipating large positive earnings surprises exists. In sum, Brown et al. take earnings surprises as an exogenous event that precipitates changes in information asymmetry while we treat earnings surprises as endogenous events that can be predicted by variables that capture managerial attention seeking. 9 earnings were somewhat surprising to at least a few of you. The street had anticipated a record quarter, but not one that more than doubled the previous record,” confirming that the firm’s exceptional earnings were truly unanticipated by the market. Lewis Kreps with Aperion Group, the second analyst selected to ask a question, confirmed Hertel’s point of view by beginning his question with “Congratulations Geoff…You've surprised me.”
If the TETRA management team was attempting to garner attention from the investment community with its large positive earnings surprise, management was successful. Analysts at
Ferris, Baker, Watts Inc. noted in their quarterly research report that TETRA Technologies’ EPS
“substantially beat our estimates…[with] outstanding performance in the company’s Completion
Fluids and Well Abandonment and Decommissioning segments [driving] this outperformance.”
The analysts continued by stating that “as a result of the strength that TETRA is experiencing in its end markets…and our views for continued growth in each of TETRA’s markets, we are increasing our 2005 and 2006 revenue and EPS forecasts.” In addition, Jefferies’ analysts commented that “TETRA Technologies reported second-quarter EPS of $0.64 [was] well ahead of our $0.46 estimate...with each of the company's segments registering double-digit growth in revenues and margin improvement…[we] are reiterating our Buy rating on TETRA
Technologies with a new 12-month price target…TETRA is very well positioned to achieve solid earnings over the next several years.” It appears that TETRA’s large positive earnings surprise caught the attention of analysts.
Because we cannot directly observe management’s intentions, we look for indirect evidence to assess this hypothesis. We use four variables to proxy for managerial intent to attract attention through a larger positive earnings surprise (all variables are defined in detail in the
Appendix). We hypothesize that if managers are attempting to create a large positive earnings
10 surprise they are less likely to provide earnings guidance prior to the announcement. While it is possible that they might also actively guide analysts’ forecasts down ahead of the positive announcement, such blatant manipulation is likely to damage their future credibility. We hypothesize that they simply say nothing. We measure manager guidance with GUIDE_D, a binary variable set equal to one if management provided any type of EPS guidance for period t earnings prior to the earnings announcement date, and set equal to zero otherwise. We predict a negative association between GUIDE_D and PEES_D.
We also expect managers to have positive private information about the firms’ expected future performance. Our assumption is that managers will not want to attract attention to the firm if the positive news is temporary, but rather that the positive performance will persist into the future. Following Lang and Lundholm (1993), we measure this construct by using the change in mean industry-adjusted return on assets (CH_ROA) from four quarters prior to the
PEES quarter (t-4 to t-1) to the four quarters following the PEES quarter t (t+1 to t+4), to proxy for managers’ private information about their firm’s expected future performance. We predict a positive association between CH_ROA and PEES_D.
Our next variable is designed to capture situations where management perceives that its firm’s strong past earnings performance has not been adequately rewarded by the stock market.
We create the binary variable NOLOVE_D which is set equal to one if the firm’s average ROA in periods t-4 through t-1 is greater than the industry median ROA and yet its market-adjusted buy-and-hold stock return during the prior year is less than the industry median, and equal to zero otherwise. In other words, the firm has experienced strong earnings performance (better than its industry peers) but yet the stock performance lags those same industry peers (i.e., it isn’t
11 getting any “love” from the capital markets). We predict a positive association between
NOLOVE_D and PEES_D.
Our final variable captures managers’ direct benefits from announcing surprisingly good news by examining whether insiders engage in more net purchases prior to the announcement of a PEES firm-quarter than before a non-PEES firm-quarter. Similar to our future ROA variable, insider trading is often interpreted as revealing management’s private information about a firm’s future prospects, with positive (negative) information being associated with positive (negative) private information (Ke et al. 2003; Huddart and Ke 2007). Insider selling prior to the revelation of bad news can be costly because of 10-b(5) litigation concerns, and prior research fails to find evidence of strategic firm disclosure prior to insider selling (Noe 1999, Cheng and Lo 2006,
Rogers 2008). In contrast, insider purchases prior to the revelation of good news are less risky from a litigation perspective because the insider trading harms no existing shareholder. Gu and
Li (2007) find that insider purchases enhance the credibility of qualitative voluntary disclosure
(especially in firms that exhibit higher degrees of information asymmetry), and firms with insider purchases that precede voluntary disclosure exhibit greater future abnormal returns than firms with no insider purchases preceding the firms’ voluntary disclosure.
If PEES managers truly anticipate their firm’s current strong earnings performance will continue into future periods, then they will want to take advantage of this upward trajectory.
Accordingly, we expect managers to increase their holdings in their firm’s stock prior to the announcement of an extreme positive earnings surprise. In contrast, if managers are unaware of the impeding good news at the earnings announcement in PEES quarters, there should be no relation between the probability of PEES and net insider acquisitions prior to a PEES quarter.
We set the binary variable INSIDER_PURCHASES_D equal to one if the sum of all shares
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traded by any firm insider during the four weeks preceding the week of that firm’s earnings announcement is positive (indicating that shares purchased exceeded shares sold), and equal to zero otherwise.
2.2 Analyst Inattention Hypothesis
Our second hypothesis focuses on analysts and the quality of their forecasts, as it is possible large earnings surprises occur because analysts made particularly poor forecasts.
Earnings forecast accuracy has been shown to increase with an analyst’s general experience, firm-specific experience, employment at a top-ten brokerage house, and the number of forecast revisions issued; and decrease with the number of firms and industries an analyst follows and the staleness of the analyst’s forecast (Mikhail et al. 1997, 1999; also see Ramnath et al. 2008 for a literature review). Prior research also finds that while forecast accuracy is unrelated to analysts’ annual compensation, accuracy is negatively related to analyst turnover, suggesting analysts who consistently issue inaccurate forecasts are more likely to be terminated (Groysberg et al., 2011).
Assuming that the analyst labor market allocates the most time and talent to the firms that are most valuable to the analysts, it could be that large earnings surprises are associated with firms where the benefit of greater accuracy is simply too low to justify the cost. Consistent with the idea that analysts rationally trade off the costs and benefits of expending effort on forecast accuracy, Alford and Berger (1999) find that both analyst following and forecast accuracy increase with the firm’s trading volume (and thus trading commissions). Similarly, Lang and
Lundholm (1996) find that forecast accuracy increases as the cost of collecting information declines, where the cost of collection is measured by the firm’s disclosure activity. Our second hypothesis is that large earnings surprises occur in firms where analyst attention is low.
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We use five variables to capture various aspects of analyst inattention. The first three
variables relate to properties of the analysts themselves. We use the number of analysts issuing
forecasts included in the most recent I/B/E/S analyst consensus EPS forecast prior to the earnings
announcement date (ANALYST_CNT) as a summary measure of the supply of analyst effort allocated to a firm. We expect ANALYST_CNT to be negatively associated with PEES_D.
ANALYST_EXP is the average number of years the analysts who contribute earnings forecasts to the consensus forecast in quarter t have been providing earnings forecasts as recorded on
I/B/E/S. Given that prior research has found that more experienced analysts are more accurate, we expect ANALYST_EXP to be negatively associated with PEES_D (Ramnath et al. 2008).
ANALYST_BUSY is an alternate measure of the supply of analyst attention and is defined as the average number of other firms each analyst is following in the quarter; we expect a positive relation between ANALYST_BUSY and PEES_D.
The next two variables relate to properties of the consensus forecast.
FORECAST_STALE is the number of days between the most recent I/B/E/S analyst consensus
EPS forecast and the earnings announcement date. We require the analyst consensus estimate date to occur before the earnings announcement date and so all values are postive. The greater the period of time between the two dates, the greater the chance that new information regarding the earnings realization is not incorporated into the consensus forecast. We therefore expect this variable to be positively associated with PEES_D. Finally, the standard deviation of individual analyst EPS forecasts that comprise the consensus forecast (FORECAST_SD) has been used in prior research as a measure of analyst uncertainty (Baron et al. 1998; Burgstahler and Chuk
2009). We expect FORECAST_SD to be positively associated with PEES_D. If the consensus
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forecast is comprised of only one analyst forecast, FORECAST_SD is set to zero (because there
is no disagreement among analysts in this case).
The interpretation of the manager attention-seeking variables somewhat overlaps with the
interpretation of the analyst inattention variables. This is not surprising, as the manager
attention-seeking hypothesis posits that managers feel their firm is neglected and attempt to
increase investor attention through PEES, while the analyst inattention hypothesis measures how
neglected the firm actually is. If extreme positive earnings surprises are simply due to analyst
inattention, however, then the analyst inattention variables should predict both positive and
negative extreme earnings surprises while the managerial inattention variables should only
predict PEES. In order to distinguish between the hypotheses, in supplemental tests we examine
extreme negative earnings surprises. For these tests we define the binary variable NEES_D which
equals one when a firm-quarter observation is in the bottom calendar-quarter SURPRISE decile
(e.g., the largest negative extreme earnings surprise values), and equals zero otherwise. By
contrasting the relation between our independent variables and PEES_D versus these variables’
relation with NEES_D, we can partially distinguish between the manager attention-seeking and
analyst inattention hypotheses.8
2.3 Control Variables
We include five control variables in all logistic regressions. These variables control for
the alternative explanation that firms who report PEES are simply more volatile and this makes
their earnings more difficult to predict. We use the standard deviation of ROA measured from t-
4 to t-1 (SD_ROA) to capture earnings volatility. In addition, the earnings of firms with high
8 Note that by construction, when PEES_D = 1, NEES_D = 0 and vice versa. Consequently, a variable that predicts PEES_D = 1, will weakly predict NEES_D = 0, and vice versa. 15
operating leverage (i.e., high fixed costs as a percent of total costs) are inherently difficult to
forecast because most of these firms’ profit (or lack thereof) occurs in the last few days of each
quarter. For these firms, neither the manager nor the analysts may know the earnings generated
during the quarter until right before the earnings announcement. Following Darrat and
Mukherjee (1995), we measure operating leverage (OP_LEV) as the slope coefficient from
regressing the change in earnings before interest and taxes on the change in sales, estimated via
rolling regressions using data from quarters t-20 through t-1. Both SD_ROA and OP_LEV are
expected to be positively associated with PEES_D.
Another possible explanation is that large earnings surprises occur when a firm has large
special items, and these special items are often, but not always, excluded from the I/B/E/S
consensus forecast. Both the fundamental uncertainty about the firm in this quarter, and the
uncertain treatment of the special item by I/B/E/S, may make it difficult to forecast EPS. We
include an indicator variable to capture large special items (SPEC_ITEMS_D), where the
variable takes the value of one when the absolute value of special items exceeds one percent of
total assets, and is zero otherwise. We expect large special items to be positively associated with
PEES_D.
Finally, the percent of shares owned by institutional investors (INST_OWN) proxies for
the market’s interest in a firm apart from analyst coverage, and the natural log of the firm’s
market capitalization (MVE) controls for the prior finding that larger firms generally provide
more disclosers and are followed by more analysts (Lang and Lundholm 1993, 1996). Further,
Vuolteenaho (2002) argues that small firms have greater idiosyncratic risk and consequently have more to gain by increasing their investor recognition. We expect INST_OWN and MVE to
be negatively correlated with PEES_D.
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2.4 Additional Predictions relating to the Managerial Attention-Seeking Hypothesis
In this section we discuss supplemental tests designed to explore PEES firm-quarter characteristics in greater detail. We first consider whether large positive earnings surprises are a
function of increases in revenue or decreases in expenses. As one investment adviser notes in a
quarterly letter to clients,
"there are two ways we can see a company grow its bottom line. One is through cost cutting...The problem with these types of gains is they are not sustainable...After a certain point, companies will have wrung all the gains from cost cutting that are available. The only way net income rises after that is from gains in revenues, [which is] a much more sustainable model for growth" (Myers 2010).
We conjecture that a large positive earnings surprise due to revenue growth is more eye-catching
than a large positive earnings surprise due to cost-cutting. While we expect both unexpected
revenue growth and unexpected expense reduction to be greater during a PEES firm-quarter, we
expect a stronger positive relation between PEES and revenue growth that between PEES and
expense reductions. Unexpected revenue growth (CH_REV) and unexpected expense reduction
(CH_OPEXP) are measured as the seasonal quarterly changes per share outstanding.
We also consider factors that might explain why management would want a PEES in this particular quarter. Specifically, we measure the firm’s ex-ante financing needs, and whether the firm raised money in the capital markets after a PEES quarter (presumably when the capital markets is paying more attention to the firm). We would expect that a manager would engage in activities aimed at raising the share price before accessing the capital markets (Erickson and
Wang 1999). Thus, we expect a positive relation between the PEES_D and both a firm’s ex-ante financing needs and future cash inflows from financing activities. Ex-ante financing
(ExAnte_Fin_Needs_D) needs is measured by an indicator variable that take the value of one
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when capital expenditures exceeds cash from operations over the past 12 quarters by more than
0.5 percent of current assets, and is zero otherwise. Future financing needs
(FUTURE_EQUITY_D) is measured as with an indicator variable set to one if the net equity
financing inflows over the subsequent four quarters are positive and zero otherwise.
Finally, in addition to the NOLOVE_D variable discussed earlier, we use a firm’s price-
to-earnings ratio relative to its industry (PE_RANK) as an additional attention proxy. Managers
care a great deal about “the multiple” used to value the stock price (Liu et al. 2002), and it is
plausible that managers with a low multiple feel neglected by the market. Consequently, we
predict that PEES firm-quarters are more likely for firms with low price-to-earnings ratios
relative to their industry peers.
3. DESCRIPTIVE STATISTICS
3.2 Descriptive Statistics, Industry Distribution, and Correlations
Our sample consists of 98,477 firm-quarter observations from January 1998 through June
2008 with calendar-quarter year-ends (e.g., March, June, September, and December) and non-
missing values for the variables included in our analysis.9 All variables are defined in detail in the Appendix, and all continuous variables are winsorized at the 1st and 99th percentiles by year to reduce the effect of outliers. Panel A of Table 1 presents our descriptive statistics for the
pooled sample. The median earnings surprise (SURPRISE_UNSCALED) is one cent per share,
with an interquartile range of negative two to positive four cents per share, and the mean
9 We begin our analysis in January 1998 because we require manager earnings guidance data from Thomson First Call’s Company Issued Guidance database. While this database begins in 1993, the data are not reliable until 1998 (Chuk et al. 2009). We end our analysis before the financial crisis began in fall 2008 so our results retain their generalizability. 18
earnings surprise per share as a percentage of stock price per share (SURPRISE) is negative 0.1
percent.
The mean GUIDE_D value is 0.25, indicating management provides earnings guidance in
less than one quarter of firm-year quarters. As CH_ROA is industry-median adjusted, the median value is zero. On average 6.4 analysts provide earnings forecasts for firm j (ANALYST_CNT), and each analyst provides earnings forecasts for 13.4 other firms in quarter t (ANALYST_BUSY) and has been issuing earnings forecasts as recorded in I/B/E/S for 6.1 years (ANALYST_EXP).
The analyst consensus forecast is measured an average of 17.6 days before firm j’s earnings announcement (FORECAST_STALE), and a one dollar change in sales is on average associated with a 30 percent change in earnings before interest and taxes (OP_LEV). Institutional ownership averages 55.3 percent and the average firm has a market capitalization of $3.5 billion.
While firms have ex-ante financing needs in 32.4 percent of all firm-quarters
(EXANTE_FIN_NEEDS_D), 43.2 percent of firms experience a net cash inflow from equity financing activities in the four quarters subsequent to period t (FUTURE_EQUITY_D). The average firm experiences an increase in analyst coverage of 0.7 analysts (CH_ANALYST_CNT) and an increase in institutional ownership of 4.8 percent (CH_INST_OWN) during the twelve quarters following period t, and the mean firm has been filing SEC forms as a publicly traded entity for approximately 15.9 years (AGE_UNLOGGED).
Panel B of Table 1 shows descriptive statistics after separating observations into PEES and non-PEES subsamples (e.g., PEES_D = 1 and PEES_D = 0, respectively). The results of two-sample tests of the difference in means between these two samples are broadly consistent with our hypotheses. Specifically, firm-quarter observations in the PEES subsample are less likely to have managers who provide earnings guidance (GUIDE_D) and more likely to have
19
managers who expect positive future economic performance (CH_ROA). PEES firm-quarters are also more likely to be associated with firms that exhibit characteristics of “neglected” firms –
these firms are smaller in market capitalization (MVE), not “feeling the love” from the market
(NOLOVE_D), and followed by fewer analysts (ANALYST_CNT) who are less experienced
(ANALYST_EXP) and issue less timely earnings forecasts (FORECAST_STALE) that have
greater dispersion (FORECAST_SD). In addition, firms in the PEES firm-quarter subsample
have greater variation and predictability in operating performance (SD_ROA and OP_LEV, respectively).
We also find that during a PEES quarter firms have greater growth in revenue (CH_REV) and less growth in expenses (CH_OPEXP) and are more likely to experience net financing cash inflows (FUTURE_EQUITY_D) during the four quarters following a PEES quarter. Consistent
with our hypothesis that managers create large positive earnings surprises to attract analyst and
investor attention, we find that firms experience greater increases in analyst following
(CH_ANALYST_CNT), institutional ownership (CH_INST_OWN), and trading volume
(CH_VOLUME_LONG) following a PEES quarter.
To gain a better understanding of PEES firms’ earnings surprises before and after a PEES
quarter, Figure 2 displays PEES firms’ SURPRISE ranking transition matrix during the four
periods prior and four periods subsequent to a PEES quarter. The figure shows that 33.3 percent
of firms with PEES_D = 1 in period t are also labeled as PEES firms in periods t-1 and t+1, with
this percentage declining monotonically as a firm gets farther away from a PEES quarter (e.g.,
down to 24.6 percent in periods t-4 and t+4). Another observation from Figure 2 is that a firm
classified as a PEES observation in quarter t is nearly two times as likely to be classified as a
NEES firm in periods t-4 through t-1 than one would expect on average, consistent with PEES
20
firms having greater earnings volatility (and highlighting the need to control for this
characteristic in our analysis).
Table 2 gives the distribution of the full sample and the PEES sub-sample across 48
industries (Fama and French 1997). If PEES firm-quarters are evenly distributed across
industries, the values in the far right ‘PEES ÷ Total’ column would equal ten percent. The
percentage of PEES firm-quarter observations relative to total observations within an industry
ranges from 2.1 to 19.5 percent. Many of the industries with a low percentage of PEES observations are low-beta industries that generate predictable financial results from period to period (e.g., Printing & Publishing, Aircraft, and Defense). On the other end, PEES observations represent more than 18 percent of the firm-period observations in the Pharmaceutical, Real
Estate, and Construction industries, which have unpredictable earnings streams that are heavily dependent upon macroeconomic factors and investor confidence. However, the fact that 3.4 percent of the Banking industry are PEES firm-quarter observations and 16.7 percent of the Steel
Works industry are PEES firm-quarter observations suggest that factors beyond industry classification determine which firm-quarter observations are in the PEES sub-sample. We include industry fixed effects in all regressions to ensure that our independent variables of interest are not simply proxying for industry differences. We also include year and fiscal quarter indicator variables to control for cross-sectional dependence in the data.
Table 3 provides the Pearson (Spearman) correlation coefficients in the upper (lower) portion of the correlation matrix. Our primary variables of interest are presented in Panel A, and the variables used in our supplemental tests are presented in Panel B. Similar to the descriptive statistics presented in Panel B of Table 1, the correlations are consistent with both the manager attention-seeking and analyst inattention variables influencing the likelihood of a positive
21 extreme earnings surprise in the predicted direction. Panel A shows that managers are less likely to provide earnings guidance (GUIDE_D) and more likely to have private information about the firm’s positive future prospects (CH_ROA) in a PEES firm-quarter. PEES firm-quarters are also more likely to be associated with firms with prior earnings performance that is not fully rewarded by the stock market (NOLOVE_D) with low analyst following (ANALYST_CNT) by analysts with less experience (ANALYST_EXP) who are busier (ANALYST_BUSY) and issue staler forecasts (ANALYST_STALE) with greater dispersion (FORECAST_SD). PEES firms are smaller (MVE) with a lower percentage of institutional investors (INST_OWN) and have more volatile and unpredictable earnings (SD_ROA and OP_LEV, respectively).
Panel B shows that greater future revenue growth (CH_REV) and future net financing cash inflows (FUTURE_EQUITY_D), less future expense growth (CH_OPEXP), and a lower
P/E multiple (PE_RANK) are correlated with PEES_D. Finally, we also find evidence that our four proxies for increases in attention (CH_ANALYST_CNT, CH_INST_OWN,
CH_VOLUME_LONG, and CH_VOLUME_SHORT) are positively correlated with PEES_D.
We note that ANALYST_CNT, INST_OWN, and MVE are all highly correlated, with correlation coefficients ranging between 0.41 and 0.70. Therefore, we test for multicollinearity by computing the variance inflation factor (VIF) for each independent variable in the full regression model. All independent variables included in our model have VIF values less than 2.6 (e.g., well under the general rule of thumb factor of 10.0 per Belsley et al. 1980), indicating multicollinearity is not an issue in our analysis.
4. RESULTS
4.1 Predicting Positive Extreme Earnings Surprises (PEES).
22
Our main analysis is conducted analyzing the output of a logistic model with our
independent variables sorted into two categories corresponding to our two hypothesized groups of PEES determinants (manager attention-seeking versus analyst inattention). We assess each hypothesis separately and then examine both together, controlling for earnings volatility
(SD_ROA), operating leverage (OP_LEV), special items (SPEC_ITEMS_D), institutional ownership (INST_OWN), and firm size (MVE) in all regression specifications.
In addition to the statistical significance of the coefficient estimates, we report the odds ratio for a one-standard deviation (unit) change in each continuous (binary) variable so readers can meaningfully interpret the size of the effect of each independent variable on the dependent variable. For continuous variables, this statistic is constructed as the odds of PEES_D = 1 occurring when an independent variable is one standard deviation higher than its mean, divided by the odds of PEES_D = 1 occurring when the variable is at its mean. An odds ratio equal to one means that PEES_D = 1 is equally likely at both levels of the independent variable, indicating that the independent variable has no effect on the dependent variable. Positive
(negative) odds ratios are associated with an increase (decrease) in the likelihood of the odds of the dependent variable being observed. The ratio controls for the unconditional ten percent probability of PEES_D = 1, as well as for the effect of the other variables in the model.10
Panel A of Table 4 presents the results from testing the manager attention-seeking and
analyst inattention hypotheses individually (Columns 1 and 2, respectively) and then both
hypotheses together (Column 3). Beginning with the manager attention-seeking hypothesis
10 For binary variables, the odds ratio is constructed as the odds of PEES_D=1 occurring when an independent variable is equal to one divided by the odds of PEES_D=1 occurring when the variable is equal to zero. To illustrate, suppose that the conditional probability of PEES_D=1 is 0.05 when management issues guidance (GUIDE=1) and 0.15 when management issues no guidance (GUIDE=0). Thus, if GUIDE=1, the odds of PEES_D=1 is 0.05 ÷ 0.95, or 0.053-to-1; if GUIDE=0, the odds of PEES_D=1 is 0.15 ÷ 0.85, or 0.176-to-1; The odds ratio is then 0.053 ÷ 0.176 = 0.301, meaning that PEES_D=1 is only 0.301 as likely when management gives guidance for quarter t versus when management does not. 23
(Column 1), all four proxies are highly statistically significant in the predicted direction. The
probability that the firm will experience a positive extreme earnings surprise is decreasing in
management-provided EPS guidance for the quarter (GUIDE_D) and increasing in managers’
private information about their firms’ future prospects (CH_ROA) and instances when the firm’s
prior year accounting performance exceeded the industry median but the firm’s prior year stock
performance did not (NOLOVE_D). Managers are also more likely to engage in net purchases during the month prior to PEES quarters relative to non-PEES quarters
(INSIDER_PURCHASES_D).
To give readers a sense of economic significance, the odds ratio when management issues guidance is 0.73, meaning that a positive extreme earnings surprise is only 0.73 as likely when management gives guidance for the quarter compared to when management does not. A one standard deviation increase in CH_ROA is associated with a 17 percent increase in the odds of a
PEES quarter occurring. When NOLOVE_D (INSIDER_PURCHASES_D) equals one, it is 1.73
(1.13) times more likely that a PEES occurs than when NOLOVE_D
(INSIDER_PURCHASES_D) equals zero. Not surprisingly, the size of the firm is one of the most influential predictor (e.g., an odds ratio farthest from one) of observing a PEES firm-
quarter. Column 1 shows that a standard deviation increase in MVE increases the odds of a firm
experiencing a PEES quarter by 0.51. The other four control variables are also highly significant in the predicted direction.
While we cannot directly observe managerial intent, our regression results are consistent
with the hypothesis that management is at least partially responsible for the large positive
earnings surprise. Specifically, management failed to provide earnings guidance (keeping
whatever information it had to itself), the firm has stronger future earnings prospects (which
24
management was presumably expecting given management’s private information), management
was not “feeling the love” from the market during the past year, and management was a net
purchaser of stock in the firm during the weeks preceding the firm’s earnings announcement.
Column 2 of Panel A of Table 4 gives the results for the analyst inattention hypothesis.
The likelihood of a PEES quarter significantly decreases with the number of analysts following
the firm (ANALYST_CNT) and significantly increases with how busy the analyst is
(ANALYST_BUSY) and the lack of consensus among analysts (FORECAST_SD).11 The largest
marginal effects come from the variation in individual analyst forecasts that comprise the
consensus forecast (e.g., a one standard deviation increase in FORECAST_SD increases the odds
by a factor of 1.31) and the number of analysts following the firm (e.g., a one standard deviation
increase in ANALYST_CNT lowers the odds of a PEES observation by a factor of 0.85). While
the analyst experience (ANALYST_EXP) coefficient is significantly different from zero in the
direction opposite of that predicted and the forecast staleness (FORECAST_STALE) coefficient is
not significantly different from zero, the odds ratios for these two variables are very close to one
(1.02 and 0.98, respectively), indicating these variables have little economic effect on the
model’s predictive ability.
The regression results in Columns 1 and 2 of Panel A of Table 4 are consistent with both
the managerial attention-seeking and analyst inattention hypotheses. We next assess the relative
contribution of each hypothesis using the full model specification in Column 3. Overall, the
coefficient estimates, their significance, and the corresponding odds ratios for all the variables
remain remarkably similar to the results presented in Columns 1 and 2. Among our hypothesized
11 Missing values of FORECAST_SD (N = 16,278, or 16.5 percent of the sample) are reset to zero. 25
causes for a PEES observation, NOLOVE_D has the most extreme odds ratio of 1.76, followed by FORECAST_SD with an odds ratio of 1.32 and GUIDE_D with an odds ratio of 0.79.12
We can summarize the marginal impact of the groups of variables associated with each
hypothesis by constructing a joint odds ratio, which is computed as the product of the odds ratio
associated with each individual variable (after inverting the odds ratio values that are less than
one). Figure 3 illustrates the joint odds ratios for each group of hypothesized variables and for
all three groups of variables together. For example, the 3.00 joint odds ratio for the management
attention-seeking variables (GUIDE_D, CH_ROA, NOLOVE_D, and
INSIDER_PURCHASES_D) is computed from Column 3, Panel A, Table 4 as follows:
3.00 = (1 ÷ 0.788) * 1.179 * 1.759 * 1.141). The joint odds ratio is interpreted as follows: after
controlling for all other variables in the full model, the combination of a 1 to 0 change in
GUIDE_D, a 0 to 1 change in NOLOVE_D and INSIDER_PURCHASES_D, and a one-standard
deviation increase in CH_ROA makes observing a PEES observation three times as likely. A
collective one standard deviation change in the five analyst inattention variables increases the
odds of an extreme positive earnings surprise by a factor of 1.59.13
The ‘Manager and Analyst Variables’ bar in Figure 3 shows the collective impact of the
four manager attention-seeking and five analyst inattention variables (a one standard deviation
(unit) change in the continuous (binary) variables in the predicted direction) make it almost five
times as likely a PEES will occur. Including a one standard deviation change in the four
continuous and a 0 to 1 change in the one binary control variables in the joint odds ratio analysis
12 When observations with reset FORECAST_SD values are omitted and the regression specification presented in Columns 2 and 3 of Panel A of Table 4 is re-estimated, untabulated results are similar to those presented (with the exception of ANALYST_CNT and ANALYST_EXP becoming insignificantly different from zero).
13 It is more conservative to report a joint odds ratio that includes all five analyst inattention variables (e.g., 1.59) rather than a joint odds ratio that includes only the three variables we found to be significantly associated with PEES_D in the predicted direction (e.g., 1.66). 26 for the full model yields a joint odds ratio of 17.68 (e.g., the odds of a positive extreme earnings surprise increases by a factor of nearly 18 to 1).
In sum, we find statistically and economically significant evidence consistent with our hypothesis that both management and analysts are partially responsible for large positive earnings surprises. While analysts do appear to devoting less attention to PEES firms, the joint odds ratio for the manager attention-seeking variables is 88 percent larger than the joint odds ratio for the analyst inattention variables, indicating managers play a much greater role in generating large positive earnings surprises.
4.2 Predicting Negative Extreme Earnings Surprises (NEES)
In this section we investigate the ability of our independent variables of interest to predict a negative extreme earnings surprise (NEES). We create the indicator variable NEES_D and set the variable equal to one when firm-quarter observations are in the bottom decile of the
SURPRISE ranking by calendar quarter (i.e., the largest negative earnings surprise values), and equal to zero otherwise. While the analyst inattention and control variables are expected to predict both positive and negative extreme earnings surprises, we do not expect the manager attention-seeking variables to predict negative earnings surprises, as managers presumably are not attempting to manufacture large negative surprises to attract investor attention when their firm’s future prospects are strong.
While management-provided guidance (GUIDE_D) may be negatively associated with both PEES and NEES firm-quarter observations, there is no reason to expect a positive association between NEES_D and CH_ROA, NOLOVE_D, or INSIDER_PURCHASES_D.
Consistent with our expectations, Column 4 of Panel A of Table 4 confirms that strong expected future performance (CH_ROA) and “no love” from the capital markets (NOLOVE_D) make it
27
less likely a firm will experience a NEES quarter, and there is no indication that insiders increase their stock holdings (INSIDER_PURCHASES_D) prior to a NEES quarter. These findings are in
contrast to the regression results presented in Column 3 and consistent with our expectations.
We do find that manager guidance (GUIDE_D) is negatively associated with both PEES and
NEES. Finally, all five of the control variables and three of the analyst inattention variables have
similar relations to both PEES and NEES.
4.3 Additional Evidence in Support of the Manager Attention-Seeking Hypothesis.
Panel B of Table 4 provides the results of additional tests that lend further support to the
manager attention-seeking hypothesis. We conjecture that management is more inclined to
create a positive extreme earnings surprise to attract investor attention if the earnings surprise is due to revenue growth rather than to cost-cutting measures. Therefore, we expect extreme positive earnings surprises to be more likely to be the result of higher than expected revenues
relative to lower than expected expenses. Descriptive statistics in Panel B of Table 1 show the
average seasonally-adjusted quarterly increase in revenue (CH_REV) of $0.73 per share for
PEES firm-quarters exceeds the average seasonally-adjusted quarterly increase in operating
expenses (CH_OPEXP) of $0.49 per share. Column 2 of Panel B of Table 4 shows that while
PEES quarters have a larger increase in seasonally adjusted revenue (CH_REV) and a larger
decrease in seasonally adjusted operating expenses (CH_OPEXP) relative to non-PEES
observations, the CH_REV coefficient is greater in magnitude than the CH_EXP coefficient
28
(χ2-statistic = 3.72, one-tailed p-value = 0.027), consistent with managers knowing that revenue
growth is a strong signal of future firm prospects relative to cost cutting.14
We also consider why a manager chooses quarter t relative to any other quarter to create a positive extreme earnings surprise. In other words, why does a PEES quarter occur now? We posit that managers of firms with significant ex-ante financing needs who plan to issue capital in the near future have a stronger incentive to attract investor attention prior to the new issuance in hopes of securing more favorable financing terms and/or broadening the firm’s investor base.
We use an ex-ante financing measure because we want to capture a firm’s need for capital in period t, regardless of whether that need is ultimately met subsequent to period t. We also predict that managers are more likely to raise capital after a PEES quarter relative to non-PEES quarters. We predict a positive relation between the probability of observing a PEES observation and both a firm’s ex-ante financing needs and future capital issuances.
Following Dechow et al. (2011), we proxy for a firm’s ex-ante financing needs with
EXANTE_FIN_NEEDS_D, a binary indicator variable set equal to one if a firm is expected to have negative future free cash flow that exceed its current assets balance, and set equal to zero otherwise. Following Bradshaw et al. (2006), we proxy for future capital increases with
FUTURE_EQUITY_D, an indicator variable equal to one if the net amount of cash flow received from external financing activities during quarters t+1 through t+4 is positive, and set equal to zero if the net amount is equal to zero or negative.
Column 2 of Panel B of Table 4 shows that the EXANTE_FIN_NEEDS_D coefficient of
0.26 is significantly different from zero with an odds ratio of 1.29, consistent with PEES firms
14 While analyst forecasted revenues and expenses are the ideal proxy for this regression specification, analyst forecasts of non-EPS data are limited. Therefore, we use a seasonally-adjusted quarterly change in revenue and expenses in our empirical tests. The Wald test is conducted after transforming CH_OPEXP values from negative to positive. 29
being more likely to have ex-ante financing needs and managers attempting to attract investor
attention in anticipation of a future capital offering. The FUTURE_EQUITY_D coefficient of
0.09 is also positive and significantly different from zero with an odds ratio of 1.10, consistent with firms being more likely to experience positive net cash from financing activities during the four quarters subsequent to a PEES quarter relative to a non-PEES quarter.15 As the decision to
raise capital is made many months prior to the actual issuance, our finding that PEES firm-
quarter observations precede financing cash inflows more often than non-PEES firm-quarters is
consistent with managers anticipating their firms’ future capital needs and creating a positive
extreme earnings surprise to attract investor attention. Column 3 includes the change in earnings
components and financing variables in the same regression specification with no change in
inferences.
Finally, while we believe the variable NOLOVE_D best captures when a firm’s strong
past earnings performance was not adequately rewarded by the stock market, we consider that a
manager may rely on a simpler heuristic like a price-to-earnings ratio to gauge market neglect.
Therefore, we add firms’ decile ranked price-to-earnings ratio (PE_RANK) as an additional
independent variable in our regression, where PE_RANK is defined such that a higher rank value
indicates a higher price-to-earnings ratio. We find a negative relation between PEES_D and
PE_RANK, consistent with our prediction that PEES firm-quarters are more likely when a firm’s
price-to-earnings ratio is low.16 This relation is incremental to the positive relation between
PEES_D and NOLOVE_D, suggesting that NOLOVE_D and PE_RANK capture different aspects
15 Inferences are similar using a continuous variable that captures the amount of cash inflow from external financing activities during quarters t+1 through t+4, although the variable is only significant at a five percent level using a one-tailed p-value (untabulated). 16 We do not include this variable in our main regression because its inclusion reduces our sample size by 20,112 observations (e.g., the difference in number of observations when moving from Column 3 to 4 of Panel B of Table 4) due to the variable’s positive annual earnings requirement. 30
of under appreciation by investors. In sum, the results in Panel B of Table 4 provide additional
support for the manager attention-seeking hypothesis, further distinguishing this hypothesis from
the analyst inattention hypothesis.
4.4 Did Attention Increase?
We now turn to our final set of regression results. If managers are attempting to attract
attention with an extreme positive earnings surprise, the next logical question to ask is whether
the managers were successful in increasing attention. Following a research design employed by
Bushee and Miller (2011), we test whether attention increases after a large positive earnings
surprise by regressing various proxies for increases in analyst and investor attention in quarters
subsequent to t on the binary variable PEES_D and several control variables. A positive
coefficient on the PEES_D variable indicates that PEES firms experience an increase in attention
incremental to non-PEES firms.
Our increase in attention proxies include the change in analyst following and institutional
ownership from quarter t to t+12 (CH_ANALYST_CNT and CH_INST_OWN, respectively) and
the change in trading volume (CH_VOLUME_LONG and CH_VOLUME_SHORT). We use institutional ownership as a proxy for overall investor attention, not to distinguish between institutional versus retail investor ownership. Control variables include prior year industry- adjusted stock returns (PY_RET) to account for the finding that analysts and institutions are attracted to past stock market winners (Lehavy and Sloan 2008), and future period change in return on assets (CH_ROA) to account for the relation between changes in firms’ future operating performance and changes in investor attention. We also include market capitalization (MVE) to control for firm size and the number of years a firm has been publicly traded (AGE) to control for
31 business life cycle, as smaller and younger firms are expected to have greater increases in analyst and investor attention relative to larger, more established firms.
Column 1 of Table 5 shows that analyst following increases by an additional 0.26 analysts during the three years following a PEES quarter relative to non-PEES quarters, and
Column 2 shows that institutional investors increase their ownership by an additional 1.4 percent during the three years following a PEES observation relative to the change in institutional investors’ holdings during the three years following non-PEES quarters. Columns 3 and 4 show that both short-term and long-term trading volume increase following PEES quarters relative to non-NEES quarters, consistent with large positive earnings surprises attracting investor attention and being associated with greater liquidity.17 In sum, the regression results in Table 5 indicate that if managers were attempting to attract attention with a positive extreme earnings surprise, the managers were successful.
4.5 Sensitivity Analyses
We perform the following sensitivity analyses on the full regression model presented in
Column 3 of Panel A of Table 4 to ensure that our results are robust to different variable definitions and data subsamples. The remainder of this section details the reasoning motivating each sensitivity test with no change in inferences relative to the tabulated regression results.
4.5.a. Eliminating negative earnings surprise observations. Our pooled regression approach uses all non-PEES firm-quarter observations as a control group. However, it is possible that the more appropriate control group is all non-PEES firm-quarter observations
17 In untabulated analysis we add the independent variable NEES_D to consider whether the PEES coefficient loading is merely capturing a small firm effect. We find that while NEES firm-quarters are also followed by increases in analyst following and trading volume, the PEES_D coefficient is statistically larger than the NEES_D coefficient in all regression specifications excluding the analyst following regression presented in Column 1. 32
conditional on a non-negative earnings surprise. This finding reduces the possibility that
systematic differences in our independent variables for negative versus positive earnings surprise
observations are driving our results. Eliminating all negative earnings surprise firm-quarter
observations (which reduces our sample by nearly fifty percent to 52,446 observations) and re-
setting PEES_D to be equal to one for the top decile of SURPRISE values in this reduced sample
(and zero otherwise) yields no qualitative change in inferences (excluding the ANALYST_STALE
coefficient switching from negative and significant to positive and significant, which is
consistent with our prediction).
4.5.b. Eliminating observations with a low stock price. We also consider if firms with
small stock price per share values (the denominator used to calculate the SURPRISE variable) are
driving our regression results. We re-estimate the full regression after eliminating 10,073
observations (10.2 percent of the sample) with a stock price per share of less than $5.00 and fail
to note any qualitative differences in regression results (with the exception of the
FORECAST_STALE coefficient becoming positive and significant as originally predicted).
4.5.c. Regulation Fair Disclosure. We also consider any possible confounding effects of
Regulation Fair Disclosure, which restricted management from making non-public, material
information available to only select parties. As the regulation (effective October 23, 2000)
changed the communication mechanisms and information flow between managers and security
market participants, we consider if the pre- or post-regulation period is driving the relation between our independent variables and the probability of observing a PEES firm-quarter. In the
pre-Reg FD period prior to October 2000 (N=23,381), there is no change in inferences related to either set of variables. Interestingly, in the post-Reg FD period (N=75,096), there is no change in
33
inferences related to the manager attention-seeking variables but the ANALYST_EXP and
FORECAST_STALE coefficients become insignificantly different from zero.
4.5.d. Alternate PEES_D definition. We also re-define our PEES indicator variable to equal one when a firm-quarter observation is in the top twentile (i.e., fifth percentile) of
SURPRISE values and equal to zero otherwise. Again, we document no change in inferences related to the manager attention-seeking variables but the ANALYST_EXP and
FORECAST_STALE coefficients become insignificantly different from zero. In sum, while some of the analyst inattention variables lose significance in the alternate regression specifications presented in Section 4.5, the relation between the existence of a positive extreme earnings surprise and the manager attention-seeking variables remains consistent in all sensitivity tests conducted.
5. CONCLUSION
This study examines the determinants and consequences of positive extreme earnings surprises (PEES). Instances where a firm’s quarterly EPS far exceeds the analyst consensus forecast are prima facie puzzling because it seems neither managers nor analysts have an incentive for this outcome to occur. We consider whether attention-seeking managers or
inattentive analysts are responsible for the large deviation we observe. We hypothesize and find
evidence consistent with both managers and analysts being responsible for a PEES outcome.
However, we find the greatest amount of support for our manager attention-seeking hypothesis. Specifically, we find that large positive earnings surprises are associated with managerial expectations of strong future firm performance, managers increasing their ownership in the firm prior to the earnings revelation, a lack of managerial earnings guidance, and a strong
34
need for external financing that is fulfilled in the months subsequent to the large positive
earnings surprise. While a one standard deviation increase in the five analyst inattention variables is associated with increasing the odds of a large positive earnings surprise by a factor of
1.6 to one, a standard deviation (zero to one) change in the continuous (binary) manager attention-seeking variables increases the odds of a large positive earnings surprise by a factor of
3.0 to one, and including the two sets of variables together (along with the control variables)
increases the odds by 17.7 to one! Further, if managers created the earnings surprise to attract
attention, they were successful. We find a significant increase in analyst following, institutional
ownership, and trading volume following a PEES firm-quarter relative to non-PEES firm-
quarters, consistent with these large positive earnings surprises to garner capital markets
attention.
Prior research has considered extreme earnings surprises as an exogenous event that
precipitates other important capital market events, most notably a drift in stock price subsequent
to the earnings announcement (Livnat and Mendenhall 2006; Doyle et al. 2006). Our results
show that large positive earnings surprises are partially predictable, suggesting that part of the
explanation for the subsequent drift in stock prices could be due to factors that give rise to the
surprise in the first place. We explore the implications of this finding in subsequent work.
35
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APPENDIX
AGEjt = natural log of the number of years firm j has been traded on a public stock exchange [(Compustat datadatej less earliest CRSP caldtj) ÷ 365.33] as of period t;
ANALYST_BUSYjt = mean number of other firms each analyst whose EPSjt forecast is included in the consensus EPSjt forecast is providing earnings forecasts for in period t;
ANALYST_CNTjt = number of analysts issuing forecasts included in the most recent I/B/E/S analyst consensus EPSjt forecast prior to the earnings announcement datejt;
ANALYST_EXPjt = mean number of years the analysts whose EPSjt forecast is included in the consensus EPSjt forecast have provided earnings forecasts on I/B/E/S as of period t;
CH_ANALYST_CNTjt = 3-year change in the number of analysts issuing forecasts included in the I/B/E/S EPSj analyst consensus forecast (t to t+12);
CH_INST_OWNjt = 3-year change in the percentage of outstanding sharesj held by institutional Investors (t to t+12);
CH_OPEXPjt = seasonal quarterly change in operating expensesj,t-4 to j,t (Compustat item xoprq), scaled by millions of shares outstandingj,t-4 (Compustat item cshoq);
CH_REVjt = seasonal quarterly change in revenuej,t-4 to j,t (Compustat item revtq), scaled by millions of shares outstandingj,t-4 (Compustat item cshoq);
CH_ROAjt = 1-year change in the mean industry-adjusted return on assets (ROAj), where past ROAj = the sum of net income jt-4 to jt-1 (Compustat item niq) ÷ total assets jt-1 (Compustat item atq) and future ROAj = the sum of net income jt+1 to jt+4 (Compustat item niq) ÷ total assets jt+1, with past and future ROA adjusted for the Fama-French 48 industry median ROA;
CH_VOLUME_LONGjt = 3-year change in the ratio of average daily trading volume to daily shares outstanding (CRSP vol ÷ 1000 ) ÷ (CRSP shares), measured during the last month of period t+12 and the last month of period t;
CH_VOLUME_SHORTjt = change in the ratio of average daily trading volume to daily shares outstanding (CRSP vol ÷ 1000 ) ÷ (CRSP shares), measured during the day of through four days after firm j’s period t earnings announcement and the five days prior to firm j’s period t earnings announcement;
EXANTE_FIN_NEEDS_Djt = binary indicator variable set equal to 1 if cash flow from operations (Compustat item oancf) less capital expenditures (Compustat item capx) averaged over the past twelve quarters as a percentage of current assets (Compustat item act) is less than -0.5, and set equal to 0 otherwise;
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FUTURE_EQUITY_Djt = binary indicator variable set equal to 1 if net cash flow from equity financing activities cumulated over quarters t+1 through t+4 is positive, and set equal to 0 otherwise. Net cash flow from equity financing activities is defined as cash inflow from the sale of common and preferred stock (Compustat item sstky) less cash outflow from the purchase of common and preferred stock (Compustat item prstkcy) and the payment of dividends (Compustat item dvy);
FORECAST_SDjt = standard deviation of individual analyst EPSjt forecasts that comprise the consensus EPSjt forecast, with missing observations (N=27,324) reset to zero;
FORECAST_STALEjt = number of days between the most recent I/B/E/S analyst consensus EPSjt forecastjt prior to the earnings announcement datejt and the earnings announcement datejt;
GUIDE_Djt = binary variable set equal to 1 if managementj provides any type of EPSjt guidance prior to the earnings announcement datejt, and set equal to 0 otherwise;
INSIDER_PURCHASES_Djt = binary indicator variable set equal to 1 if the sum of all shares traded by any firm j insider during the four weeks preceding the week of firm j's earnings announcement is positive (indicating a net purchase), and set equal to 0 otherwise;
INST_OWNjt = percentage of outstanding sharesj held by institutional investors as of the end of the calendar quarter prior to period t;
MVEjt = natural log of market capitalizationjt-1, defined as millions of shares outstandingjt-1 multiplied by price per sharejt-1 (Compustat items cshoq * prccq);
NEES_Djt = binary variable set equal to 1 if the firm-quarter observation is in the bottom decile when firm-quarter observations are ranked by calendar quarter in descending SURPRISEjt value order, and set equal to 0 otherwise;
NOLOVE_Djt = binary variable set equal to 1 if i) average ROAj in periods t-4 through t-1 is greater than industry median ROA over the same period, and ii) prior 1-year market-adjusted buy-and-hold stock return (measured beginning 252 trading days and ending two trading days before firm j’s period t earnings announcement date) is less than the industry median 1-year value-weighted market-adjusted buy-and-hold stock return over the same time period, and set equal to 0 otherwise;
OP_LEVjt = slope coefficient from regressing change in quarterly earnings before interest and taxes (Compustat items niq + xintq + txtq) on change in quarterly sales (Compustat item revtq), estimated via rolling regressions using the twenty quarters of data prior to period t; with missing observations (N=68,588) reset to the industry quarter median;
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PE_RANKjt = firm j’s price-to-earnings ratio (defined as price per sharet-1 divided by quarterly earnings per share cumulated from quarters t-4 to t-1, or Compustat items prccq ÷ (niq ÷ cshoq)) less the industry median price-to-earnings ratio measured over the same period, decile ranked so the largest P/E values are in the highest rank, omitting firms with negative earnings;
PEES_Djt = binary variable set equal to 1 if the firm-quarter observation is in the top decile when firm-quarter observations are ranked by calendar quarter in descending SURPRISEjt value order, and set equal to 0 otherwise;
PY_RETjt = firm j’s prior year market-adjusted buy-and-hold stock return (BHR), where BHRj is measured beginning 252 trading days and ending two trading days before firm j’s period t earnings announcement date and BHRj is market-adjusted by subtracting the return on a value-weighted market portfolio from firm j’s raw stock return each day;
SD_ROAjt = standard deviation of quarterly return on assets (ROA) in periods t-4 through t-1, where ROAjt = net income ÷ total assets (Compustat items niq ÷ atq)
SPEC_ITEMSjt = binary variable set equal to 1 if the absolute value of quarterly special items (Compustat spiq) is greater than one percent of total assets, with missing values (N=6,426) set equal to zero;
SURPRISEjt = (I/B/E/S unsplit-adjusted actual EPSjt – most recent I/B/E/S median analyst consensus EPSjt forecast issued prior to the earnings announcement datejt) ÷ price per sharejt;
SURPRISE_UNSCALEDjt= I/B/E/S unsplit-adjusted actual EPSjt – most recent I/B/E/S median analyst consensus EPSjt forecast issued prior to the earnings announcement datejt;
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FIGURE 1
Mean and Minimum SURPRISE values by Year-Quarter for PEES Observations
SURPRISE Minimum SURPRISE Mean
0.030
0.025
0.020
0.015
0.010
0.005
0.000
The vertical axis of this figure shows the time-series variation in the mean and minimum SURPRISE values by year-quarter for the 9,833 firm-year observations in the top SURPRISE decile (e.g., the subsample for which PEES_D = 1).
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. FIGURE 2
SURPRISE Ranking Transition Matrix: Period t PEES Observation Rankings in Periods t-4 through t+4
100.0
90.0
80.0 RANK = 10 RANK = 9 70.0 RANK = 8
60.0 RANK = 7 RANK = 6 50.0 RANK = 5 RANK = 4 40.0 RANK = 3 30.0 RANK = 2 RANK = 1 20.0
10.0
0.0 t - 4 t - 3 t - 2 t - 1 t t + 1 t + 2 t + 3 t + 4
SURPRISE ranking distribution of the 9,833 firm-year observations in quarters t-4 through t+4 which have PEES_D = 1 (RANK = 10 in this figure) in quarter t.
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FIGURE 3 Joint Odds Ratios by Hypothesis
20.00
18.00 17.68 16.00
14.00
12.00
10.00
8.00
6.00
4.00 4.78
2.00 3.00 1.59 0.00 Manager Attention- Analyst Inattention Manager & Analyst Manager & Analyst Seeking Variables Variables Variables Variables with Controls
This figure presents joint odds ratios by hypothesis, where a joint odds ratio is computed as the product of the odds ratio associated with each individual variable (presented in Panel A of Table 4) after inverting the odds ratio values for variables predicted to have a negative relation with the dependent variable. The ‘Manager Attention-Seeking’ joint odds ratio is based on a 1 to 0 (0 to 1) change in GUIDE_D (NOLOVE_D and INSIDER_PURCHASES_D) and a one standard deviation increase in CH_ROA. The ‘Analyst Inattention’ joint odds ratio is based on a one standard deviation decrease (increase) in ANALYST_CNT and ANALYST_EXP (ANALYST_BUSY, FORECAST_STALE, and FORECAST_SD). The ‘Combined’ joint odds ratio is the collective impact of these two joint odds ratios, and the ‘Combined with Controls’ joint odds ratio combines the effect of these two joint odds ratios with a one standard deviation increase (decrease) in the control variables SD_ROA and OP_LEV (INST_OWN and MVE) and a 0 to 1 change in SPEC_ITEMS_D.
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TABLE 1 Descriptive Statistics
Panel A: Pooled Sample
Variable N Mean Std Dev P25 P50 P75 PEES_D 98,477 0.100 0.300 0.000 0.000 0.000 NEES_D 98,477 0.100 0.300 0.000 0.000 0.000 SURPRISE 98,477 -0.002 0.016 -0.001 0.000 0.002 SURPRISE_UNSCALED 98,477 0.000 0.128 -0.020 0.010 0.040 GUIDE_D 98,477 0.252 0.434 0.000 0.000 1.000 CH_ROA 98,477 -0.001 0.043 -0.009 0.000 0.007 NOLOVE_D 98,477 0.195 0.396 0.000 0.000 0.000 INSIDER_PURCHASES_D 98,477 0.115 0.319 0.000 0.000 0.000 ANALYST_CNT 98,477 6.433 5.560 2.000 5.000 9.000 ANALYST_EXP 98,477 6.089 2.725 4.248 5.898 7.597 ANALYST_BUSY 98,477 13.365 5.382 10.000 12.500 15.714 FORECAST_STALE 98,477 17.592 18.404 7.000 13.000 22.000 FORECAST_SD 98,477 0.026 0.041 0.010 0.010 0.030 SD_ROA 98,477 0.016 0.025 0.003 0.007 0.017 OP_LEV 98,477 0.302 0.317 0.120 0.225 0.483 SPEC_ITEMS_D 98,477 0.081 0.273 0.000 0.000 0.000 INST_OWN 98,477 0.553 0.272 0.335 0.575 0.777 MVE 98,477 6.555 1.733 5.307 6.440 7.664 MVE_UNLOGGED 98,477 3,483 9,672 202 627 2,131 CH_REV 86,622 0.678 1.928 -0.012 0.265 0.884 CH_OPEXP 96,636 0.549 1.650 -0.008 0.202 0.666 EXANTE_FIN_NEEDS_D 98,432 0.324 0.468 0.000 0.000 1.000 FUTURE_EQUITY_D 87,745 0.432 0.495 0.000 0.000 1.000 PE_RANK 75,445 0.503 0.313 0.222 0.556 0.778 CH_ANALYST_CNT 55,776 0.726 3.445 -1.000 0.000 3.000 CH_INST_OWN 55,776 0.048 0.261 -0.015 0.069 0.172 CH_VOLUME_LONG 68,275 1.475 3.254 -0.146 0.509 1.710 CH_VOLUME_SHORT 68,074 0.004 0.008 0.000 0.001 0.005 PY_RET 98,477 0.060 0.554 -0.256 -0.028 0.240 AGE 98,452 2.473 0.836 1.860 2.434 3.063 AGE_UNLOGGED 98,452 15.886 15.763 5.425 10.407 20.384
The sample consists of the 98,477 firm-quarter observations from January 1998 through June 2008 with non-missing values for the variables included in our regression analysis. All variables are defined in the Appendix, and continuous variables are winsorized at the 1st and 99th percentiles by year to reduce the effect of outlier. In Panel B, *** and ** indicates the variable mean is significantly different between the two groups at the one and five percent level, respectively, in the predicted direction using a two-tailed t-test.
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TABLE 1 Descriptive Statistics Panel B: PEES_D = 1 vs. PEES_D = 0
PEES_D = 1 Pred. Diff. in PEES_D = 0 Variable N Mean Std Dev P50 Diff. Mean N Mean Std Dev P50 PEES_D 9,833 1.000 0.000 1.000 88,644 0.000 0.000 0.000 NEES_D 9,833 0.000 0.000 0.000 88,644 0.111 0.314 0.000 SURPRISE 9,833 0.014 0.011 0.010 > *** 88,644 -0.003 0.015 0.000 SURPRISE_UNSCALED 9,833 0.148 0.129 0.100 > *** 88,644 -0.016 0.117 0.000 GUIDE_D 9,833 0.165 0.371 0.000 < *** 88,644 0.262 0.440 0.000 CH_ROA 9,833 0.010 0.058 0.005 > *** 88,644 -0.003 0.041 0.000 NOLOVE_D 9,833 0.314 0.464 0.000 > *** 88,644 0.182 0.386 0.000 INSIDER_PURCHASES_D 9,833 0.105 0.306 0.000 > 88,644 0.116 0.320 0.000 ANALYST_CNT 9,833 4.200 4.161 3.000 < *** 88,644 6.681 5.640 5.000 ANALYST_EXP 9,833 5.973 3.141 5.531 < *** 88,644 6.102 2.674 5.934 ANALYST_BUSY 9,833 12.971 5.277 12.500 > 88,644 13.409 5.392 12.500 FORECAST_STALE 9,833 19.440 22.316 14.000 > *** 88,644 17.387 17.906 13.000 FORECAST_SD 9,833 0.036 0.057 0.010 > *** 88,644 0.025 0.039 0.010 SD_ROA 9,833 0.027 0.034 0.014 > *** 88,644 0.015 0.024 0.006 OP_LEV 9,833 0.317 0.338 0.234 > *** 88,644 0.300 0.314 0.224 SPEC_ITEMS_D 9,833 0.114 0.318 0.000 ? *** 88,644 0.078 0.268 0.000 INST_OWN 9,833 0.467 0.276 0.447 < *** 88,644 0.563 0.270 0.588 MVE 9,833 5.613 1.570 5.403 < *** 88,644 6.660 1.718 6.544 MVE_UNLOGGED 9,833 1,192 4,053 222 < *** 88,644 3,737 10,073 695 CH_REV 9,373 0.732 2.208 0.149 > *** 77,249 0.672 1.891 0.281 CH_OPEXP 9,618 0.486 1.893 0.072 < *** 87,018 0.556 1.620 0.217 EXANTE_FIN_NEEDS_D 9,825 0.327 0.469 0.000 > 88,607 0.324 0.468 0.000 FUTURE_EQUITY_D 8,967 0.545 0.498 1.000 > *** 78,778 0.419 0.493 0.000 PE_RANK 4,992 0.413 0.343 0.333 < *** 70,453 0.509 0.309 0.556 CH_ANALYST_CNT 4,963 0.963 3.190 1.000 > *** 50,813 0.703 3.468 0.000 CH_INST_OWN 4,963 0.087 0.283 0.103 > *** 50,813 0.044 0.258 0.067 CH_VOLUME_LONG 6,359 1.982 4.135 0.593 > *** 61,916 1.423 3.145 0.503 CH_VOLUME_SHORT 6,335 0.004 0.009 0.002 > 61,739 0.004 0.008 0.001 PY_RET 9,833 -0.012 0.604 -0.121 < *** 88,644 0.068 0.548 -0.021 AGE 9,830 2.297 0.793 2.266 < *** 88,622 2.492 0.838 2.454 AGE_UNLOGGED 9,830 12.912 13.553 8.643 < *** 88,622 16.216 15.956 10.640
The sample consists of the 98,477 firm-quarter observations from January 1998 through June 2008 with non-missing values for the variables included in our regression analysis. All variables are defined in the Appendix, and continuous variables are winsorized at the 1st and 99th percentiles by year to reduce the effect of outlier. In Panel B, *** and ** indicates the variable mean is significantly different between the two groups at the one and five percent level, respectively, in the predicted direction using a two-tailed t-test.
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TABLE 2 Industry Distribution
PEES Sample Total Sample PEES ÷ Total Industry Count % Count % % Pharmaceutical Products 1,173 11.9% 6,013 6.1% 19.5% Real Estate 69 0.7% 371 0.4% 18.6% Construction 185 1.9% 998 1.0% 18.5% Steel Works 242 2.5% 1,448 1.5% 16.7% Telecommunications 405 4.1% 2,774 2.8% 14.6% Coal 34 0.3% 239 0.2% 14.2% Agriculture 21 0.2% 158 0.2% 13.3% Computer Hardware 289 2.9% 2,213 2.2% 13.1% Textiles 53 0.5% 406 0.4% 13.1% Ships & Railroad Equipment 27 0.3% 210 0.2% 12.9% Petroleum & Natural Gas 538 5.5% 4,220 4.3% 12.7% Fabricated Products 14 0.1% 111 0.1% 12.6% Automobiles & Trucks 158 1.6% 1,255 1.3% 12.6% Insurance 509 5.2% 4,363 4.4% 11.7% Precious Metals 78 0.8% 671 0.7% 11.6% Transportation 323 3.3% 2,793 2.8% 11.6% Apparel 102 1.0% 906 0.9% 11.3% Business Services 1,190 12.1% 11,218 11.4% 10.6% Electronic Equipment 639 6.5% 6,105 6.2% 10.5% Rubber & Plastic Products 67 0.7% 643 0.7% 10.4% Financial Services 341 3.5% 3,285 3.3% 10.4% Recreation 63 0.6% 607 0.6% 10.4% Entertainment 120 1.2% 1,169 1.2% 10.3% Shipping Containers 33 0.3% 339 0.3% 9.7% Machinery 309 3.1% 3,192 3.2% 9.7% Retail 215 2.2% 2,291 2.3% 9.4% Computer Software 152 1.5% 1,659 1.7% 9.2% Beer & Liquor 23 0.2% 256 0.3% 9.0% Medical Equipment 317 3.2% 3,572 3.6% 8.9% Chemicals 153 1.6% 1,767 1.8% 8.7% Utilities 280 2.8% 3,303 3.4% 8.5% Candy & Soda 15 0.2% 177 0.2% 8.5% Construction Materials 125 1.3% 1,483 1.5% 8.4% Miscellaneous 57 0.6% 679 0.7% 8.4% Mining 36 0.4% 433 0.4% 8.3% Measuring & Control Equip. 188 1.9% 2,267 2.3% 8.3% Wholesale 200 2.0% 2,537 2.6% 7.9% Consumer Goods 99 1.0% 1,307 1.3% 7.6% Health Care 112 1.1% 1,545 1.6% 7.2% Electrical Equipment 91 0.9% 1,257 1.3% 7.2% Business Supplies 86 0.9% 1,210 1.2% 7.1% Food Products 75 0.8% 1,080 1.1% 6.9% Personal Services 76 0.8% 1,107 1.1% 6.9% Restaurants & Lodging 92 0.9% 1,440 1.5% 6.4% Defense 9 0.1% 172 0.2% 5.2% Aircraft 24 0.2% 496 0.5% 4.8% Tobacco Products 3 0.0% 75 0.1% 4.0% Banking 409 4.2% 11,994 12.2% 3.4% Printing & Publishing 14 0.1% 663 0.7% 2.1% 9,833 100% 98,477 100%
The sample consists of the 98,477 firm-quarter observations between January 1998 and June 2008 with non-missing values for the variables included in our regression analysis. Fama-French (1997) industry classifications are based on four-digit SIC codes. The ‘PEES ÷ Total’ column compares the number of industry observations where PEES_D = 1 relative to the full sample. If an industry has a greater (fewer) number of firm-quarter observations with PEES_D=1 than expected based on an even distribution of PEES sample observations, ‘PEES ÷ Total’ value is greater (less) than 10 percent.
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TABLE 3 Correlation Matrix (Spearman \ Pearson)
Panel A: Main Variables [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [1] PEES_D --- -0.111 0.325 -0.067 0.085 0.100 -0.011 -0.134 -0.014 -0.024 0.033 0.084 0.148 0.017 0.040 -0.106 -0.181 [2] NEES_D -0.111 --- -0.617 -0.062 -0.157 -0.040 -0.018 -0.179 -0.017 -0.053 0.168 0.098 0.135 -0.001 0.117 -0.147 -0.241 [3] SURPRISE 0.518 -0.516 --- 0.019 0.188 0.070 0.010 0.087 -0.005 0.023 -0.132 -0.082 -0.059 0.006 -0.102 0.089 0.133 [4] GUIDE_D -0.067 -0.062 0.030 --- -0.022 -0.018 0.023 0.189 0.065 -0.114 -0.052 -0.073 -0.023 -0.126 0.059 0.224 0.134 [5] CH_ROA 0.110 -0.203 0.264 -0.026 --- 0.255 -0.002 0.013 0.007 0.010 -0.009 -0.040 0.075 0.001 -0.164 0.022 0.027 [6] NOLOVE_D 0.100 -0.040 0.102 -0.018 0.415 --- -0.002 0.008 0.009 0.031 0.013 -0.017 0.038 0.009 -0.040 -0.028 -0.021 [7] INSIDER_PURCHASES_D -0.011 -0.018 0.009 0.023 0.000 -0.002 --- 0.057 0.030 0.046 -0.006 0.017 -0.035 0.008 -0.008 0.052 0.079 [8] ANALYST_CNT -0.155 -0.218 0.062 0.211 0.025 -0.005 0.056 --- 0.090 -0.010 -0.137 0.130 -0.092 -0.015 0.005 0.407 0.695 [9] ANALYST_EXP -0.031 -0.038 0.010 0.086 0.008 0.010 0.038 0.182 --- 0.182 -0.045 0.062 -0.071 -0.087 -0.006 0.155 0.175 [10] ANALYST_BUSY -0.019 -0.056 0.007 -0.103 0.009 0.032 0.051 0.070 0.225 --- -0.040 0.077 -0.160 0.193 -0.081 -0.068 0.065 [11] FORECAST_STALE 0.021 0.106 -0.076 -0.040 -0.016 0.011 -0.006 -0.112 -0.043 -0.025 --- -0.077 0.051 0.019 0.012 -0.144 -0.144 [12] FORECAST_SD 0.020 -0.014 0.023 -0.034 -0.045 -0.019 0.032 0.430 0.138 0.131 -0.062 --- 0.024 0.034 0.023 0.144 0.198 [13] SD_ROA 0.166 0.165 0.028 0.032 -0.001 0.015 -0.057 -0.083 -0.053 -0.296 0.026 0.049 --- -0.007 0.139 -0.104 -0.201 [14] OP_LEV 0.013 -0.003 -0.006 -0.139 -0.009 0.010 0.007 -0.042 -0.124 0.224 0.017 -0.006 -0.125 --- -0.034 -0.099 -0.025 [15] SPEC_ITEMS_D 0.040 0.117 -0.045 0.059 -0.160 -0.040 -0.008 -0.001 -0.010 -0.080 0.008 0.015 0.166 -0.032 --- 0.003 -0.051 [16] INST_OWN -0.104 -0.144 0.067 0.224 0.042 -0.027 0.051 0.487 0.195 0.007 -0.096 0.260 -0.014 -0.117 0.002 --- 0.422 [17] MVE -0.183 -0.243 0.041 0.139 0.063 -0.022 0.077 0.705 0.241 0.125 -0.085 0.350 -0.193 -0.045 -0.053 0.456 ---
The sample consists of the 98,477 firm-quarter observations from January 1998 through June 2008 with non-missing values for the variables included in our regression analysis. All variables are defined in the Appendix, and continuous variables are winsorized at the 1st and 99th percentiles by year to reduce the effect of outliers. Spearman (Pearson) correlation coefficients s are presented in the lower (upper) portion of the matrix. Correlations significant at the 5 percent level are in bold.
TABLE 3 Correlation Matrix (Spearman \ Pearson)
Panel B: Supplemental Test Variables [A] [B] [C] [D] [E] [F] [G] [H] [I] [J] [K] [L] [M] [N] [A] PEES_D --- -0.111 0.325 0.010 -0.013 0.002 0.077 -0.076 0.022 0.047 0.050 0.020 -0.043 -0.070 [B] NEES_D -0.111 --- -0.617 -0.106 -0.041 0.005 0.075 -0.017 0.002 0.026 0.023 -0.019 -0.160 -0.078 [C] SURPRISE 0.518 -0.516 --- 0.095 0.016 -0.021 -0.010 -0.013 0.011 -0.016 0.009 0.033 0.140 0.043 [D] CH_REV -0.039 -0.181 0.138 --- 0.917 -0.002 -0.005 0.009 0.045 -0.045 0.043 0.050 0.130 0.037 [E] CH_OPEXP -0.080 -0.076 0.019 0.868 --- -0.047 -0.001 -0.006 0.029 -0.039 0.036 0.049 0.066 0.033 [F] EXANTE_FIN_NEEDS_D 0.002 0.005 -0.035 0.019 -0.042 --- -0.109 -0.028 0.027 0.018 0.069 -0.148 -0.026 -0.011 [G] FUTURE_EQUITY_D 0.077 0.075 0.026 -0.019 -0.019 -0.109 --- 0.135 0.120 0.095 0.051 0.113 0.130 -0.267 [H] PE_RANK -0.076 -0.017 -0.050 0.036 0.012 -0.028 0.135 --- 0.066 -0.022 0.001 0.066 0.085 -0.074 [I] CH_ANALYST_CNT 0.022 0.000 0.042 0.062 0.030 0.029 0.110 0.061 --- 0.180 0.196 -0.002 0.183 -0.049 [J] CH_INST_OWN 0.058 0.033 0.023 -0.066 -0.062 0.002 0.114 -0.027 0.206 --- 0.143 -0.087 0.020 -0.093 [K] CH_VOLUME_LONG 0.017 -0.030 0.039 0.087 0.065 0.124 -0.022 0.012 0.229 0.171 --- -0.087 0.001 -0.046 [L] CH_VOLUME_SHORT 0.021 -0.037 0.075 0.099 0.091 -0.169 0.089 0.043 -0.005 -0.059 -0.096 --- 0.097 -0.079 [M] PY_RET -0.072 -0.204 0.142 0.254 0.112 0.008 0.061 0.076 0.201 -0.005 0.084 0.064 --- 0.004 [N] AGE -0.070 -0.075 -0.005 0.054 0.041 0.002 -0.262 -0.073 -0.035 -0.100 0.063 -0.034 0.077 ---
The sample consists of the 98,477 firm-quarter observations from January 1998 through June 2008 with non-missing values for the variables included in our regression analysis. All variables are defined in the Appendix, and continuous variables are winsorized at the 1st and 99th percentiles by year to reduce the effect of outliers. Spearman (Pearson) correlation coefficients s are presented in the lower (upper) portion of the matrix. Correlations significant at the 5 percent level are in bold.
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TABLE 4 Logistic Regression Results Panel A: Main Analyses (1) (2) (3) (4) Y = PEES_D Y = PEES_D Y = PEES_D Y = NEES_D Manager Attention-Seeking Analyst Inattention Combined Combined Pred. Odds Pred. Odds Variable Sign Coeff. Pr>Χ 2 Ratio Coeff. Pr>Χ 2 Odds Ratio Coeff. Pr>Χ 2 Odds Ratio Sign Coeff. Pr>Χ 2 Ratio GUIDE_D − -0.317 <.0001 0.729 -0.238 <.0001 0.788 ? -0.125 0.0001 0.882 CH_ROA + 3.569 <.0001 1.167 3.804 <.0001 1.179 ? -7.957 <.0001 0.709 NOLOVE_D + 0.550 <.0001 1.733 0.565 <.0001 1.759 ? -0.237 <.0001 0.789 INSIDER_PURCHASES_D + 0.124 0.0008 1.132 0.132 0.0004 1.141 ? 0.035 0.3838 1.035 ANALYST_CNT − -0.030 <.0001 0.845 -0.031 <.0001 0.841 − -0.060 <.0001 0.716 ANALYST_EXP − 0.008 0.0381 1.023 0.007 0.0695 1.021 − 0.019 <.0001 1.052 ANALYST_BUSY + 0.012 <.0001 1.064 0.010 0.0001 1.055 + -0.013 <.0001 0.931 FORECAST_STALE + -0.001 0.1041 0.984 -0.001 0.048 0.981 + 0.013 <.0001 1.280 FORECAST_SD + 6.520 <.0001 1.308 6.772 <.0001 1.322 + 10.063 <.0001 1.514 SD_ROA + 5.849 <.0001 1.158 6.843 <.0001 1.187 5.231 <.0001 1.140 + 5.405 <.0001 1.145 OP_LEV + 0.254 <.0001 1.084 0.210 <.0001 1.069 0.207 <.0001 1.068 + 0.097 0.0251 1.031 SPEC_ITEMS_D +/− 0.327 <.0001 1.386 0.153 <.0001 1.043 0.280 <.0001 1.324 +/− 0.718 <.0001 2.050 INST_OWN − -0.582 <.0001 0.854 -0.634 <.0001 0.842 -0.611 <.0001 0.847 − -0.473 <.0001 0.879 MVE − -0.388 <.0001 0.511 -0.389 <.0001 0.51 -0.383 <.0001 0.515 − -0.555 <.0001 0.383
N where Y=1 9,833 9,833 9,833 9,827 Total N 98,477 98,477 98,477 98,477 Likelihood Ratio Χ 2 7,855 7,624 8,745 13,838 R-Square 0.077 0.075 0.085 0.131 Max-rescaled R-Square 0.161 0.156 0.178 0.275 Kendall’s Tau-a 0.093 0.094 0.099 0.118 % Concordant / Discordant 75.6 / 23.8 75.8 / 23.6 77.2 / 22.2 82.7 / 16.8
The sample consists of the 98,477 firm-quarter observations from January 1998 through June 2008 with non-missing values for the variables included in our regression analysis. All variables are defined in the Appendix, and continuous variables are winsorized at the 1st and 99th percentiles by year to reduce the effect of outliers. The odds ratio column is a measure of the magnitude of each independent variable’s effect on the likelihood of Y =1 when continuous (binary) independent variables change by one standard deviation (change from a value of zero to one). Industry, year, and fiscal quarter indicator variables are included in each regression; coefficients are not reported for brevity.
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TABLE 4 Logistic Regression Results
Panel B: Supplemental Analyses (1) (2) (3) (4) Y = PEES_D Y = PEES_D Y = PEES_D Y = PEES_D Pred. Odds Odds Odds Odds Variable Sign Coeff. Pr>Χ 2 Ratio Coeff. Pr>Χ 2 Ratio Coeff. Pr>Χ 2 Ratio Coeff. Pr>Χ 2 Ratio GUIDE_D − -0.224 <.0001 0.799 -0.239 <.0001 0.788 -0.229 <.0001 0.795 -0.206 <.0001 0.814 CH_ROA + 2.871 <.0001 1.141 3.910 <.0001 1.185 2.930 <.0001 1.141 7.084 <.0001 1.259 NOLOVE_D + 0.556 <.0001 1.743 0.577 <.0001 1.781 0.560 <.0001 1.751 0.615 <.0001 1.85 INSIDER_PURCHASES_D + 0.152 <.0001 1.164 0.133 0.0006 1.043 0.140 0.0005 1.045 0.135 0.0103 1.145 CH_REV + 0.328 <.0001 1.882 0.334 <.0001 1.914 0.276 <.0001 1.769 CH_OPEXP − -0.315 <.0001 0.580 -0.315 <.0001 0.577 -0.249 <.0001 0.630 EXANTE_FIN_NEEDS_D + 0.255 <.0001 1.291 0.271 <.0001 1.311 0.157 0.0022 1.170 FUTURE_EQUITY_D + 0.094 0.0003 1.099 0.067 0.0128 1.069 0.060 0.1073 1.061 PE_RANK − -0.754 <.0001 0.790 ANALYST_CNT − -0.027 <.0001 0.861 -0.032 <.0001 0.836 -0.028 <.0001 0.856 -0.049 <.0001 0.758 ANALYST_EXP − 0.004 0.3384 1.011 0.012 0.0068 1.032 0.010 0.0318 1.026 0.014 0.0247 1.036 ANALYST_BUSY + 0.017 <.0001 1.084 0.009 0.0007 1.047 0.015 <.0001 1.069 0.006 0.1379 1.026 FORECAST_STALE + -0.001 0.0312 0.978 -0.001 0.1114 0.984 -0.001 0.0543 0.980 0.002 0.0292 1.031 FORECAST_SD + 6.749 <.0001 1.331 6.590 <.0001 1.318 6.562 <.0001 1.321 6.934 <.0001 1.321 SD_ROA + 5.278 <.0001 1.146 5.059 <.0001 1.136 5.245 <.0001 1.144 8.275 <.0001 1.143 OP_LEV + 0.224 <.0001 1.073 0.193 <.0001 1.063 0.208 <.0001 1.067 0.218 0.0004 1.067 SPEC_ITEMS_D +/− 0.297 <.0001 1.346 0.302 <.0001 1.352 0.323 <.0001 1.382 0.527 <.0001 1.695 INST_OWN − -0.645 <.0001 0.843 -0.619 <.0001 0.847 -0.630 <.0001 0.846 -0.418 <.0001 0.899 MVE − -0.420 <.0001 0.483 -0.369 <.0001 0.530 -0.412 <.0001 0.492 -0.370 <.0001 0.541 N where Y=1 9,295 8,967 8,592 4,338 Total N 85,830 87,745 80,133 60,021 Likelihood Ratio Χ 2 8,151 7,811 7,638 4,318 R-Square 0.091 0.085 0.091 0.069 Max-rescaled R-Square 0.183 0.176 0.184 0.172 Kendall’s Tau-a 0.106 0.100 0.106 0.075 % Concordant / Discordant 77.2 / 22.3 77.1 / 22.3 77.4 / 22/1 77.8 / 21.5
The sample consists of the 98,477 firm-quarter observations from January 1998 through June 2008 with non-missing values for the variables included in our regression analysis. All variables are defined in the Appendix, and continuous variables are winsorized at the 1st and 99th percentiles by year to reduce the effect of outliers. The odds ratio column is a measure of the magnitude of each independent variable’s effect on the likelihood of Y = 1 when continuous (binary) independent variables change by one standard deviation (change from a value of zero to one). Industry, year, and fiscal quarter indicator variables are included in each regression; coefficients are not reported for brevity.
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TABLE 5 OLS Regression Results
(1) (2) (3) (4) Y = CH_ANALYST_CNT Y = CH_INST_OWN Y = CH_VOLUME_LONG Y = CH_VOLUME_SHORT Pred. Variable Sign Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Intercept 1.150 17.88*** 0.237 48.59*** 2.501 45.64*** 0.0037 27.40*** PEES_D + 0.259 5.05*** 0.014 3.57*** 0.388 8.90*** 0.0008 7.36*** PY_RET + 1.048 43.24*** 0.010 5.32*** 0.008 0.37 0.0012 22.37*** CH_ROA + -0.175 -0.50 0.016 0.60 1.430 4.69*** 0.0047 6.24*** MVE – -0.038 -4.02*** -0.026 -36.36*** -0.138 -17.30*** 0.0004 18.83*** AGE – -0.130 -6.95*** -0.007 -4.74*** -0.057 -3.50*** -0.0010 -25.77*** N 55,772 55,772 68,269 68,069 2 Adj. R 0.036 0.034 0.009 0.021
The sample consists of the 98,477 firm-quarter observations from January 1998 through June 2008 with non-missing values for the variables included in our regression analysis. All variables are defined in the Appendix, and continuous variables are winsorized at the 1st and 99th percentiles by year to reduce the effect of outliers. The sample included in Columns 1, 2, and 3 of this table ends in June 2005 because the regression specification presented requires 12 future quarters of data.
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