Is Guidance a Macro Factor? The Nature and Information Content of Aggregate Earnings Guidance*

Carol Anilowski University of Michigan Business School

Mei Feng

Katz School of Business, University of Pittsburgh

Douglas J. Skinner University of Michigan Business School

March 2004, revised October 2004

Abstract Although there is a great deal of research that documents the information content of management earnings forecasts at the firm level, there is almost no research on the informativeness of aggregate guidance. We argue that aggregate earnings guidance is informative at the market/economy level through its effects on expectations about market-level expected future cash flows and expected returns. Consistent with aggregate guidance capturing information about economy-wide cash flows, we find that aggregate guidance, especially relative levels of quarterly downward guidance, is associated with analyst- and time-series-based measures of aggregate earnings news. We also find that guidance affects market returns in those months each quarter when the most guidance is released, and that relative levels of downward guidance are especially informative. Overall, the evidence supports our contention that guidance affects market returns by aggregating news about market-level cash flow shocks and through its effects on market uncertainty.

____________________________________________________________________ * We received useful comments from workshop participants at Carnegie Mellon University, The University of Chicago, and the University of Exeter (Xfi Conference) as well as from Ray Ball, Phil Berger, Alan Gregory, and Greg Miller. Skinner’s work on this project was supported by the Neubauer Faculty Fellows program at the University of Chicago Graduate School of Business during the 2003-2004 academic year. We also appreciate the financial support of the University of Michigan Business School.

1. Introduction

Earnings guidance is now pervasive, with thousands of management earnings forecasts

released every year by close to two thousand firms. As a result, stock market commentators

sometimes make statements about the implications of trends in aggregate earnings guidance for

market returns. For example, financial press discussions of market-moving economic and

financial news sometimes feature statistics on the relative number of negative earnings

preannouncements (“earnings warnings”) in a quarter. The implication is that earnings guidance

is informative with respect to overall earnings trends in the economy, and perhaps even for

trends in macroeconomic variables such as corporate investment and GDP growth, and so affects

market returns.1

Our principal research question is whether aggregate earnings guidance provides

information to the market. Whether aggregate guidance affects market returns depends on the

implications of guidance for market-wide expected future cash flows and expected returns. With

respect to cash flows, the informativeness of guidance depends on the extent to which earnings

guidance, when aggregated across firms, is pervasive, representative, and timely, as well as on

interactions among these factors. Because managers have traditionally issued guidance when

their firms are performing unusually well or unusually poorly, it is not obvious that aggregate

guidance, even if pervasive, will be informative about economy-wide earnings; guidance may

not be representative. On the other hand, guidance may be informative even if it is issued by a

1 See, for example, “Storm Warnings Replace Profit Dreams,” Wall Street Journal, Abreast of the Market, September 23, 2002, C1; “Stocks Face Test as Quarter Ends,” Wall Street Journal, Abreast of the Market, June 30, 2003, C1; “Wall Street Expects Rosy Earnings – Figures Might Show Strongest Profits Growth Since 2000 and Further..,” Financial Times, p. 27, October 13, 2003. Many of these articles cite Chuck Hill, until recently director of research at Thomson First Call. Thomson First Call produces a weekly earnings report (“This Week in Earnings”) which reports, among other statistics, summary data on the ratio of negative to positive preannouncements. The report for 23 January 2004 indicated that “The 1Q04 pre-announcements are now up to meaningful levels with 198 total to date. The ratio of negative to positive pre-announcements is at a very low 1.4, well below the 1.9 at the same date for 1Q03, and even further below the 2.3 average over the last nine years.”

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relatively small number of firms if these are firms, such as GE and Intel, whose earnings are very

representative of overall earnings trends.

Evidence suggests that variation in firm-level stock returns is driven primarily by cash

flow shocks, while variation in market-level returns is driven primarily by shocks to expected

returns. The argument is that firms’ earnings news is largely idiosyncratic and diversifies away

upon aggregation, while expected returns are driven by macro factors that are not diversifiable

(Campbell, 1991; Vuolteenaho, 2002). We see two ways in which aggregate guidance might

affect market-level expected returns. First, guidance can affect levels of market uncertainty. For

example, if the arrival of earnings news increases market uncertainty, the associated increase in

return volatility will increase expected returns, reducing stock prices. This tendency might be

worse for bad news if earnings warnings have a “contagion” effect, so that warnings from a

small number of firms have a disproportionately large effect on market uncertainty.2 Note that

this result may hold in spite of the fact that, at the firm level, one of the principal reasons for

issuing earnings guidance is to avoid surprising investors, especially on the down side (e.g.,

Graham et al., 2004; Skinner, 1994). Second, the negative relation between economic conditions

and expected returns (Fama and French, 1989) means that good news about aggregate earnings

may be bad news for stocks because it lowers future expected returns, and vice versa.

Alternatively, aggregate cash flow shocks may be linked directly to changes in expected returns,

perhaps because they affect expectations about macroeconomic variables such as inflation or

corporate investment, which in turn affect interest rates and expected returns (e.g., Boyd et al.,

2005; Kothari et al., 2003).

2 Anecdotally, a spate of earnings warnings from technology companies apparently had this effect early in the summer of 2004; see “Investor Jitters Greet Earnings Season,” Financial Times, July 12, 2004.

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There is little research on the relation between firm-level earnings news and market-wide

returns. Penman (1987) finds systematic variation in aggregate earnings news during calendar

years and links this to variation in aggregate returns. Kothari, Lewellen and Warner (2003)

investigate a number of properties of aggregate earnings news and its relation to market returns

and find, among other things, that market returns are negatively related to aggregate earnings

growth. Our study is also similar in spirit to Seyhun (1988), who investigates the information

content of aggregate insider trading.

We use an extensive database of management-issued earnings guidance from Thomson

First Call to investigate our predictions. First, we find that there has been a substantial increase

in guidance over the 1994 to 2002 sample period. Consistent with previous research, we find

that the majority of earnings guidance is quarterly rather than annual (a 60/40 ratio), and that

over half (56%) of all quarterly guidance is downward guidance, with the remainder equally

divided between upward and neutral guidance. We confirm previous evidence that quarterly

guidance, especially downward quarterly guidance, is more informative than other types of

guidance. All of this is consistent with the view that the relative extent of quarterly downward

guidance (also known as earnings warnings or preannouncements) is potentially informative

about market level returns.

Our evidence indicates that guidance is increasingly pervasive and representative. In

addition to a consistent increase in the proportion of firms issuing guidance, from around 10% in

the mid 1990s to around 30% in 2001 and 2002, we find that firms issuing guidance now

represent over one-half of the total market capitalization of Compustat and nearly one-half of

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Compustat total assets, up from 10-20% in the mid-1990s.3 We also find a shift over time in the

variables that explain which firms issue guidance. In the 1990s, measures of firm performance

such as ROA, losses, and earnings surprises were most important in explaining when managers

issued guidance, consistent with previous research. In recent years, however, firm characteristics

such as size, book-to-market, and growth are relatively more important. This suggests that

firms’ guidance policies are now more consistent than in the past, and that guidance is now more

representative, in the sense that it is now less likely to be driven by unusual/surprising earnings

performance in a particular period.

There are clear patterns in the timing of guidance. In relative terms, managers tend to

issue neutral guidance early in the quarter, downward guidance toward the end of the quarter,

and upward guidance after the end of the quarter. In addition, the information content of

guidance increases during the quarter. This evidence supports conventional wisdom that, in each

quarter, it is the relative extent of downward guidance available the end of the fiscal quarter that

is informative about overall earnings trends.

We also find that aggregate quarterly guidance measures, and especially the relative

extent of downward guidance (as measured by the ratio of downward to upward guidance), vary

a good deal from one quarter to the next, and vary more than corresponding aggregate annual

guidance measures. This reinforces the idea that variation in aggregate downward guidance has

the potential to capture variation in overall earnings news.

Finally, we assess whether aggregate measures of quarterly earnings guidance capture

overall earnings news and drive market returns. Using quarterly data, we find that the guidance

measures, and especially the relative extent of downward guidance, are strongly associated with

3 These trends may be overstated if the completeness of First Call’s coverage of earnings guidance has improved over time. Nevertheless, it is clear that the value-weighted proportion of firms issuing guidance has increased over time to levels that are now very substantial.

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other measures of aggregate earnings news such as analyst forecast-based surprise measures and

more conventional time series-based measures of earnings news. These results hold for both

overall guidance and for S&P 500 guidance. The results are significant given that the guidance

measures are available earlier in the quarter than the other measures, which require earnings

realizations. There is no evidence of a relation between these guidance measures and quarterly

stock market returns.

Using monthly data, however, we find evidence that aggregate guidance is related to

market returns. In particular, we find that the relative extent of downward guidance in the last

month of each quarter is negatively related to contemporaneous market returns. There is a

similar, but weaker, result for neutral guidance in this month, suggesting that neutral guidance

sometimes conveys bad news. These results are stronger when we look at the most recent time

period (1998-2002), as expected given the increases in pervasiveness and representativeness that

occur over our sample period. In addition, when we divide quarters into “good news” and “bad

news” quarters, we find different patterns of intra-quarter market returns. While positive market

returns in good news quarters tend to accrue smoothly and steadily over the entire period,

negative market returns in bad news quarters are concentrated in the last 2-3 weeks of the fiscal

period, so that returns are essentially flat in the announcement month. These patterns appear to

reflect systematic differences in the way that guidance is released during the quarter: bad news

quarters are characterized by sharp increases in the relative extent of downward guidance in the

last 2-3 weeks of the fiscal period; no such patterns exist in good news quarters. We also find

that bad news quarters are characterized by higher levels of market volatility, consistent with

greater market uncertainty in these quarters. Finally, we find that earnings guidance issued by

the largest firms (“bellwethers”) is associated with market returns in short windows around its

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release. This is evidence that firm-level earnings guidance is informative at the market level, at

least for large and influential firms.

Section 2 discusses our predictions in more detail. Section 3 describes sample selection

and provides descriptive information. Section 4 reports evidence on the informativeness of

guidance at the firm level while Section 5 reports on the pervasiveness and representativeness of

aggregate guidance. Section 6 presents evidence on the intra-quarter timing of aggregate

guidance. Section 7 presents evidence on the relation between aggregate guidance, aggregate

earnings news, and market returns. Section 8 concludes and discusses implications.

2. Development of Empirical Predictions

Our principal research question is whether earnings guidance is informative for returns at the

market level. We motivate this question with the observation that there are three sources of

variation in stock returns (e.g., Fama, 1990): (1) shocks to expected cash flows, (2) predictable

variation in expected returns, (3) shocks to expected returns. We discuss how earnings guidance

is likely to affect market returns though its effects on cash flows in Section 2.1 and expected

returns in Section 2.2.

2.1 Earnings guidance and expected future cash flows

To the extent that earnings guidance provides information about firms’ expected future

cash flows, it seems likely that aggregating guidance would provide information about the

expected future cash flows of firms in general, and thus for the market as a whole. This is

similar to the intuition behind papers that investigate “intra-industry information transfer,” the

idea that earnings news for one firm in an industry will affect the returns of other firms in the

same industry (e.g., see Clinch and Sinclair, 1987; Pownall and Waymire, 1989; Lang and

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Lundholm, 1996). We extend this argument to the economy level – to the extent that

macroeconomic factors affect firm-level cash flows, we expect that the earnings news of firms

that provide earnings guidance will have implications for the cash flows of firms in general.

This argument implies that firm-level earnings news is informative about market level macro

variables such as real GDP, industrial production, and investment, an argument often made in the

financial press.

The more firms that issue earnings guidance, the more likely it is to have implications for

the market as a whole. If all firms in the economy issue guidance, aggregating that guidance

should provide us with a complete picture of economy-wide earnings. Thus, we expect that the

extent to which guidance is informative for the market as a whole will increase with the

pervasiveness of the guidance.

We measure the pervasiveness of guidance using both equal-weighted and value-

weighted measures. Recent evidence shows that the cross-sectional distribution of earnings has

become more and more concentrated – an increasingly small number of firms is responsible for

an increasing fraction of total earnings – implying that value-weighting will be important.

DeAngelo, DeAngelo and Skinner (2004) show that the 25 firms paying the largest dividends in

2000 account for over one half of aggregate Compustat earnings. This means that even if

guidance is issued by a relatively small number of firms, their size may make the guidance

informative for market-wide earnings.4

4 General Electric’s (GE) size and diversification across both industries and geographical regions makes its earnings news informative about overall economic trends. For example, see “GE buoys market with growth optimism” (Financial Times, July 9, 2004): “General Electric…a closely watched economic bellwether, on Friday forecast a strengthening global economy as it reported better-than-expected second-quarter earnings. Its optimism lifted financial markets’ spirits…The US conglomerate sounded an upbeat tone for key economic and business indicators...(emph. added).”

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Whether earnings guidance is a useful indicator of market-wide earnings also depends on

the representativeness of firms that provide guidance. If firms that provide guidance do so for

particular economic reasons or otherwise have unusual characteristics, their results may not be

representative, making it less obvious that we can draw implications from their results for the

economy as a whole. Previous evidence shows that firms that issue annual earnings guidance

tend to be larger and more profitable than other firms (e.g., Patell, 1976; Lev and Penman, 1990)

while firms that provide guidance often do so when they have adverse earnings news (e.g.,

Skinner, 1994; Kasznik and Lev, 1995). Thus, we investigate the characteristics and

circumstances of firms that provide earnings guidance.

Earnings guidance need not be pervasive if it is representative. That is, it may be that

there are a small number of firms whose results are very representative of those for the market as

a whole and which therefore have a disproportionate effect on market-wide earnings

expectations.5 For example, the press sometimes features discussions of the earnings guidance

of firms such as Intel or Dell, which are seen as “bellwethers” for the entire tech sector, if not for

the market as a whole. Our empirical tests attempt to isolate some of these firms, to see whether

their guidance is, in fact, informative at the market level.

The informativeness of earnings guidance for market-wide returns also depends on the

timeliness of the guidance – the earlier in the quarter guidance becomes available, the more

likely it is that it will drive market-wide earnings expectations. However, it may be that there is

a relation between the timing of the news and its representativeness. For example, if adverse

earnings news tends to be disclosed on a more timely basis than good news,6 the market will

5 In the extreme, one could imagine a single firm whose earnings represented a “sufficient statistic” for the earnings of the economy, in the same way that the voting results of a single small town are sometimes seen as highly predictive of national election results. 6 See, for example, Skinner (1994, 1997), Soffer et al. (2000), Graham et al. (2004).

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observe systematically more adverse earnings news early in the quarter and will condition its

response accordingly, and only respond when guidance is more or less negative than expected.7

Thus, we also provide evidence on the timeliness of earnings guidance and on whether and how

the timing of disclosure varies according to the type of the guidance that firms provide. The

timing of guidance may also affect market uncertainty, as discussed next.

2.2 Earnings guidance and expected returns

Recent evidence indicates that news about expected cash flows is relatively more

important in explaining firm-level returns than it is in explaining market-level returns

(Vuolteenaho, 2002). In fact, the evidence suggests that information about expected returns

explains most of the variation in market returns (e.g., Campbell, 1991) while cash flow news

explains most of the variation in firm-level returns. These papers argue that because firm-level

earnings news is largely idiosyncratic, its effects will be diversified away when aggregated. In

contrast, variation in expected returns is predominantly driven by systematic macro factors,

which are not diversifiable. The implication is that earnings guidance will not be all that

informative about market-level expected future cash flows, but may be informative with respect

to expected returns.

There are at least two ways in which earnings guidance potentially affects expected

market returns. First, we might expect a relation between the aggregate news conveyed by

earnings guidance and expected returns at the market level. For example, it is sometimes argued

that good cash flow news suggests strengthening aggregate demand which increases inflationary

pressures in the macroeconomy, which in turn leads to an increase in discount rates and to lower

7Consistent with this, market commentators sometimes compute the ratio of negative to positive guidance for a quarter and discuss the fact that this ratio typically exceeds one. See, e.g., “Investor Jitters Greet Earnings Season,” Financial Times, July 12, 2004: “John Butter, research analyst at Thompson, said the ratio of 1.5 negative second quarter pre-announcements to every positive one…was better than the typical ratio of 2.2”.

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stock prices (the reverse would also follow). Fama and French (1989) provide evidence that

expected returns are lower when economic conditions are strong and higher when economic

conditions are weak. This argument implies a negative relation between economy-wide cash

flow news and market-level stock returns.8

Second, earnings guidance potentially affects the level of market uncertainty, and hence

market volatility and expected returns. This argument is based on the idea that increased market

uncertainty increases the volatility of market returns, that volatility shocks are persistent, and that

this, in turn, increases expected returns and drives down stock returns (e.g., French, Schwert and

Stambaugh, 1987; Schwert, 1989).

This argument can help explain the asymmetric reaction to earnings news, as in Skinner

and Sloan (2002) and Conrad, Cornell and Landsman (2002). Campbell and Hentschel (1992)

discuss the “volatility feedback effect,” under which an information shock increases volatility by

increasing uncertainty. If the news is good, it will naturally increase stock prices, but this effect

will be offset by an accompanying increase in uncertainty, volatility and expected returns. If the

news is bad, on the other hand, stock prices will naturally decline, and this effect will be

reinforced by the accompanying increase in uncertainty, volatility and expected returns. Thus,

volatility feedback will cause an asymmetric response to earnings news.9 This effect will be

amplified if there is something inherently different about the nature of good and bad news such

that bad news naturally creates more uncertainty than good news. For example, there may be a

“contagion” effect for earnings warnings: if investors see an unusually large number of earnings

warnings in a quarter, it is likely to increase their uncertainty about earnings news overall,

8 Kothari et al. (2003) use this type of argument to explain their finding that aggregate quarterly earnings news is negatively related to market returns. 9 The argument is related to that of Veronesi (1999), who predicts that the market overreacts to bad news in good times (when the unexpected nature of the news increases uncertainty) and underreacts to good news in bad times (for the same reason).

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driving up expected returns at the market level and compounding the negative effect of the news

itself.

An alternative argument is that guidance reduces levels of market uncertainty. By

reducing the surprise element of earnings announcements and/or by helping to explain the news

in advance, guidance potentially reduces the extent to which earnings news creates uncertainty,

reducing volatility feedback.10 Whether these “second moment” effects of guidance are

relatively more important for market returns than for the returns of individual firms, where mean

effects dominate, is an empirical question on which we hope to shed some light.

3. Sample Selection and Descriptive Statistics on Forecast Pervasiveness

Table 1 summarizes sample construction. We begin with all company issued guidance

available on First Call from 1990-2003, a total of 48,239 firm/period observations. We include

forecasts of both annual and quarterly EPS and drop all forecasts from 1990-1993 because

coverage in those years is sporadic. We remove observations that fail to satisfy various selection

criteria, most notably forecasts that do not provide information about EPS, which reduces the

sample to 45,516 observations. We next remove observations that do not have accompanying

analysts’ consensus earnings estimates from First Call because we need to measure the earnings

surprise (loss of 9,890 observations), those for which there is no earnings announcement date

available from Compustat (loss of 4,201 observations), and those for which the management

forecast is made outside of a reasonable window relative to the fiscal period being forecast (loss

of 253 observations).11 The final sample contains 31,172 management earnings forecasts.

10 The argument is similar to that for why managers carefully manage their firms’ earnings expectations, as discussed in Skinner (1994), Soffer et al. (2000), and Matsumoto (2002). 11 On the last point, we require that a quarterly management forecast is made before the earnings announcement date of the fiscal quarter being forecast and no more than 90 days before the end of that fiscal quarter. For annual forecasts we require that the forecast is made before the earnings announcement date of the fiscal year being forecast

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The advantage of First Call is that the sample is larger and covers many more firms than

would be possible if the management forecasts were collected using databases that search

newspapers and wire services, the approach typically used in the accounting literature. Having a

comprehensive dataset is important for assessing the aggregate effects of disclosure. The

disadvantage is that we have to rely on First Call, whose sampling criteria may be different from

what we might want. For example, they may prefer to focus on larger firms of more interest to

their clients.

Table 2, Panel A reports the number of forecasts in each year, in total and categorized by

reporting period (annual versus quarterly), along with the number of firms releasing forecasts in

each year. As is well known (e.g., Pownall et al., 1993) a majority of forecasts are shorter

horizon quarterly forecasts: 59% of forecasts are of quarterly earnings while 41% are of annual

earnings. The number of firms with forecasts on First Call increases through time, implying

increasing coverage. The number of firms increases fairly significantly from 1994 to 1998, is

fairly stable between 1998 and 2000, increases in 2001, and declines in 2002. This implies that,

at a minimum, coverage should be fairly complete from 1998 to 2002 and consequently we

restrict some analyses to data drawn from this subperiod. There is a strong overall increase in

the number of forecasts issued over time, which is unlikely to be fully explained by changes in

coverage by First Call. This suggests that the propensity of firms to issue earnings guidance has

increased over our sample period, as we might expect given other evidence that firms are

and no more than 730 days (two years) before the end of that fiscal year. This means that annual forecasts can be made no earlier than the beginning of the fiscal year that precedes that being forecast. With regard to the corresponding analysts’ forecasts, we require that a consensus analysts earnings forecast is available at least 5 and no more than 90 days before the corresponding quarterly management forecast, and at most 120 days before the fiscal quarter end. For annual forecasts, we require that: (1) if the management forecast is released in the same fiscal year, the consensus analyst forecast be available at least 5 and no more than 120 days before the management earnings forecasts, or (2) if the management forecast is released in the fiscal year prior to that being forecast, there is no restriction. With regard to the Compustat earnings announcement date requirement, we also drop a small number of observations for which the earnings announcement date is clearly in error given the fiscal period for which the forecast is being made.

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increasing their levels of disclosure (e.g., through conference calls; Frankel et al., 1999; Bushee

et al., 2003).

Table 2, Panel B reports on the number of forecasts issued by firms in each year.

Consistent with an increasing propensity to forecast, the average number of forecasts per

firm/year increases steadily through time, from 1.18 in 1994 to 3.95 in 2002, while the median

increases from 1 in 1994-1997 to 2 in 1998-2000 and then to 3 in 2001 and 2002. This trend

holds for both annual and quarterly forecasts (not reported in tables).

Table 2, Panel C reports on the type of earnings forecasts made by managers, overall and

by forecast period. As is conventional in the disclosure literature, we divide the forecasts into

point forecasts, range forecasts, minimum forecasts, maximum forecasts and qualitative forecasts

(e.g., Pownall et al., 1993).12 Most of the sample forecasts are point (27%) or range forecasts

(51%), with about 10% of the sample being minimum or maximum forecasts and 11% being

other, qualitative forecasts. This suggests that First Call’s collection efforts focus on more

quantitative guidance and excludes some qualitative guidance.

Managers sometimes release earnings forecasts at the same time as earnings

announcements, consistent with previous research (Waymire, 1984; Hoskin, Hughes and Ricks,

1986; Hutton et al., 2003). Panel D of Table 2 reports that nearly one-quarter (23.8%) of

quarterly forecasts are made at earnings announcement dates and that about 11% of annual

forecasts are made at earnings announcement dates. There is a strong increase in this tendency

during the sample period. The fraction of quarterly forecasts released at the previous quarter’s

earnings announcement date increases from around 5% in 1996 and 1997 to 16% in 2000, 34%

in 2001, and 42% in 2002. Because forecasts released at earnings announcement dates may be

12 The appendix describes how this is done for the First Call data in our sample.

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systematically different from other forecasts, we exclude them from many of our tests, although

our results are generally robust to whether or not we retain these observations.

4. The Informativeness of Firm-Level Earnings Guidance

Table 3 reports on the nature of the earnings news conveyed by earnings guidance in each

year of the sample period. We divide forecasts into those that convey downward, upward and

neutral guidance by either (in the case of quantitative forecasts) comparing the management

forecast to the most recent median analysts’ forecast available from First Call or (in the case of

qualitative forecasts) using information provided about the forecast by First Call.13

Panel A of Table 3 reports on forecasts of annual EPS. Overall, forecasts of annual

earnings are slightly more likely to convey downward guidance than upward guidance: 40% of

annual forecasts convey downward guidance, 36% convey upward guidance, and 24% convey

neutral guidance. There is some variation across sample years in the relative frequency of the

three types of guidance, although the only clearly discernible trend is that neutral guidance is less

frequent since 2000 while upward guidance increases. Panel B reports on forecasts of quarterly

EPS. There is a strong tendency for quarterly forecasts to convey downward guidance: 56% of

quarterly forecasts convey downward guidance while 22% convey neutral guidance and another

22% convey upward guidance. This confirms the idea that an important motivation for quarterly

earnings guidance is to manage expectations downwards in the presence of adverse earnings

news (e.g., Skinner, 1994).

To get a clearer picture of variation in the relative extent of upward and downward

guidance, Figure 1 plots the ratio of downward to upward earnings guidance for each quarter of

13 First Call provides a very detailed coding scheme (their “CIG code” variable) which facilitates this process. See the appendix for details of how we classify the forecasts into upward, downward and neutral guidance.

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the sample period. The ratio is computed separately for annual and quarterly guidance. To

compute this ratio, we divide the number of forecasts that convey downward guidance by the

number that convey upward guidance in each calendar quarter of the sample period (Q1 1994 to

Q4 2002).14

Figure 1 shows that there is a good deal of quarter-to-quarter variation in the relative

extent of downward and upward guidance, especially for quarterly forecasts, making it more

likely that variation in this measure captures aggregate earnings news. The ratio of downward to

upward guidance formed using annual forecasts (the “annual guidance ratio”) typically varies

between 1 and 2, with a mean of 1.4 and a standard deviation of .6. In contrast, the quarterly

guidance ratio has a mean of 3.2, a standard deviation of 1.3 and never falls below 1.2, and so is

both more variable and substantially larger than the annual guidance ratio, consistent with

previous evidence that quarterly forecasts are more likely to convey bad news and conventional

wisdom that the average ratio exceeds one.15

Table 4 reports the stock price reaction to management earnings forecasts. To conduct

these tests we compute market-adjusted returns for the three trading days centered on the date of

the management earnings forecast (Brown and Warner, 1985). The sample size for these tests

declines to 29,611 observations because we lose observations with no ready CRSP match or with

missing returns.16

14 One complication with this analysis arises in matching forecasts to calendar quarters – to do this we include all forecasts that pertain to the calendar quarter; that is, that are made in the four subperiods shown in Figure 3. Thus, we remove forecasts for firms that do not have (end of) March, June, September or December fiscal year ends. We also exclude forecasts made in subperiod 1, which mainly contains forecasts issued in conjunction with the previous quarter’s earnings announcement. This reduces the sample size to 13,843 quarterly forecasts and 10,410 annual forecasts. 15 These ratios also have different time series properties. While the quarterly ratio appears to be non-stationary the annual ratio displays little autocorrelation in the levels but is negatively autocorrelated in the changes. 16 The table reports results that use CRSP equal-weighted index returns as the market return. Results are similar if we use the CRSP value-weighted index instead. We do not report statistical significance tests. Given the sample

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The results in Table 4 largely accord with previous research (e.g., Baginski et al., 1993;

Skinner, 1994; Hutton et al., 2003). For the overall sample, the mean (median) return is -3.5% (-

1.5%) and 58% of the returns are negative. The central tendency is negative because most

forecasts (58%) are of quarterly earnings, which are more likely to convey bad news, and

because bad news forecasts are more informative. 63% of the returns associated with quarterly

forecasts are negative, and the mean (median) overall return for quarterly earnings guidance is -

4.9% (-2.6%). Forecasts of annual earnings are less informative, with a mean (median) return of

-1.60% (-0.42%).

The other results in Table 4 confirm that returns associated with downward and upward

guidance are reliably negative and positive, respectively, a tendency that is again stronger for

quarterly guidance than for annual guidance. The information content of both good and bad

news forecasts is larger for quarterly forecasts than it is for the corresponding annual forecasts,

with means (medians) of 4.2% (2.8%) and -10.1% (-7.1%) for quarterly forecasts of good and

bad news, respectively, compared to corresponding means (medians) of 2.5% (1.7%) and -6.2%

(-3.3%) for annual forecasts. Returns associated with the neutral forecasts are close to zero.

5. The Pervasiveness and Representativeness of Earnings Guidance

The evidence in Table 2 shows that the frequency of earnings guidance has increased

over time. In this section, we investigate: (1) whether this translates into an increase in the

pervasiveness of guidance, and (2) the representativeness of earnings guidance.

Table 5 and Figure 2a provide evidence on the number and proportion of firms in each

year that provide either annual guidance or quarterly guidance. In each year, we define

size most of the means and medians we report are likely to be reliably different from zero, in spite of some likely cross-sectional dependence.

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forecasting firms as those that issue some type of guidance and that have certain basic data on

annual Compustat, and the total number of firms as all firms on Compustat with the same

available data.17 The proportion of firms issuing guidance increases noticeably during the

sample period, although the numbers in the earlier years may be understated if First Call’s data

collection is less complete in these years. The table shows that the proportion of firms issuing

earnings guidance increases from around 5-10% in the mid 1990s to around 30% in 2001 and

2002, the last year with available data.

Earnings guidance is more pervasive when we weight the observations by firm size. We

use both total assets and market value to weight the observations because weighting by market

value alone may distort the trends given the large increase in stock prices that occurred during

the 1990s and the large subsequent reversal.18 When we weight by total assets the proportion of

forecasters is larger than the simple proportions, as expected given the well-known tendency that

larger firms are more likely to provide guidance. The total asset-weighted proportion of

forecasters increases from 13% in 1995 to 17% in 1996, 20% in 1997, and then to 43% in 1998,

before falling to 33% in 1999 and 31% in 2000, and increasing again to 46% in 2001 and 47% in

2002. Thus, in recent years firms that represent nearly one-half of total assets on Compustat

provide earnings guidance. The numbers are even more impressive when we value-weight using

market capitalization, suggesting that forecasting is associated with relatively larger valuations.

The value-weighted fraction increases from 17% in 1995 to 22% in 1996, 26% in 1997 and then

to 44% in 1999, 42% in 2000 and 47% in 2001, before increasing sharply to 57% in 2001 and

56% in 2002. Thus, in 2001 and 2002 firms representing over one-half of the market

17We require book value and prior year sales to be greater than or equal to zero, and stock price, shares outstanding, total assets and prior year total assets to be positive. For the logit regressions we also remove those observations with book-to-market, ROA or sales growth in the top and bottom 1% of the distribution. 18 We have also used earnings and positive earnings to weight the observations, with very similar results to those shown for total assets.

17

capitalization of Compustat provide earnings guidance. This is evidence that earnings guidance

has become more pervasive, especially in value-weighted terms.

To assess the representativeness of earnings guidance, Table 5 also reports the results of

annual logit regressions of whether a firm provides guidance during the year on firm

characteristics often associated with forecasting: firm size (log of total assets), book-to-market,

ROA, sales growth, and a loss dummy. Consistent with previous research, we find that size is

important – the coefficient on size is strongly positive in all years. In addition, there is evidence

of an increasing trend in the magnitude of the coefficient on size, from .22 in 1996 to .36 in

2002. We also find consistent evidence that book-to-market is significantly negatively

associated with forecasting, although there is less of a trend in its coefficient. The ROA variable

is reliably and consistently positive in the 1995-1999 period, consistent with the idea that firms

with good earnings performance are more likely to forecast earnings (Patell, 1976; Penman,

1980). However, the ROA variable is not significant in 2000 or 2001 and has a relatively small

coefficient in 2002. The loss dummy is positive and significant from 1995-1999 but not in later

years. Thus, there is some evidence that accounting performance, as measured by ROA and

reported losses, was important in explaining forecasting during the 1990s but that this is less true

over 2000-2002 when size becomes relatively more important. Sales growth is negatively

associated with forecasting, but mainly in the latter part of the sample period. Overall, it seems

that firm characteristics such as size, book-to-market, and growth have become more important

than period-specific earnings performance in explaining forecasting. This implies that forecasts

have become more representative over the sample period, in the sense that they are less likely to

be made by firms when their earnings performance is unusually good or bad. These trends are

also reflected in a fairly substantial increase in the explanatory power of the regression over the

18

sample period. The pseudo R-squared increases steadily from around 5% in the mid 1990s to

17% in 2002.

Table 6 and Figure 2b provide further evidence on representativeness, this time on a

quarterly basis for quarterly guidance. Because previous research suggests that quarterly

forecasting is likely to reflect earnings surprises, we also include the “Asurp” variable in these

regressions, which is the difference between realized quarterly earnings and the analyst

consensus at the beginning of the quarter. There is a strong tendency for firms with adverse

earnings news to provide quarterly guidance (e.g., Skinner, 1994). In fact, Asurp is the only

consistently significant variable over the quarters from 1995 until early 2001, after which it

largely fails to achieve significance. Firm size and book-to-market are generally not significant

during the 1990s, but are significant beginning in early 2000. Consistent with the evidence in

Table 5, this evidence suggests that firm characteristics, rather than period specific earnings

news, are now relatively more important in explaining guidance. The increasing importance of

size and book-to-market also helps explain the associated increase in the pervasiveness of

guidance.

6. The Timeliness of Earnings Guidance

The results to this point suggest that guidance has become increasingly pervasive and

representative over the sample period. We next investigate the timeliness of quarterly earnings

guidance.

Table 7 reports the within-quarter timing of quarterly earnings forecasts. We divide

forecasts into those that provide upward, downward, and neutral guidance to see whether

managers’ forecast strategies vary as a function of the nature of the earnings news, as previously

19

suggested in the accounting literature (e.g., Francis et al., 1994; Skinner, 1994, 1997; Soffer et al.

2000). To assess timing, we classify forecasts of quarterly earnings into four subperiods, as

shown in Figure 3: (1) the period from the end of the previous fiscal quarter to 50 calendar days

before the end of the fiscal quarter; (2) the period from 50 days before the end of the fiscal

quarter to 25 days before the end of that quarter; (3) the period from 25 days before the end of

the fiscal quarter to the end of that quarter; and (4) the period from the end of the fiscal quarter to

the earnings announcement date. We plot the results in Figure 4.

Table 7, Panel A shows that well over half (65%) of the forecasts occur either late in the

quarter (subperiod 3) or after the end of the quarter (subperiod 4), thus qualifying as earnings

“pre-announcements” or “warnings.” We also find that many forecasts (25%) fall in the first

subperiod, but that most of these are made in conjunction with the previous quarter’s earnings

release. After we remove these observations (Panel B), only about 5% of forecasts are made in

the first subperiod, while 11% are made in the middle of the quarter (subperiod 2), and 38% are

made in the last 25 days of the quarter (subperiod 3). Most forecasts (46%) are made after the

end of the quarter, as we might expect if the rate of disclosure increases with the precision of

managers’ earnings information.

The intra-quarter timing of forecasts varies with the sign of the earnings news. Relatively

more neutral news tends to be released at the beginning of the quarter, perhaps because managers

have less information about earnings at that time: about one third of all guidance is neutral in this

period. In the last part of the quarter (subperiod 3), however, more of the news is negative (63%)

and relatively less is positive (16%) or neutral (20%). This tendency reverses after the end of the

quarter when the amount of good news increases to about one-quarter (26%) while the amount of

bad news decreases to a little over one-half (54%) and confirming news stays at 21%. Over half

20

(56%) of the good news that is released is released after the end of the quarter, perhaps because

managers want to be very sure of themselves before releasing good news. The tendency of

downward guidance to cluster in the weeks before the end of the quarter suggests that investors

should know by the end of the quarter whether the quarter generally is going to be characterized

by bad quarterly earnings news.

Panel C of Table 7 reports that the information content of quarterly forecasts tends to

increase as the quarter progresses, although bad news forecasts are again more informative in

general. For forecasts made early in the quarter (first subperiod), the mean (median) return is -

9.4% (-5.1%) for bad news forecasts and 2.3% (.9%) for good news forecasts.19 Returns

associated with bad news forecasts then increase in magnitude to -10.3% (-7.6%) in the second

subperiod, -11.9% (-9.2%) in the third subperiod, and to -12.7% (-9.8%) in the last subperiod.

The magnitude of returns associated with good news forecasts also increases across the

subperiods, with means (medians) of 3.1% (1.9%) in the second subperiod, 4.3% (3.0%) in the

third subperiod, and 5.0% (3.4%) in the last subperiod.

The results in Table 7 indicate that most of the information conveyed by bad news

forecasts, in aggregate, has been conveyed by the end of the fiscal quarter, and that the

information content of these forecasts is weighted more heavily toward the end of the quarter.

This supports conventional wisdom that the relative number of earnings warnings observed by

the end of each quarter is informative with respect to the earnings news that will be announced in

the weeks to follow.

19 As in Panel B, these results exclude forecasts made in conjunction with earnings announcements. When these observations are included, the mean and median returns become more positive in all categories, indicating that returns at earnings announcement dates tend to be positive.

21

7. The Relation Between Aggregate Earnings Guidance, Aggregate Earnings News, and

Market Returns.

In the foregoing sections we report a number of regularities related to the pervasiveness,

representativeness, and timeliness of earnings guidance. This section reports evidence on

whether aggregate measures of guidance are related to measures of aggregate earnings news and

to market returns. We conduct these analyses using both quarterly data (section 7.1) and

monthly data (section 7.2). We also provide a comparison of intra-quarter return and guidance

patterns for good and bad news quarters (Section 7.3) and an analysis of short-window market

returns around the time that large firms (market “bellwethers”) release earnings guidance

(Section 7.4).

7.1 Quarterly data

Table 8, panel A provides descriptive statistics for several measures of aggregate

quarterly earnings news. First, we aggregate analysts’ earnings surprises for the quarter. To do

this, we take the difference between realized EPS and the median analyst forecast from First Call

at the beginning of the quarter for each firm, and then form an equally-weighted average across

firms. We do this for all firms with available analyst forecasts data on First Call (ASurpA) and

for those firms that issue earnings guidance in the quarter (ASurpF). Second, following Kothari

et al. (2003), we construct earnings-based measures by taking realized earnings for the quarter,

deflating by price, and either equal or value-weighting the results.20 This yields the variables

EP_agg (value-weighted by total assets) and EP_ew (equal-weighted). We then get measures of

earnings changes by taking seasonal differences in these variables, yielding ∆EP_agg and

∆EP_ew, respectively. Because the changes are highly autocorrelated, we also calculate time

series innovations in these variables (Innov_∆EP_agg and Innov_∆EP_ew, respectively) which 20 Following Kothari et al. we have also deflated by book value, with similar results.

22

are the residuals from time-series regressions that assume the changes follow an AR1 process.

For all measures, we restrict the set of firms to those with fiscal quarters that coincide with

calendar quarters. Finally, to gauge the extent to which the guidance and earnings news

variables are related to GDP growth, we also include the logarithmic change in GDP for the

quarter.21

Panel B of Table 8 reports descriptive statistics for two sets of aggregate guidance

variables, one for all firms and the other for S&P 500 firms, as well as for three market return

measures, the CRSP equal-weighted market index, the S&P 500 index, and the NASDAQ

Composite Index.22 To be consistent with our timing convention (see Figure 3) we cumulate

these returns over the three months beginning with the second month of each calendar quarter

and ending with the month after the end of the quarter, which usually contains the earnings

announcement (i.e., first quarter returns are cumulated over February, March, and April, etc.).

This aligns our quarterly return window with the way that earnings news for a given quarter is

typically disclosed and announced.

The guidance variables, ∆D, ∆N, and ∆U, are seasonal logarithmic changes in the

number of forecasts of quarterly earnings that represent downward, neutral and upward guidance,

respectively. Ratio is the ratio of downward to upward guidance for the quarter (as in Figure 1)

and ∆Ratio is the logarithmic seasonal change in this variable. As we might expect given the

overall increase in guidance over the sample period, the seasonal changes in guidance are all

21 This is the real, seasonally-adjusted change in gross domestic product, obtained from the St. Louis Fed, http://research.stlouisfed.org/fred2/categories/106. 22 We have also restricted attention to guidance for tech firms, defined in two ways based on SIC codes. First, following Fama and French (1997), we define tech firms as those in the chip and computer industries (for computers, SIC codes 3570-3579, 3680-3689, 3695, 7373 and for chips, SIC codes 3622, 3661-3679, 3810, and 3812). Second, following Francis and Schipper (1999) we also include pharmaceutical and telecom firms (SIC codes 283, 357, 360-368, 481, 737, 873). The results are generally similar to those reported, although not surprisingly the tech guidance aggregates tend to be related to the NASDAQ Composite returns rather than to returns on the other two indices. Returns on the CRSP-value weighted index were also used, but the results are not reported because they are so similar to those obtained using the S&P 500 index return.

23

positive, although the standard deviations are quite large. For example, the mean for ∆D is .338

(mean annual increase in downward guidance is 34%) with a standard deviation of .733 and first

order autocorrelation of .71. The high autocorrelation means that the changes do not represent

innovations in these variables, although more sophisticated time series analysis is precluded by

our relatively short time series (36 quarterly observations, from 1994 until 2002). As expected,

the mean Ratio is well above one at 3.27 with a standard deviation of 1.41. The mean ∆Ratio is

close to 0 (-.09) with a standard deviation of .56 and first order autocorrelation of .45, and so

behaves more like an innovation than the other variables. The statistics are similar for guidance

issued by S&P 500 firms, although these numbers tend to be less highly autocorrelated and the

mean Ratio is somewhat lower at 2.59, although it is still well above one.23

Panel C reports correlations among all of these variables for the 36 quarters of the sample

period, from Q1 1994 until Q4 2002. Panel D reports the correlations for the most recent 20

quarters, from Q1 1998 until Q4 2002. Given the increases we observe in both the pervasiveness

and representativenss of earnings guidance, we expect stronger results in the 1998-2002 period.

The correlations in Panel C first show that the various earnings news measures are

positively correlated, as we would expect. The two analyst surprise measures, ASurpA and

ASurpF are correlated at .79, suggesting that the earnings surprises for forecasters are a good

guide to those for the market as a whole. The correlation increases to .94 in Panel D, indicating

that the earnings surprises of forecasting firms have become essentially a sufficient statistic for

those of the market as a whole. The analyst surprise variables are also positively correlated with

both changes and innovations in aggregate earnings changes, with correlations that are around .5

and .6 in Panel C and that get up to .7 in Panel D. This is expected if these measures are all

capturing aggregate earnings news. With the exception of the ∆EP_agg variable and its 23 Assuming independence, the mean Ratios are both many standard errors above one.

24

innovation, these variables are not significantly correlated with the change in GDP for the

quarter.

Our main argument is related to the guidance variables. Panels C and D report

correlations using both overall guidance (all firms) and guidance issued by S&P 500 firms (in

parentheses). The correlations show that both downward guidance (∆D) and changes in the ratio

of downward to upward guidance (∆Ratio) are correlated with the various earnings news

measures, consistent with our predictions. Both variables are negatively related to the earnings

news variables, with correlations of -.3 to -.4 in Panel C and -.5 to -.6 in Panel D. The

correlations are higher in magnitude if we restrict attention to guidance issued by S&P 500 firms.

The correlations for downward guidance variable (∆D) tend to be higher than those for ∆Ratio.

Neither neutral nor upward guidance are correlated with the earnings news variables. This

suggests that trends in downward guidance are more informative than those in either upward or

neutral guidance, which is expected given other regularities in these data (i.e., quarterly

downward guidance is both more prevalent and more informative at the firm level). The

generally larger magnitudes of the correlations in Panel D is consistent with the earlier results on

trends in earnings guidance. Finally, because the bulk of downward guidance is issued by the

end of the fiscal period, this form of guidance is potentially the most timely source of news about

earnings for the quarter, since both the analyst surprise variables and the realized earnings

changes rely on earnings realizations which are not available until earnings are actually

announced.

Even though the earnings news and guidance variables are correlated in ways that we

might expect given our predictions, there is no evidence that any of these variables are correlated

25

with market returns. In fact, the only variable that is correlated with market returns is ∆GDP,

which is positively associated with quarterly returns.

The fact that market returns are not correlated with the earnings news measures is

surprising in light of the very strong positive relation that exists in firm-level data (Kothari,

2001). The result is, however, consistent with the recent findings of Kothari et al. (2003), who

also examine the relation between quarterly market returns and aggregate earnings news, and

find a significant but negative relation.24

There are several possible explanations for why quarterly guidance numbers are not be

correlated with quarterly market returns. First, as discussed in Section 2.2, recent evidence

shows that while cash flow news (of which earnings news is perhaps the most prominent

example) is the most important explanatory variable for firm-level returns, variation in expected

returns explains most of the variation in market-level returns, with cash flows news being

relatively unimportant. Second, as we argue in Section 2, earnings guidance may have

countervailing effects on market returns.25 Third, it may be that aggregating guidance data for

the entire quarter obscures informational effects. This would follow if guidance is particularly

informative in particular periods within the quarter and not informative in others. For example,

the evidence in Table 7 and Figure 4 suggests that the bulk of downward guidance occurs in the

last 2-3 weeks of the quarter and that the information content of earnings guidance is relatively

modest early in each quarter but increases as the quarter progresses. Fourth, numerous factors

affect quarterly market returns, making it difficult to isolate the effect of earnings guidance.

24 There are at least two important differences between our tests and those in Kothari et al. (2003). First, we cumulate quarterly returns over the three month period ending with the announcement month rather than cumulating returns over the three month fiscal period. Second, Kothari et al. have a much longer time series of data, since they use only quarterly Compustat data for their earnings measures and do not examine earnings guidance. 25 For example, good news about aggregate earnings increases expectations about future cash flows but might also increase discount rates by pushing up expectations about future inflation.

26

For all of these reasons, we are likely to obtain more powerful tests of the relation

between earnings guidance, earnings news, and market returns using monthly rather than

quarterly data, which is the evidence we present next.

7.2 Monthly data

As shown in Figure 4 and Table 7, there are clear intra-quarter patterns in the way that

earnings guidance is disclosed which interact with the nature of the news and its information

content. Table 9 reports mean and median frequencies of downward, neutral, and upward

guidance by calendar month. Panel A shows results for 1994-2002 while Panel B shows results

for 1998-2002. When we include observations at earnings announcement dates, the data show a

clear seasonal pattern, with higher levels of guidance in April, July, October, and January, when

most firms announce earnings (recall that the frequency of earnings guidance issued at earnings

announcement dates increases substantially over the sample period). Guidance is especially high

in October, perhaps because a lot of firms issue fourth quarter guidance when they announce

third quarter earnings. Because guidance issued in conjunction with earnings announcements

relates to future fiscal quarters rather than to the present one, we exclude these observations from

further analysis. After we exclude these observations, the peak months for guidance become

March, June, September, and December. Thus, consistent with the earlier evidence on disclosure

timing, the bulk of guidance occurs in what we define as the second month of each quarter (the

last month of the calendar quarter). Although levels of guidance remain high in the third month,

we will argue that most of the information in guidance regarding economy-wide trends is

conveyed by the end of this second month.26

The strong seasonal patterns in guidance complicate our efforts to measure innovations in

guidance. In particular, we estimate the guidance “surprises” in each month as first differences 26 In fact, high levels of downward guidance in month 2 predict high levels of downward guidance in month 3.

27

in the monthly guidance numbers. Results are, however, fairly similar if we take seasonal (third)

differences (i.e., by defining the guidance surprise in April as April guidance minus January

guidance, etc.).27 We then take these monthly guidance surprises, computed separately for

downward, neutral and upward guidance and for each month of the fiscal quarter, and correlate

them with contemporaneous monthly market returns. For purposes of comparison, we also

report correlations between the monthly market returns and the ∆GDP and ASurpA variables for

the quarter, as well as regressions of the monthly returns on various subsets of the variables. We

expect the strongest results in the second month of the quarter (last month of the calendar

quarter) and for downward guidance.

We report the results of these analyses in Table 10. Once again, we present results for

the entire 36 quarter sample period in Panel A and for the 1998-2002 period in Panel B. The

labels 1, 2, and 3 on the guidance variables denote guidance in months 1, 2, and 3 of our

quarterly periods, where quarters are again defined such that the first month in the quarter is the

second month of the fiscal period. For example, ∆D2 is the change in downward guidance in

month 2 of each quarter (i.e., in March, June, September or December).

The results in Table 10 support the idea that monthly guidance is informative with respect

to monthly market returns, but only for guidance issued in the second month of the quarter. We

find that month 1 changes in downward, neutral, and upward guidance are not significantly

related to returns. In month 2, however, changes in downward guidance are significantly

negatively correlated with returns: we observe a correlation of -.34 between the change in

downward guidance and equal weighted returns, and of -.40 between the change in S&P 500

downward guidance and S&P 500 returns. Both correlations are significant at 5%. As

expected, we obtain stronger results in Panel B, with correlations of -.49 and -.59, respectively 27 More sophisticated time series approaches are precluded by the fact that we have so few time series observations.

28

(significant at 5% or better). There is some evidence that changes in month 2 neutral guidance

are also negatively related to returns, implying that neutral guidance can be bad news for returns,

although these correlations are smaller than those for downward guidance. There is limited

evidence that guidance is informative with respect to month 3 returns. The change in month 3

upward guidance is positively related to equal-weighted returns (correlation of .38, increasing to

.51 in Panel B, both significant at 5%) and the change in month 3 downward S&P 500 guidance

is negatively related to S&P returns, but only over 1998-2002. Overall, the evidence supports

our view that guidance, especially downward guidance, is informative in month 2 of the quarter.

With respect to the other variables, we find that ∆GDP is positively correlated with both

equal-weighted and S&P 500 returns in month 2 (correlations ranging from .4 to .7, all

significant at at least the 5% level) while the ASurpA variable is negatively associated with both

return measures in month 3 (correlations ranging from -.3 to -.7).

The regression statistics reported at the bottom of the table generally support the

correlation results. When we regress monthly market returns on the three contemporaneous

guidance variables, we find modest evidence that these variables explain the month 2 market

returns in Panel A (R-squareds of around 10%; significance at 10% level or better) and strong

evidence in Panel B, with adjusted R-squares of 59% for equal-weighted returns and 47% for

S&P 500 index returns (significance at 5% or better). There is little consistent evidence that

guidance explains returns in the other two months. When we regress market returns for month 2

on the three guidance variables along with ∆GDP and ASurpA, the R-squares increase (with one

or other of these variables being statistically significant) and the guidance variables, as a group,

remain statistically significant (at 5% or better using an F-test).

29

Overall, this evidence indicates that guidance, especially downward guidance, is

informative with respect to market returns in the last month of each calendar quarter, when most

earnings warnings are issued. This is consistent with our earlier evidence on the timing and

information content of guidance, and with conventional wisdom that it is the relative extent of

downward guidance issued by the end of the fiscal period that “matters” with respect to market

returns.

7.3 Good and Bad News Quarters

In Section 2, we predict that earnings guidance affects market returns through its effect

on expected returns (discount rates) by affecting market uncertainty, and that these effects are

likely to be different for good and bad earnings news. That is, apart from its effect on expected

cash flows, earnings guidance may have “second moment” effects on market returns. To address

this idea, Table 11 reports evidence on how monthly market returns, return volatility, and

guidance compare for quarters in which overall (full quarter) market returns are good and bad

(defined as positive and negative).28 We again present results for both equal-weighted and S&P

500 market returns. To show the intra-quarter return patterns more clearly, Figure 5 plots

cumulative market returns from 45 trading days before the end of the calendar quarter through 22

trading days afterward.

From Table 11, it is evident that market return patterns in the good and bad news quarters

are systematically different, a result that holds for both equal-weighted and S&P 500 index

returns. While positive monthly returns in good news quarters tend to be spread evenly over the

three months, negative returns in the bad news quarters are concentrated in the first two months,

so that the announcement month has a close-to-zero return. This is consistent with the idea that

28 Following French et al. (1987), monthly return volatility is calculated from daily returns. Guidance again excludes that issued at the same time as earnings announcements.

30

market-level bad news is preempted more often than good news, a well-established result at the

firm level (e.g., Skinner, 1994; Soffer et al., 2000; Matsumoto, 2002). The result is reinforced by

looking at Figure 5, which shows that while cumulative returns in good news quarters increase

smoothly over the entire quarter, negative returns in bad news quarters tend to cluster in the 2-3

weeks before the end of month 2, when most earnings warnings are released.

Table 11 also documents that mean and median levels of downward guidance are

significantly higher in month 2 of the bad news quarters than they are in the good news quarters.

In month 2 the mean (median) level of downward guidance is 112 (120) for the bad news

quarters and 70 (62) for the good news quarters, differences that are highly significant.

Interestingly, and consistent with the evidence in Table 10, neutral guidance also tends to be

higher in these months, suggesting that neutral guidance issued at this time also conveys bad

news. The results are similar, and somewhat stronger, for the S&P guidance, for which month 1

downward guidance is also noticeably higher. The larger levels of negative guidance in the bad

news quarters persist into the third month of the quarter, but, by this point, are apparently

anticipated by the market and so do not greatly affect returns.29 Once again, this evidence is

consistent with our argument that most of the information contained in aggregate guidance has

been effectively conveyed by the end of the fiscal quarter.

Market volatility also tends to be higher in bad news quarters than in good news quarters.

For the equal-weighted market return, the volatility differences are significant in months 2 and 3

of the quarter, and largest in month 3. For the S&P 500 market return, volatility is significantly

higher in all months of the bad news quarters than in corresponding months of the good news

29 Consistent with this argument, we find that unusually high downward guidance (measured using third differences in the guidance data) in month 2 is highly predictive of unusually high guidance in month 3, with a correlation of .7 for the full sample period. This is not generally true of other types of guidance or of downward guidance in other months, although neutral guidance in month 2 is highly correlated with downward guidance in that month and in month 3. All of this suggests that there is little new information in month 3 downward guidance.

31

quarters. In addition, for all quarters, volatility increases monotonically in each month,

presumably reflecting the pattern of news arrival. The largest monthly increases are from month

2 to month 3 of the bad news quarters, which offers some support for the idea that bad news

creates the most market uncertainty.

Overall, the evidence suggests that patterns in aggregate guidance tend to be associated

with systematically different patterns of market returns in good and bad news quarters, consistent

with our view that guidance is informative with respect to market returns. We also observe

systematic differences in levels of market return volatility for good and bad news quarters.

7.4 Market “Bellwethers”

The evidence to this point examines whether there is a relation between earnings

guidance and market returns by looking at the relation between aggregate earnings guidance and

market-level returns. An alternative approach is to investigate whether earnings guidance

released by individual firms affects market returns by conducting an event study of earnings

guidance releases for certain stocks that are likely market “bellwethers,” perhaps because their

earnings are especially representative of those for the overall economy.

To conduct this analysis, we first sort all firms that issue quarterly earnings guidance in a

particular quarter by size, as measured by market capitalization, and retain the largest 20 firms in

each quarter. These are our candidate “bellwether” stocks. As a descriptive matter, the largest

20 forecasters account for, in a typical recent quarter, about 60-70% of the total market

capitalization of all firms issuing guidance, and for about 10-15% of the market capitalization of

all firms on Compustat, which reflects increasing skewness in the size distribution. To assess

whether guidance issued by these firms affects market returns, we calculate correlations between

the earnings news conveyed by their guidance and market returns in a 3-day announcement

32

window. We use two measures of earnings news: (1) the difference between the earnings

forecast and analysts’ consensus immediately before the forecast (available only when the

guidance is quantitative), and (2) the sign of the guidance, as previously defined, available for all

observations (downward, neutral, and upward guidance, coded as -1, 0, and 1 respectively). The

return is the three-day cumulative market return, centered on the day the guidance is released.

For purposes of comparison, we also report correlations between the earnings news variables and

the firm’s own raw return for the same three day window. Pearson correlations, with p-values in

parentheses, are reported below.

Firm’s own raw returns

Equal-weighted market return

S&P 500 index return

Quantitative Guidance (N = 571)

.20 (<.0001)

.02 (ns)

.05 (ns)

Qualitative Guidance (N = 712)

.40 (<.0001)

.09 (.011)

.10 (.009)

The earnings guidance variables are both positively related to contemporaneous own-firm

returns, and both correlations are highly statistically significant. The correlation is twice as high

(.4 versus .2) for the qualitative guidance measure than for the quantitative guidance measure,

which suggests relatively more measurement error in the latter variable.30 Perhaps as a result of

this, we find that market returns are uncorrelated with firm-level quantitative guidance.

However, we do find a reliable positive relation between qualitative earnings guidance and

market returns. Although these returns may at first blush seem small, we see them as

economically significant given: (1) the myriad other factors that affect market returns on any

given day, and (2) the fact that the explanatory power of this guidance is comparable to what

30 The Pearson correlation of .4 indicates that the qualitative surprise explains 16% of the three-day return while that for the quantitative measure explains only 4%. These numbers are comparable to those of short-window earnings announcement studies that use firm-level data (e.g., Brown et al., 1987) which typically report R-squareds of around 5-10%.

33

studies in accounting find when they conduct potentially more powerful tests by regressing firm

specific abnormal returns for short windows around earnings announcements on firm-specific

earnings surprise variables.

As an alternative, simpler test, we also compare the mean three-day market returns for the

sample of upward guidance announcements to that for the sample of downward guidance

announcements. Consistent with the tests above, we find that market returns are significantly

different for these two groups: the mean equal-weighted (S&P) market return is 5.73% (7.45%)

for the sample of upward guidance, compared to -3.10% (-5.44%) for the sample of downward

guidance. These differences are both significant at the 5% level, with t-statistics of 2.39 (2.27).

Overall, the results of our “bellwether” analyses support the idea that firm-level earnings

guidance is informative with respect to short window market returns.

8. Summary and Implications

We investigate whether aggregate earnings guidance is informative with respect to

market-level stock returns. First, we find that over the last decade, earnings guidance has

become increasingly pervasive and representative. Guidance is now issued by around 30% all

firms on Compustat. These firms represent over one-half of the total market capitalization of

Compustat and nearly one-half of Compustat total assets. In addition, firm characteristics such

as size, book-to-market and growth are, in the last several years, better predictors of forecasting

than measures of earnings performance such as ROA, losses, and earnings surprises, which were

associated with guidance in the mid to late 1990s and are more traditionally associated with

forecasting.

34

Second, we find that measures of aggregate guidance, and especially relative levels of

quarterly downward guidance, do a good job of capturing quarterly aggregate earnings news,

although they are not associated with quarterly market returns. When we examine monthly

returns, however, we find that aggregate earnings guidance does affect market returns in those

months of the quarter when the most informative earning guidance – downward guidance or

earnings “warnings” – is issued. We also find systematic differences in the intra-quarter pattern

of market returns between good news and bad news quarters, that these patterns appear to reflect

systematic differences in the timing and nature of aggregate earnings guidance, and that market

return volatility is higher in bad news quarters, perhaps reflecting higher levels of market

uncertainty. Finally, we examine market returns during short windows when the largest firms

(potential “bellwethers”) release earnings guidance, and find that firm-level earnings guidance is

associated with market returns in short windows around its release. All of this evidence supports

our main contention that guidance is informative with respect to market-level stock returns.

The fact that guidance issued by a relatively small number of firms can affect market

returns is related to other recent evidence of increasing levels of concentration in securities

markets. For example, DeAngelo, DeAngelo and Skinner (2004) report that over half of

Compustat earnings and dividends in 2000 are attributable to only 25 firms. We offer similar

evidence, for example that firms issuing guidance now represent over one half of the market

capitalization of Compustat, and that guidance for very large, influential firms such as GE,

Microsoft, and Intel can affect market returns by themselves. These results collectively suggest

35

that important economic aggregates are increasingly dominated by the fortunes of a relatively

small number of very large firms.31

Overall, our evidence suggests that measures of aggregate earnings guidance, and

especially the relative extent of downward earnings guidance, are informative with respect to

market returns. Thus, our evidence adds to the body of work that attempts to link shocks in

macroeconomic variables to stock returns (e.g., Chen et al., 1986; Boyd et al., 2005), although it

may be that guidance is informative more because of its “second moment” effects on market

uncertainty and volatility than because it represents a macro factor in its own right. In other

words, because guidance fundamentally accelerates the timing of the release of given news about

cash flow fundamentals, it is possible that it affects returns only temporarily. Moreover, if it is

the case that firm-level earnings news is largely idiosyncratic (Vuolteenaho, 1999) then these

second moment effects are perhaps the only way in which guidance affects market returns. On

the other hand, like the aggregate insider trading measures of Seyhun (1988, 1992), it may be

that by aggregating earnings news in a particular way, guidance captures important news about

economic fundamentals. In the end, while we provide evidence that aggregate guidance affects

market returns, sorting out the precise nature of this linkage is likely to be a fruitful avenue for

future research.

31 For example, some analysts anticipate that the special dividend recently announced by Microsoft could by itself influence fourth quarter GDP growth in the US. See “Nudging the Needle (The Macro Investor),” The Wall Street Journal, July 23, 2004.

36

Appendix. Classification of Analyst Forecast Data. This appendix describes: (1) how we categorize the First Call (FC) sample into various forecast types: point forecasts, range forecasts, minimum and maximum forecasts, and qualitative forecasts; and (2) how we use this information in combination with First Call forecast data to classify the forecasts into those that convey upward, neutral and downward guidance. 1. Classification of forecasts into point forecasts, range forecasts, minimum and maximum forecasts, and qualitative forecasts. This is based on the FC “CIG code” (these are the numbers and letters indicated below), the existence of numerical estimates (non-missing “est_1” and/or “est_2”), and the FC “comment” field. Quantitative forecasts (must contain at least one numerical estimate32) Point forecasts.

A. About $X. F. Comfortable with $X. Z. Break even.

Range forecasts (must give two numerical estimates)

B. Between $X and $Y. G. low end of $X and $Y. H. high end of $X and $Y.

UB/Max

1. May be below $X. 2. Not comfortable with $X (assume this means less than $X). 4. Significantly less than $X. 6. May not meet earnings of between $X and $Y. 8. Slightly less than $X. L. Less than $X. U. At or below $X. W. As high as $X. X. Expects loss.

LB/Min 3. Significantly greater than $X. 7. Slightly greater than $X. C. May exceed $X. E. At least $X. M. More than $X. V. As low as $X. 32 The only exceptions are cases where the guidance is that the result will be “break even” (classified as point estimate of $0), “expects loss” (classified as UB/Max of $0), or “expects profit” (classified as LB/Min of $0).

37

Y. Expects profit.

Qualitative guidance (no numerical estimates). Classified according to the news conveyed, good, bad, or none/neutral. Upward guidance. 5. Meets or exceeds expectations. P. Above expectations. Q./S. Revenue/sales above expectations. Downward guidance. D. Below expectations. J. May not meet expectations. K. May be below expectations. R/T. Revenues/sales below expectations. Neutral/none. O. OK with expectations. N. Sundry qualitative statements. 2. Classification of forecasts into downward, upward and neutral surprises. The classification is based on the sign of the management forecast surprise (“SURP”), defined as the difference between the new expectation and the prevailing median FC analysts’ earnings forecast. We define upward guidance as SURP > 0, downward guidance as SURP < 0, and neutral guidance as SURP = 0. The determination of whether a forecast represents upward, downward or neutral guidance depends on the type of forecast, as detailed below.

1. If Point, SURP = $X – AF.

2. If Range: o B. SURP = ($Midpoint of range) – AF. o G “low end”. SURP = $X – AF, where $X is low end. o H “high end”. SURP = $Y – AF, where $Y is high end.

3. If UB/Max

o 1./2./4./6./8./L./U./W. If AF < $X code as neutral, otherwise code as downward.

o X. If AF < 0 code as neutral, otherwise code as downward.

4. If LB/Min o 3./7./C./E./M./V. If AF > $X code as neutral, otherwise code as upward. o Y. If AF > 0 code as neutral, otherwise code as upward.

5. Qualitative. See classifications defined above.

38

Fig. 1: Quarterly ratios of downward to upward guidance, quarterly (solid) and annual (dashed) forecasts, Q1 1994 - Q4 2002

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Mar-94 Mar-95 Mar-96 Mar-97 Mar-98 Mar-99 Mar-00 Mar-01 Mar-02

Fig 2a: Fractions of Firms Issuing Earnings Guidance by Year: unweighted (lower solid line) and weighted by assets (dashed line) and by market capitalization (upper solid line)

0

0.1

0.2

0.3

0.4

0.5

0.6

1994 1995 1996 1997 1998 1999 2000 2001 2002

Fig 2b: Fraction of Firms Issuing Quarterly Earnings Guidance: unweighted (lower solid line) and weighted by assets (dashed line) and by market capitalization (upper solid line)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Mar-94 Nov-94 Jul-95 Mar-96 Dec-96 Aug-97 Apr-98 Dec-98 Aug-99 May-00 Jan-01 Sep-01 May-02 Jan-03

1

Figure 3: Summary of Disclosure Subperiods for a Given Fiscal Quarter

Subperiod 1 Subperiod 2 Subperiod 3 Subperiod 4

FQEt-1 EADt-1 FQEt – 50 FQEt – 25 FQEt EADt

About40 days

25 days 25 days About21 days

Fiscal Quarter

Figure 4: The intra-quarter timing of earnings disclosures for upward, downward and neutral quarterly earnings guidance, First Call data, 1994-2003

0

10

20

30

40

50

60

70

80

90

100

-50 -40 -30 -20 -10 0 10 20 30

Calendar days relative to fiscal quarter end

downwardneutralupward

Figure 5: Cumulative equal weighed market returns in good and bad news quarters, defined as those with positive and negative market returns respectively (the horizontal axis shows days relative to the end of the

fiscal quarter)

-0.1

-0.05

0

0.05

0.1

0.15

-45 -35 -25 -15 -5 5 15 25

Table 1: Details of Sample Construction: First Call Management Earnings Forecasts, 1994-2003 Annual Quarterly Total

All observations on the First Call CIG (“Company issued guidelines”) database, 1990-2003

19,363 28,876 48,239

Drop observations made before 1994 and/or for which currency is not USD and/or duplicate observations and/or that do not provide information about EPS33

(1,396) (1,303) (2,699)

Drop observations missing split adjustment factors

(8) (6) (14)

Drop observations with missing/unclassifiable cigcodes

(6) (4) (10)

Total sample of forward-looking disclosures on First Call

17,953 27,563 45,516

Drop observations not meeting analyst forecast criteria

(2,051) (7,839) (9,890)

Drop observations that do not have a CUSIP and do not have earnings announcement date on Compustat

(3,078) (1,123) (4,201)

Drop management guidance after earnings announcement dates

(50) (203) (253)

Final sample of forward-looking disclosures

12,774 18,398 31,172

33 There are about 1,200 observations which are forecasts of funds from operations, and a small number of other observations that are of “cash EPS”, EBITDA., etc. (less than 500 of these altogether).

44

Table 2: Descriptive Information on First Call Management Earnings Forecasts, 1994-2003 Panel A. Number of forecasts by year, along with number of forecasting firms. Year

Number of

Forecasts

AnnualForecasts

QuarterlyForecasts

Forecasting

Firms

1994 173 52 (31.1%) 121 (69.9%) 1461995 802 305 (38.0%) 497 (62.0%) 5581996 1,295 447 (34.5%) 848 (65.5%) 8671997 1,770 608 (34.4%) 1,162 (65.6%) 1,0981998 3,379 1,257 (37.2%) 2,122 (62.8%) 1,7181999 3,657 1,525 (41.7%) 2,132 (58.3%) 1,7142000 4,051 1,676 (41.4%) 2,375 (58.6%) 1,7972001 7,616 3,280 (43.1%) 4,336 (56.9%) 2,1712002 7,218 3,451 (47.8%) 3,767 (52.2%) 1,8262003 1,211 173 (14.3%) 1,038 (85.7%) 805 Total 31,172 12,774 (41.0%) 18,398 (59.0%) 12,700 Panel B. Number of forecasts per forecasting firm per year. Year

Number of forecasts

Total firms

issuing forecasts

Mean number of forecasts issued per

firm

Median number of forecasts issued per

firm

Maximum number of forecasts issued per

firm 1994 173 146 1.18 1 3 1995 802 558 1.44 1 6 1996 1,295 867 1.49 1 7 1997 1,770 1,098 1.61 1 9 1998 3,379 1,718 1.97 2 14 1999 3,657 1,714 2.13 2 11 2000 4,051 1,797 2.25 2 22 2001 7,616 2,171 3.51 3 21 2002 7,218 1,826 3.95 3 30 2003 1,211 805 1.50 1 5 Total 31,172 12,700

45

Panel C. Relative frequency of different forecast types Point

forecasts Range

forecastsMinimum forecasts

Maximum forecasts

Qualitative forecasts

Rowtotal

Annual 3,702

(29.0%) 6,893

(54.0%)687

(5.4%)259

(2.0%)1,233

(9.6%) 12,774

Quarterly 4,808

(26.1%) 9,123

(49.6%)976

(5.3%)1,223

(6.6%)2,258

(12.4%) 18,398

Total 8,510

(27.3%) 16,016

(51.4%)1,663

(5.3%)1,492

(4.8%)3,491

(11.2%) 31,172

Panel D. Proportion of forecasts released at earnings announcement dates Year

Number (proportion) of annual forecasts at EAD

Number (proportion) of quarterly forecasts at

EAD

1994 1 (1.9%) 2 (1.7%)1995 11 (3.6%) 17 (3.4%)1996 25 (5.6%) 47 (5.5%)1997 37 (6.1%) 61 (5.2%)1998 80 (6.4%) 187 (8.8%)1999 114 (7.5%) 253 (11.9%)2000 160 (9.5%) 383 (16.1%)2001 377 (11.5%) 1,479 (34.1%)2002 559 (16.2%) 1,590 (42.2%)2003 29 (16.8%) 360 (34.7%)

Total 1,393 (10.9%) 4,379 (23.8%)

46

Table 3: First Call Management Earnings Forecasts, 1994-2003, sorted into downward, neutral and upward guidance Panel A: Forecasts of Annual Earnings Year Downward

guidance Neutral

guidanceUpward

guidanceTotal

1994 30 9 13 52 (57.7%) (17.3%) (25.0%) 1995 125 79 101 305 (41.0%) (25.9%) (33.1%) 1996 190 121 136 447 (42.5%) (27.1%) (30.4%) 1997 219 205 184 608 (36.0%) (33.7%) (30.3%) 1998 491 474 292 1,257 (39.1%) (37.7%) (23.2%) 1999 552 549 424 1,525 (36.2%) (36.0%) (27.8%) 2000 690 446 540 1,676 (41.2%) (26.6%) (32.2%) 2001 1,456 581 1,243 3,280 (44.4%) (17.7%) (37.9%) 2002 1,239 560 1,652 3,451 (35.9%) (16.2%) (47.9%) 2003 72 29 72 173 (41.6%) (16.8%) (41.6%) Total 5,064 3,053 4,657 12,774 (39.6%) (23.9%) (36.4%)

47

Panel B: Forecasts of Quarterly Earnings Year Downward

guidance Neutral

guidanceUpward

guidanceTotal

1994 77 21 23 121

(63.6%) (17.4%) (19.0%) 1995 286 122 89 497

(57.6%) (24.6%) (17.8%) 1996 493 194 161 848

(58.1%) (22.9%) (19.0%) 1997 662 318 182 1,162

(57.0%) (27.4%) (15.6%) 1998 1,225 608 289 2,122

(57.7%) (28.7%) (13.6%) 1999 1,132 598 402 2,132

(53.1%) (28.1%) (18.8%) 2000 1,273 583 519 2,375

(53.6%) (24.6%) (21.8%) 2001 2,720 710 906 4,336

(62.7%) (16.4%) (20.9%) 2002 1,824 705 1,238 3,767

(48.4%) (18.7%) (32.9%) 2003 579 183 276 1,038

(55.8%) (17.6%) (26.6%)

Total 10,271 4,042 4,085 18,398 (55.8%) (22.0%) (22.2%)

48

Table 4: Firm-Level Stock Price Reaction to First Call Management Earnings Forecasts by forecast period and nature of guidance, 1994-2002

Obs. Percent of

category Mean Median Percent

negative Annual: Downward 4,920 40% -6.22% -3.28% 68% Neutral 2,987 24% -0.31% 0.00% 50% Upward 4,557 37% 2.53% 1.71% 38% 12,464 42% -1.60% -0.42% 53% Quarterly: Downward 9,555 56% -10.05% -7.14% 77% Neutral 3,819 22% -1.09% -0.47% 53% Upward 3,767 22% 4.20% 2.75% 34% 17,141 58% -4.92% -2.57% 63% Total 29,605 -3.53% -1.51% 58%

Returns are three-day market-adjusted returns centered on the management forecast date, where the market return is the CRSP equal-weighted index return.

49

Table 5: Analyses of firms that do and do not provide forecasts of earnings (“earnings guidance”) using First Call data, 1994-2002

TA-weighted proportion of forecasting firms is the sum otal assets in millions (Compustat annual item 6) for all forecastin firms divided by the sum of total assets for the full sample (forecasters and non-forecasters) for the year indicated. MV-weighted proportion of forecasting firms is the sum of the market capitalizations for all forecasting firms (Compustat item 199 times item 25) divided by the sum of the market capitalizations for the full sample for the year indicated.

of t g

Number of forecasting firms relative to total number of firms:

Logit Results:

Number of forecasting

firms

Total

number of firms

Proportion

of forecasting

firms

TA-

weighted proportion

of forecasting

firms

MV-

weighted proportion

of forecasting

firms

LN(Total Assets)

Book-to-Market

ROA

Sales

Growth

Loss

Dummy

Pseudo-

R squared

1994 128 5,934 .022 .040 .053 .241** -.146 2.09* -.265 -.022 .011995 504 6,377 .079 .134 .166 .274** -.374** 4.14** .010 .506** .051996 756 6,872 .110 .168 .219 .221** -.520** 1.13** -.215** .300* .031997 930 6,861 .136 .196 .263 .221** -.446** 1.16** -.104 .350** .041998 1,437 6,509 .221 .431 .443 .274** -.224** 0.97** -.110* .232* .071999 1,400 6,372 .220 .332 .421 .287** -.158** 1.19** -.168** .208* .082000 1,458 6,208 .235 .305 .465 .303** -.094* 0.44 -.097* -.013 .092001 1,723 5,641 .305 .456 .565 .333** -.622** -0.14 -.255** .074 .142002 1,453 4,957 .293 .469 .555 .363** -.517** .75** -.442** .104 .17

The dependent variable for the annual logit analysis is equal to 1 if a firm made any forecast within the year, and 0 otherwise. LN(Total Assets) is the natural logarithm of total assets (Compustat item 6). Book-to-Market is the total common equity in millions of dollars (Compustat item 60) divided by the market value of equity at fiscal year end. ROA is the return on assets for the fiscal year, Compustat items 170 (pretax income) plus 15 (interest expense) divided by average total assets for the year. Sales Growth is the percent change in sales from last years’ sales to this year, Compustat item 12 (Salest – Salest-1)/(Salest-1). Loss Dummy is equal to 1 if net income (Compustat item 172) for the fiscal year is less than zero. The logit results sample has been truncated at the top and bottom 1% of Book-to-Market ratio, ROA, and Sales Growth. **, * Significant at the 1% and 5% levels, two-tailed tests, respectively.

50

Table 6: Analyses of firms that do and do not provide quarterly forecasts of earnings (“earnings guidance”) using First Call data, 1994-2002

LN(Total Assets)

Book-to-Market

Asurp

ROA

Loss

Dummy

Pseudo-

R squared 1994,1 .122 -2.43 .494 11.6 1.65* .007 1994,2 .144 -.920 -3.57** -1.37 -2.11 .019 1994,3 .025 .304 -3.37* 4.94 -1.36 .004 1994,4 .239 -1.78 -1.46 -.356 .560 .005 1995,1 .258* -.993 2.04 13.3 1.31* .012 1995,2 .095 -1.22* -1.44* 8.07 -.040 .011 1995,3 .020 -.604 -2.45** 6.02 .159 .018 1995,4 -.034 -.440 -2.44** 9.32* .133 .019 1996,1 -.082 -1.13* -3.42** -2.88 -.920 .029 1996,2 .080 -.546 -2.08** .673 .360 .018 1996,3 .106 -1.03** -2.19** 2.59 -.058 .019 1996,4 .003 -.403 -1.63** .068 -.245 .007 1997,1 -.072 -.347 -2.15** 4.67 .218 .010 1997,2 -.029 .240 -2.30** 1.78 -.151 .018 1997,3 .063 -.628 -2.44** -.081 -.223 .021 1997,4 .155** -.976* -1.69** 1.30 -.509 .020 1998,1 .022 -.557 -2.21** 2.73 -.171 .016 1998,2 .070 -.738** -3.47** -.791 -.445 .044 1998,3 .066 -.385 -2.18** 1.75 -.171 .028 1998,4 .097* -.147 -1.59** .910 -.332 .019 1999,1 .085* -.566** -1.72** 3.82 .171 .019 1999,2 .190** -.149 -2.09** 4.78 .236 .035 1999,3 .212** -.604** -2.23** 2.63 .222 .045 1999,4 .042 -.302 -1.08** 1.38 .211 .009 2000,1 .110* -.208 -1.05** 1.54 -.180 .009 2000,2 .158** -.562** -2.01** 1.40 -.269 .029 2000,3 .234** -.132 -1.80** .417 -.457* .045 2000,4 .102** -.281* -0.601* 2.75 -.150 .013 2001,1 .136** -.342** -1.71** -3.80* -.557** .041 2001,2 .214** -.565** -1.29** 2.42 .438* .051 2001,3 .213** -.489** -.619* -1.69 -.044 .040 2001,4 .088 -.484** -.224 3.53 .360 .011 2002,1 .202** -.498** -.719 5.60* .500** .035 2002,2 .203** -.549** -.355 .902 -.091 .033 2002,3 .194** -.439** -1.05** .679 -.148 .037 2002,4 .078 -.550** -.320 1.30 .551* .012 The dependent variable is equal to 1 if a firm made at least one quarterly forecast within the quarter (defined as the period beginning with the previous quarter’s earnings announcement date, EADt-1 in Figure 1, and ending two days before the current quarter’s earnings announcement date, EADt), and 0 otherwise. The full sample consists of all firms with a fiscal year ending in March, June, September, or December and has been truncated at the top and bottom 1% of Book-to-Market ratio, ROA, and Sales Growth. LN(Total Assets) is the natural logarithm of total assets for the end of the previous quarter (Compustat quarterly item 44) . Book-to-Market is the total common equity in millions of dollars for the end of the previous quarter (Compustat item 59) divided by the market value of equity for the end of the previous quarter (Compustat items 14 times 61). ROA is the return on assets for the quarter, Compustat items 23 (pretax income) plus 22 (interest expense) divided by average total assets for the quarter. Asurp is defined as the difference between realized earnings and analysts’ forecasts

51

at the beginning of the period and is winsorized at the 1% and 99% levels. Loss Dummy is equal to 1 if net income (Compustat item 69) for the calendar quarter is less than zero. **, * Significant at the 1% and 5% levels, two-tailed tests, respectively.

52

Table 7 The Timing, Nature and Information Content of Earnings Guidance for First Call Quarterly Management Earnings Forecasts, 1994-2002

Panel A: The relative frequency of forecasts by subperiod; all forecast observations

Downward

Guidance Neutral

Guidance Upward

Guidance Total

Subperiod

one 2,438 [53%] (24%)

1,005 [22%] (25%)

1,140 [25%] (28%)

4,583 (25%)

Subperiod

two 1,018 [52%] (10%)

580 [29%] (14%)

383 [19%] (9%)

1,981 (11%)

Subperiod

three 3,343 [63%] (33%)

1,078 [20%] (27%)

857 [16%] (21%)

5,278 (29%)

Subperiod

four 3,472 [53%] (33%)

1,379 [21%] (34%)

1,705 [26%] (42%)

6,556 (36%)

Total 10,271

[56%] 4,042 [22%]

4,085 [22%]

18,398

Percentages of row totals are reported in square brackets. Percentages of column totals are reported in parentheses.

53

Panel B: The relative frequency of forecasts by subperiod; Excluding forecasts made in conjunction with previous earnings announcements

Downward

Guidance Neutral Guidance

Upward Guidance

Total

Subperiod

one 314

[47%] (4%)

220 [33%] (7%)

129 [20%] (4%)

663 (5%)

Subperiod

two 781

[50%] (10%)

501 [32%] (16%)

282 [18%] (10%)

1,564 (11%)

Subperiod

three 3,330 [63%] (42%)

1,073 [20%] (35%)

846 [16%] (30%)

5,249 (38%)

Subperiod

four 3,419 [54%] (44%)

1,306 [21%] (42%)

1,618 [26%] (56%)

6,343 (46%)

Total 7,844

[57%] 3,100 [22%]

2,875 [21%]

13,819

Percentages of row totals are reported in square brackets. Percentages of column totals are reported in parentheses.

54

Panel C: The Stock price response to forecasts as a function of news and the subperiod in which it is reported, excluding forecasts made in conjunction with previous earnings announcements.

Downward

Guidance Neutral Guidance

Upward Guidance

Total

Subperiod

one 290

-9.38% (-5.08%)

.71

212 -0.74% (-.65%)

.56

124 2.26% (.88%)

.42

626 -4.15%

(-1.61%) .60

Subperiod

two 737

-10.32% (-7.59%)

.77

474 -2.02% (-.82%)

.57

260 2.82%

(1.54%) .40

1,471 -5.33%

(-2.75%) .64

Subperiod

three 3,159

-11.88% (-9.13%)

.83

1,028 -2.46%

(-1.00%) .57

798 4.28%

(3.03%) .33

4,985 -7.35%

(-4.52%) .70

Subperiod

four 3,221

-12.65% (-9.82%)

.82

1,295 -1.03% (-.60%)

.54

1,558 5.03%

(3.37%) .32

6,074 -5.64%

(-3.05%) .63

Table reports the number of observations in each cell, the mean market-adjusted return, the median market-adjusted return (in parentheses), and the fraction of negative market-adjusted returns. Market adjusted-returns are cumulated over the three trading days centered on the forecast announcement date and are calculated using the return on the CRSP equal-weighted index to measure the market return.

55

Table 8: Descriptive information, including correlations, for quarterly aggregate data on quarterly earnings guidance, earnings surprises, and market returns, Q1 1994 to Q4 2002 (36 quarters). Panel A. Mean, standard deviation, and first order correlation of earnings surprise data based on the 36 quarterly cross-sections. The table also reports the average number of firm observations used to compute the variable in each cross section. ASurpF

(Forecasting firms)

ASurpA (All

firms)

EP_agg EP_ew ∆EP_agg ∆EP_ew Innov._∆EP_agg

Innov._ ∆EP_ew

∆GDP

Mean

-.0617 -.0421 .0125 -.0001 .0006 .0020 -.0002 .0000 .0079Std. Devn. .0266 .0156 .0058 .0085 .0036 .0048 .0026 .0038 .0053

Mean # obs. per cross section

951

3,951

4,073

4,073

4,073

4,073

na

na

na

First order autocorrelation

.31 .48 na na .69 .72 .01 -.05 .17

Panel B. Mean, standard deviation, and first order correlation of earnings guidance, S&P 500 earnings guidance and market return data based on the 36 quarterly cross-sections. Guidance data for all firms: S&P 500 Guidance data: Market return measures: ∆Down ∆Neut. ∆Up Ratio ∆Ratio ∆Down ∆Neut. ∆Up Ratio ∆Ratio Equal-

weighted return

S&P 500

return

NASDAQ Composite

Return

Mean .338 .356 .426 3.27 -.087 .322 .324 .413 2.59 -.064 .061 .020 .026Std. Devn. .733 .732 .569 1.41 .563 .763 .680 .773 1.83 1.02 .096 .074 .142

First order

autocorrelation .71 .73 .56 .36 .45 .62 -.17 .25 .15 .28 -.22 .15 .07

56

Panel C. Correlation among the variables computed using the 36 quarterly cross-sections, Q1 1994 to Q4 2002. AsurpA ∆EP_agg ∆EP_ew Inv.

∆EP_agg Inv. ∆EP_ew

∆GDP ∆D ∆N ∆U ∆Ratio EWReturn

S&P Return

NDQ Return

ASurpF .79*** .28* .58*** .30* .45** -.11 -.44** -.24 -.15 -.42** -.12 -.16 -.15 (-.55***) (-.26) (.17) (-.55***)

ASurpA .65*** .53*** .59*** .50*** .19 -.35* -.10 -.12 -.33* -.08 -.02 -.05

(-.58***) (-.15) (.07) (-.48***) ∆EP_agg .46*** .71*** .25 .41** .07

(-.33*) .14 (.12)

.01 (.04)

-.10 (-.27)

.02 .18 .18

∆EP_ew .46*** .79*** .15 -.38** -.14

(-.49***) (-.13) -.14 (.08)

-.36** (-.43**)

-.01 -.15 .05

Inv. ∆EP_agg .53*** .41** -.33*

(-.42**) -.08 (.04)

-.17 (-.21)

-.25 (-.17)

-.04 .02 .07

Inv. ∆EP_ew .13 -.30*

(-.32*) -.16 (-.06)

-.15 (-.00)

-.24 (-.24)

.09 -.09 .07

∆GDP -.14

(-.29) -.06 (.07)

-.12 (-.22)

-.06 (-.03)

.28* .32* .44***

∆Down 6**.7 * .65***

(.24) (.13) .64*** (.66***)

.01 (-.03)

.18 (.03)

-.02 (-.12)

∆Neutral 1**.7 * .28

(.17) (.07) .15 (.29)

.30* (.40*)

.16 (.34*)

∆Up -.16

(-.66***) .07 (.08)

.21 (.22)

.09 (.14)

∆Ratio -.05

(-.01) .02 (-.08)

-.13 (-.16)

EW Retur 8** 9*** n .6 * .7 S&P Retur 2*** n .8

57

***Significantly different from zero at the 1% level. **Significantly different from zero at the 5% level. *Significantly different from zero at the 10% level.

58

Panel D. Correlation among the variables computed using the 20 quarterly cross-sections, Q1 1998 to Q4 2002. AsurpA ∆EP_agg ∆EP_ew Inv.

∆EP_agg Inv. ∆EP_ew

∆GDP ∆D ∆N ∆U ∆Ratio EWReturn

S&P Return

NDQ Return

ASurpF .94*** .41* .68*** .48** .47** -.01 -.63***(-.72***)

-.40* (-.30)

-.09 (.28)

-.50** (-.71***)

-.20 -.17 -.24

ASurpA .61*** .75*** .63*** .53** .14 -.65***

(-.72***) -.38* (-.32)

-.06 (.27)

-.53** (-.72***)

-.24 -.15 -.17

∆EP_agg .53** .71*** .32 .49** -.26

(-.33) .07 (.02)

.19 (.01)

-.14 (-.28)

-.09 .11 .15

∆EP_ew .52** .80*** .20 -.64*** -.40*

(-.66***) (-.23) -.31 (.29)

-.42* (-.60***)

-.05 -.19 .04

Inv. ∆EP_agg .62*** .43* -.48** -.25

(-.04) (-.46**) .12 (.21)

-.46** (-.40*)

-.14 .04 .03

Inv. ∆EP_ew .21 -.54** -.28

(-.39**) (-.01) .14 (.23)

-.42* (-.37)

.01 -.12 .03

∆GDP -.26

(-.25) -.11 (.31)

.11 (.19)

-.27 (-.26)

.43* .49** .65***

∆Down .83*** -.12

(.43*) (-.54**) .90*** (.95***)

-.16 (-.18)

.02 (.03)

-.16 (-.15)

∆Neutral .16

(-.23) .64*** (.39)

-.19 (-.03)

.15 (.17)

-.04 (.10)

∆Up 53*-. * -.01

(-.77***) (.02) .12 (-.03)

.13 (.16)

∆Ratio -.16

(-.02) -.03 (.02)

-.23 (-.18)

EW Return 8** 4*** .6 * .7 S&P Return 3*** .8

59

***Significantly different from zero at the 1% level. **Significantly different from zero at the 5% level. *Significantly different from zero at the 10% level. ASurpF = The mean earnings surprise for forecasting firms, defined as realized EPS minus the median analyst earnings forecast as of the beginning of the quarter. ASurpA = The mean earnings surprise for all firms with available data, defined as realized EPS minus the median analyst earnings forecast as of the beginning of the quarter. EP_agg = EPS deflated by stock price, aggregated on a value-weighted basis (using total assets) across all firms with available data in the quarter. EP_ew = EPS deflated by stock price, aggregated on a equal-weighted basis across all firms with available data in the quarter. ∆EP_agg = The seasonal change in EP_agg. ∆EP_ew = The seasonal change in EP_ew. Inv. ∆EP_agg = The time series innovation in ∆EP_agg. Inv. ∆EP_agg = The time series innovation in ∆EP_ew. ∆GDP = the logarithmic change in real, seasonally-adjusted GDP for the quarter. ∆Down = the fourth difference in the number of forecasts of quarterly earnings that convey downward guidance about earnings for a given calendar quarter, defined as forecasts made in the second and third months of the calendar quarter and in the first month of the next calendar quarter (so that we include preannouncements that occur after the fiscal period end). We take fourth differences to account for the strong seasonality in these numbers. We exclude forecasts made concurrent with earnings announcements. Changes are logarithmic changes. ∆Neut. = the fourth difference in the number of forecasts of quarterly earnings that convey neutral guidance about earnings for a given calendar quarter, defined as forecasts made in the second and third months of the calendar quarter and in the first month of the next calendar quarter (so that we include preannouncements that occur after the fiscal period end). We take fourth differences to account for the strong seasonality in these numbers. We exclude forecasts made concurrent with earnings announcements. Changes are logarithmic changes. ∆Up = the fourth difference in the number of forecasts of quarterly earnings that convey upward guidance about earnings for a given calendar quarter, defined as forecasts made in the second and third months of the calendar quarter and in the first month of the next calendar quarter (so that we include preannouncements that occur after the fiscal period end). We take fourth differences to account for the strong seasonality in these numbers. We exclude forecasts made concurrent with earnings announcements. Changes are logarithmic changes. Ratio is the ratio of the number of forecasts of quarterly earnings that convey downward guidance about earnings for a given calendar quarter to the corresponding number of forecasts that provide upward guidance. ∆Ratio is the logarithmic seasonal change in Ratio (the natural logarithm of Ratio in quarter t to Ratio in quarter t-4). Equal-weighted return = the CRSP equal-weighted market return for the quarter, defined as the second and third months of the calendar quarter and the first month of the next calendar quarter. S&P 500 return = the S&P 500 index return for the quarter, defined as the second and third months of the calendar quarter and the first month of the next calendar quarter. NASDAQ Composite return = the return on the NASDAQ Composite index for the quarter, defined as the second and third months of the calendar quarter and the first month of the next calendar quarter.

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Table 9: Means (Medians) of number of downward, neutral and upward quarterly forecasts that provide earnings guidance, issued by First Call firms with calendar quarter fiscal period ends, 1994-2002, by calendar month Panel A: Full sample period, 1994-2002 (n = 9) Including observations at EADs. Excluding observations at EADs. Means: Medians: Means: Medians: Down. Neut. Up. Down. Neut. Up. Down. Neut. Up. Down. Neut. Up. Q1:

Feb. 43 19 19 27 21 11 19 14 8 19 15 10 Mar. 72 26 23 56 22 9 70 25 22 54 21 9 Apr. 97 38 54 61 34 27 62 26 34 53 28 25

Q2: May 22 13 12 17 13 5 13 10 7 15 12 4 Jun. 87 28 21 84 25 12 87 27 20 84 25 12 Jul. 110 39 43 89 41 32 73 26 29 73 29 27

Q3: Aug. 22 11 6 16 13 3 13 9 4 13 9 3 Sep. 101 33 21 120 38 19 101 33 21 120 38 19 Oct. 152 55 59 144 70 45 100 33 36 118 35 33

Q4: Nov. 23 18 12 27 20 7 14 14 8 16 13 6 Dec. 71 27 19 63 29 19 71 27 19 63 29 19 Jan. 99 41 49 78 51 30 67 30 35 66 36 27

Panel B: 1998-2002 (n = 5) Including observations at EADs. Excluding observations at EADs. Means: Medians: Means: Medians: Down. Neut. Up. Down. Neut. Up. Down. Neut. Up. Down. Neut. Up.

Q1:

Feb. 66 28 29 59 29 24 27 19 12 27 19 12 Mar. 109 41 36 77 35 39 107 39 35 76 32 33 Apr. 155 58 85 108 58 65 92 38 49 86 31 54

Q2: May 35 19 20 22 20 8 19 14 10 18 12 7 Jun. 127 38 31 104 37 25 127 38 31 104 37 25 Jul. 167 57 67 108 53 40 103 35 41 97 35 32

Q3: Aug. 35 15 10 32 16 6 19 12 5 21 12 6 Sep. 142 47 33 141 44 26 142 47 33 141 44 26 Oct. 238 82 93 186 86 98 146 46 52 127 40 53

Q4: Nov. 35 25 20 38 22 20 19 18 12 22 17 14 Dec. 101 38 31 82 36 28 101 38 31 82 36 28 Jan. 163 64 88 176 66 103 100 44 59 87 44 61

61

The table reports on the mean and median number of quarterly earnings forecasts that convey downward, neutral and upward guidance about quarterly earnings in each calendar month. The table reports these statistics for samples that include and exclude forecasts made concurrent with earnings announcements (“at EADs”).

62

Table 10: Relation between market returns, earnings guidance, earnings surprises and GDP growth Panel A. Full sample period: Monthly data from 36 quarters, 1994-2002 Guidance for all firms; Equal-

weighted market returns: Guidance for S&P 500 firms; S&P 500 Index returns:

Return:

Month 1 Return: Month 2

Return: Month 3

Return: Month 1

Return: Month 2

Return: Month 3

Univariate Correlations: ∆D1 -.03 NA NA .06 NA NA ∆D2 NA -.34** NA NA -.40** NA ∆D3 NA NA -.01 NA NA -.24 ∆N1 -.01 NA NA -.25 NA NA ∆N2 NA -.08 NA NA -.36** NA ∆N3 NA NA -.02 NA NA .11 ∆U1 .07 NA NA .09 NA NA ∆U2 NA -.32* NA NA -.16 NA ∆U3 NA NA .38** NA NA -.01 ∆GDP .01 .48** -.01 .08 .42** -.01 AsurpA .31* .07 -.56** .25 -.03 -.32* Regression statistics: Adj. R2: Guidance (F-Stat.)

-.08 (.11)

.08 (2.07)

.09 (2.17)

.00 (1.04)

.11 (2.49*)

.06 (1.71)

Adj. R2: Total (F-Stat.) [F-Stat. Guidance]

.14 (2.19*) [2.29*]

.17 (2.40*) [.73]

.23 (3.12**) [.25]

.03 (1.19) [1.19]

.29 (3.81***) [3.30**]

.00 (1.00) (0.43)

t-stat.: ∆GDP

-.54 2.21** .65 -.20 1.28 -.15

t-stat.: AsurpA

3.24** -.65 -2.52** 1.71* -2.70** -.83

63

1Guidance Regressions: For quarterly return, the guidance regression includes all nine guidance variables; for return in month 1, guidance includes ∆D1, ∆N1, ∆U1; for month 2, guidance includes ∆D2, ∆N2, ∆U2; for month 3, guidance includes ∆D3, ∆N3, ∆U3. The Total regressions include the same guidance variables along with ∆GDP and AsurpA. We report the F-statistic for the total regression as well as that from an F-test of the null hypothesis that all of the guidance variables have zero coefficients. We also report the t-statistics on ∆GDP and AsurpA from the same regressions. The quarterly return is the return on the CRSP equal-weighted market portfolio for each quarter, defined as the second and third months of each calendar quarter and the first month of the next calendar quarter. Return for month 1 is the return on the CRSP equal-weighted market portfolio for the first month of this quarterly period (the second month of each calendar quarter), return for month 2 is the return on the CRSP equal-weighted market portfolio for the second month of this quarterly period (the third month of each calendar quarter), return for month 3 is the return on the CRSP equal-weighted market portfolio for the third month of this quarterly period (the first month of the next calendar quarter). The guidance variables are defined as follows: ∆D1 is the change in downward guidance from the previous month for the first month in the quarter (second month in the calendar quarter), ∆D2 is the change in downward guidance from the previous month for the second month in the quarter (third month in the calendar quarter), ∆D3 is the change in downward guidance from the previous month for the third month in the quarter (first month of the next calendar quarter). ∆N1, ∆N2, ∆N3 are defined analogously for neutral guidance. ∆U1, ∆U2, ∆U3 are defined analogously for upward guidance. The numbers reported in the S&P 500 columns are defined above except that the guidance variables only include guidance issued by S&P 500 firms while the returns are those on the S&P 500 index. ***Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.

64

Panel B. Sample subperiod: Monthly data from 20 quarters, 1998-2002 Guidance for all firms; Equal-

weighted market returns: Guidance for S&P 500 firms; S&P 500 Index returns:

Return:

Month 1 Return: Month 2

Return: Month 3

Return: Month 1

Return: Month 2

Return: Month 3

Univariate correlations: ∆D1 -.24 NA NA -.08 NA NA ∆D2 NA -.49** NA NA -.59*** NA ∆D3 NA NA -.05 NA NA -.48** ∆N1 -.22 NA NA -.41* NA NA ∆N2 NA -.49** NA NA -.54** NA ∆N3 NA NA -.09 NA NA .07 ∆U1 -.07 NA NA .05 NA NA ∆U2 NA -.49** NA NA -.22 NA ∆U3 NA NA .51** NA NA -.03 ∆GDP -.07 .70*** .09 .10 .54** -.01 AsurpA .25 .02 -.67*** .27 -.04 -.52** Regression statistics: Adj. R2: Guidance (F-Stat.)

-.08 (.51)

.59 (10.5***)

.17 (2.41)

.03 (1.18)

.47 (6.82**)

.27 (3.51**)

Adj. R2: Total (F-Stat.) [F-Stat. Guidance]

.19 (1.87) [2.54*]

.67 (8.58***) [4.93**]

.41 (3.64**) [.81]

.08 (1.31) [1.65]

.62 (7.12***) [6.75***]

.20 (1.93) (1.02)

t-stat.: ∆GDP

-.80 1.62 1.51 -.08 .08 .28

t-stat.: AsurpA

2.73** -1.21 -1.65 1.63 -2.84** -1.05

1Guidance Regressions: For quarterly return, the guidance regression includes all nine guidance variables; for return in month 1, guidance includes ∆D1, ∆N1, ∆U1; for month 2, guidance includes ∆D2, ∆N2, ∆U2; for month 3, guidance includes ∆D3, ∆N3, ∆U3. The Total regressions include the same guidance variables along with ∆GDP and AsurpA. We report the F-statistic for the total regression as well as that

65

from an F-test of the null hypothesis that all of the guidance variables have zero coefficients. We also report the t-statistics on ∆GDP and AsurpA from the same regressions. The quarterly return is the return on the CRSP equal-weighted market portfolio for each quarter, defined as the second and third months of each calendar quarter and the first month of the next calendar quarter. Return for month 1 is the return on the CRSP equal-weighted market portfolio for the first month of this quarterly period (the second month of each calendar quarter), return for month 2 is the return on the CRSP equal-weighted market portfolio for the second month of this quarterly period (the third month of each calendar quarter), return for month 3 is the return on the CRSP equal-weighted market portfolio for the third month of this quarterly period (the first month of the next calendar quarter). The guidance variables are defined as follows: ∆D1 is the change in downward guidance from the previous month for the first month in the quarter (second month in the calendar quarter), ∆D2 is the change in downward guidance from the previous month for the second month in the quarter (third month in the calendar quarter), ∆D3 is the change in downward guidance from the previous month for the third month in the quarter (first month of the next calendar quarter). ∆N1, ∆N2, ∆N3 are defined analogously for neutral guidance. ∆U1, ∆U2, ∆U3 are defined analogously for upward guidance. The numbers reported in the S&P 500 columns are defined above except that the guidance variables only include guidance issued by S&P 500 firms while the returns are those on the S&P 500 index. ***Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.

66

Table 11: Comparison of monthly markets, market return volatility, and earnings guidance for good and bad news quarters, Equal-weighted and S&P 500 index market returns Equal-weighted return results: S&P 500 Index returns:

Good News Quarters

Bad News

Quarters

t-test for difference

Good News Quarters

Bad News

Quarters

t-test for difference

Obs. 25 11 21 15 Quarterly Return

.109* -.052* .070* -.045*

Monthly returns: Return, month 1

.036* -.021 2.64* .024* -.027* 3.35*

Return, month 2

.032* -.031* 4.37* .021* -.016 2.37*

Return, month 3

.038* .004 1.62 .024* .001 1.58

Monthly return volatility: Volatility, month 1

.028 .032 -.69 .037 .055 -3.11*

Volatility, month 2

.030 .040 -1.75* .042 .056 -1.96*

Volatility, month 3

.035 .051 -2.04* .044 .066 -2.65*

Mean (median) monthly earnings guidance: Downward, month 1

14 (14)

17 (18)

-.1.07 -.98

2 (1)

4 (4)

-2.66* -2.36*

Downward, month 2

70 (62)

112 (120)

-2.10* -1.68*

10 (7)

23 (19)

-3.07* -2.68*

Downward, month 3

68 (66)

102 (98)

-2.02* -1.71*

6 (4)

14 (13)

-3.07* -2.83*

Neutral, month 1

11 (12)

13 (14)

-.50 -.85

3 (3)

5 (3)

-1.59 -1.18

Neutral, month 2

25 (25)

37 (38)

-2.00* -1.70*

6 (5)

12 (12)

-3.25* -3.00*

Neutral, month 3

27 (28)

35 (38)

-1.20 -1.35

6 (5)

7 (6)

-.33 -.32

Upward, month 1

6 (4)

7 (6)

-.35 -.53

1 (0)

2 (1)

-1.63 -1.16

Upward, month 2

20 (16)

23 (19)

-.58 -.50

4 (2)

7 (6)

-1.77* -1.91*

Upward, month 3

33 (29)

37 (33)

-.40 -.45

5 (4)

8 (9)

-2.28* -1.97*

67

Good news quarters are those for which the cumulative market return is non-negative while bad news quarter are those for which the cumulative market return is negative. The quarterly return is the average cumulative market return across all quarters in a given category. Return, month 1 (2, 3), is the average cumulative market return in the first (second, third) month of the quarter across all quarters in a given category. Volatility, month 1 (2, 3), is the average volatility of the monthly return in the first (second, third) month of the quarter across all quarters in a given category. All of these return-based numbers are computed from daily market returns during the relevant period. Downward, month 1 (2, 3) is the mean (median) number of forecasts conveying downward guidance in the first (second, third) month of the quarter across all quarters in a given category. Neutral, month 1 (2, 3), and Upward, month 1 (2, 3) are defined analogously. Quarters are calendar quarters from Q1, 1994 through Q4, 2002. Month 1 of the quarter is the second month of the calendar quarter (Feb., May, Aug., Nov.), month 2 of the quarter is the last month of the calendar quarter (Mar., Jun., Sep., Dec.), month 3 of the quarter is the first month of the next calendar quarter (Apr., Jul., Oct., Jan.).

68

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